From 7c128bbdac0da1767c239174e91af6f327845372 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Thu, 19 Oct 2023 13:56:17 +0800
Subject: Add fp8 for sd unet

---
 extensions-builtin/Lora/network.py       |  2 +-
 extensions-builtin/Lora/network_full.py  |  4 ++--
 extensions-builtin/Lora/network_glora.py | 10 +++++-----
 extensions-builtin/Lora/network_hada.py  | 12 ++++++------
 extensions-builtin/Lora/network_ia3.py   |  2 +-
 extensions-builtin/Lora/network_lokr.py  | 18 +++++++++---------
 extensions-builtin/Lora/network_lora.py  |  6 +++---
 extensions-builtin/Lora/network_norm.py  |  4 ++--
 extensions-builtin/Lora/networks.py      |  6 +++---
 9 files changed, 32 insertions(+), 32 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py
index 6021fd8d..a62e5eff 100644
--- a/extensions-builtin/Lora/network.py
+++ b/extensions-builtin/Lora/network.py
@@ -137,7 +137,7 @@ class NetworkModule:
     def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
         if self.bias is not None:
             updown = updown.reshape(self.bias.shape)
-            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
+            updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
             updown = updown.reshape(output_shape)
 
         if len(output_shape) == 4:
diff --git a/extensions-builtin/Lora/network_full.py b/extensions-builtin/Lora/network_full.py
index bf6930e9..f221c95f 100644
--- a/extensions-builtin/Lora/network_full.py
+++ b/extensions-builtin/Lora/network_full.py
@@ -18,9 +18,9 @@ class NetworkModuleFull(network.NetworkModule):
 
     def calc_updown(self, orig_weight):
         output_shape = self.weight.shape
-        updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        updown = self.weight.to(orig_weight.device)
         if self.ex_bias is not None:
-            ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
+            ex_bias = self.ex_bias.to(orig_weight.device)
         else:
             ex_bias = None
 
diff --git a/extensions-builtin/Lora/network_glora.py b/extensions-builtin/Lora/network_glora.py
index 492d4870..efe5c681 100644
--- a/extensions-builtin/Lora/network_glora.py
+++ b/extensions-builtin/Lora/network_glora.py
@@ -22,12 +22,12 @@ class NetworkModuleGLora(network.NetworkModule):
         self.w2b = weights.w["b2.weight"]
 
     def calc_updown(self, orig_weight):
-        w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
-        w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
-        w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
-        w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+        w1a = self.w1a.to(orig_weight.device)
+        w1b = self.w1b.to(orig_weight.device)
+        w2a = self.w2a.to(orig_weight.device)
+        w2b = self.w2b.to(orig_weight.device)
 
         output_shape = [w1a.size(0), w1b.size(1)]
-        updown = ((w2b @ w1b) + ((orig_weight @ w2a) @ w1a))
+        updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a))
 
         return self.finalize_updown(updown, orig_weight, output_shape)
diff --git a/extensions-builtin/Lora/network_hada.py b/extensions-builtin/Lora/network_hada.py
index 5fcb0695..d95a0fd1 100644
--- a/extensions-builtin/Lora/network_hada.py
+++ b/extensions-builtin/Lora/network_hada.py
@@ -27,16 +27,16 @@ class NetworkModuleHada(network.NetworkModule):
         self.t2 = weights.w.get("hada_t2")
 
     def calc_updown(self, orig_weight):
-        w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
-        w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
-        w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
-        w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+        w1a = self.w1a.to(orig_weight.device)
+        w1b = self.w1b.to(orig_weight.device)
+        w2a = self.w2a.to(orig_weight.device)
+        w2b = self.w2b.to(orig_weight.device)
 
         output_shape = [w1a.size(0), w1b.size(1)]
 
         if self.t1 is not None:
             output_shape = [w1a.size(1), w1b.size(1)]
-            t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
+            t1 = self.t1.to(orig_weight.device)
             updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
             output_shape += t1.shape[2:]
         else:
@@ -45,7 +45,7 @@ class NetworkModuleHada(network.NetworkModule):
             updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
 
         if self.t2 is not None:
-            t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
+            t2 = self.t2.to(orig_weight.device)
             updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
         else:
             updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
diff --git a/extensions-builtin/Lora/network_ia3.py b/extensions-builtin/Lora/network_ia3.py
index 7edc4249..96faeaf3 100644
--- a/extensions-builtin/Lora/network_ia3.py
+++ b/extensions-builtin/Lora/network_ia3.py
@@ -17,7 +17,7 @@ class NetworkModuleIa3(network.NetworkModule):
         self.on_input = weights.w["on_input"].item()
 
     def calc_updown(self, orig_weight):
-        w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
+        w = self.w.to(orig_weight.device)
 
         output_shape = [w.size(0), orig_weight.size(1)]
         if self.on_input:
diff --git a/extensions-builtin/Lora/network_lokr.py b/extensions-builtin/Lora/network_lokr.py
index 340acdab..fcdaeafd 100644
--- a/extensions-builtin/Lora/network_lokr.py
+++ b/extensions-builtin/Lora/network_lokr.py
@@ -37,22 +37,22 @@ class NetworkModuleLokr(network.NetworkModule):
 
     def calc_updown(self, orig_weight):
         if self.w1 is not None:
-            w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
+            w1 = self.w1.to(orig_weight.device)
         else:
-            w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
-            w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w1a = self.w1a.to(orig_weight.device)
+            w1b = self.w1b.to(orig_weight.device)
             w1 = w1a @ w1b
 
         if self.w2 is not None:
-            w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2 = self.w2.to(orig_weight.device)
         elif self.t2 is None:
-            w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
-            w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+            w2a = self.w2a.to(orig_weight.device)
+            w2b = self.w2b.to(orig_weight.device)
             w2 = w2a @ w2b
         else:
-            t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
-            w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
-            w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
+            t2 = self.t2.to(orig_weight.device)
+            w2a = self.w2a.to(orig_weight.device)
+            w2b = self.w2b.to(orig_weight.device)
             w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
 
         output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
diff --git a/extensions-builtin/Lora/network_lora.py b/extensions-builtin/Lora/network_lora.py
index 26c0a72c..4cc40295 100644
--- a/extensions-builtin/Lora/network_lora.py
+++ b/extensions-builtin/Lora/network_lora.py
@@ -61,13 +61,13 @@ class NetworkModuleLora(network.NetworkModule):
         return module
 
     def calc_updown(self, orig_weight):
-        up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
-        down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        up = self.up_model.weight.to(orig_weight.device)
+        down = self.down_model.weight.to(orig_weight.device)
 
         output_shape = [up.size(0), down.size(1)]
         if self.mid_model is not None:
             # cp-decomposition
-            mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
+            mid = self.mid_model.weight.to(orig_weight.device)
             updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
             output_shape += mid.shape[2:]
         else:
diff --git a/extensions-builtin/Lora/network_norm.py b/extensions-builtin/Lora/network_norm.py
index ce450158..d25afcbb 100644
--- a/extensions-builtin/Lora/network_norm.py
+++ b/extensions-builtin/Lora/network_norm.py
@@ -18,10 +18,10 @@ class NetworkModuleNorm(network.NetworkModule):
 
     def calc_updown(self, orig_weight):
         output_shape = self.w_norm.shape
-        updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+        updown = self.w_norm.to(orig_weight.device)
 
         if self.b_norm is not None:
-            ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
+            ex_bias = self.b_norm.to(orig_weight.device)
         else:
             ex_bias = None
 
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 60d8dec4..8ea4ea60 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -381,12 +381,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
                             # inpainting model. zero pad updown to make channel[1]  4 to 9
                             updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
 
-                        self.weight += updown
+                        self.weight.copy_((self.weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
                         if ex_bias is not None and hasattr(self, 'bias'):
                             if self.bias is None:
-                                self.bias = torch.nn.Parameter(ex_bias)
+                                self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
                             else:
-                                self.bias += ex_bias
+                                self.bias.copy_((self.bias.to(dtype=ex_bias.dtype) + ex_bias).to(dtype=self.bias.dtype))
                 except RuntimeError as e:
                     logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
                     extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
-- 
cgit v1.2.3


From 370a77f8e78e65a8a1339289d684cb43df142f70 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Tue, 21 Nov 2023 19:59:34 +0800
Subject: Option for using fp16 weight when apply lora

---
 extensions-builtin/Lora/networks.py | 16 ++++++++++++----
 modules/initialize_util.py          |  1 +
 modules/sd_models.py                | 14 +++++++++++---
 modules/shared_options.py           |  1 +
 4 files changed, 25 insertions(+), 7 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 0170dbfb..d22ed843 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -388,18 +388,26 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
             if module is not None and hasattr(self, 'weight'):
                 try:
                     with torch.no_grad():
-                        updown, ex_bias = module.calc_updown(self.weight)
+                        if getattr(self, 'fp16_weight', None) is None:
+                            weight = self.weight
+                            bias = self.bias
+                        else:
+                            weight = self.fp16_weight.clone().to(self.weight.device)
+                            bias = getattr(self, 'fp16_bias', None)
+                            if bias is not None:
+                                bias = bias.clone().to(self.bias.device)
+                        updown, ex_bias = module.calc_updown(weight)
 
-                        if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
+                        if len(weight.shape) == 4 and weight.shape[1] == 9:
                             # inpainting model. zero pad updown to make channel[1]  4 to 9
                             updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
 
-                        self.weight.copy_((self.weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
+                        self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
                         if ex_bias is not None and hasattr(self, 'bias'):
                             if self.bias is None:
                                 self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
                             else:
-                                self.bias.copy_((self.bias.to(dtype=ex_bias.dtype) + ex_bias).to(dtype=self.bias.dtype))
+                                self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
                 except RuntimeError as e:
                     logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
                     extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
diff --git a/modules/initialize_util.py b/modules/initialize_util.py
index 1b11ead6..7fb1d8d5 100644
--- a/modules/initialize_util.py
+++ b/modules/initialize_util.py
@@ -178,6 +178,7 @@ def configure_opts_onchange():
     shared.opts.onchange("gradio_theme", shared.reload_gradio_theme)
     shared.opts.onchange("cross_attention_optimization", wrap_queued_call(lambda: sd_hijack.model_hijack.redo_hijack(shared.sd_model)), call=False)
     shared.opts.onchange("fp8_storage", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
+    shared.opts.onchange("cache_fp16_weight", wrap_queued_call(lambda: sd_models.reload_model_weights()), call=False)
     startup_timer.record("opts onchange")
 
 
diff --git a/modules/sd_models.py b/modules/sd_models.py
index eb491434..0a7777f1 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -413,14 +413,22 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
         devices.dtype_unet = torch.float16
         timer.record("apply half()")
 
+    for module in model.modules():
+        if hasattr(module, 'fp16_weight'):
+            del module.fp16_weight
+        if hasattr(module, 'fp16_bias'):
+            del module.fp16_bias
+
     if check_fp8(model):
         devices.fp8 = True
         first_stage = model.first_stage_model
         model.first_stage_model = None
         for module in model.modules():
-            if isinstance(module, torch.nn.Conv2d):
-                module.to(torch.float8_e4m3fn)
-            elif isinstance(module, torch.nn.Linear):
+            if isinstance(module, (torch.nn.Conv2d, torch.nn.Linear)):
+                if shared.opts.cache_fp16_weight:
+                    module.fp16_weight = module.weight.clone().half()
+                    if module.bias is not None:
+                        module.fp16_bias = module.bias.clone().half()
                 module.to(torch.float8_e4m3fn)
         model.first_stage_model = first_stage
         timer.record("apply fp8")
diff --git a/modules/shared_options.py b/modules/shared_options.py
index d27f35e9..eaa9f135 100644
--- a/modules/shared_options.py
+++ b/modules/shared_options.py
@@ -201,6 +201,7 @@ options_templates.update(options_section(('optimizations', "Optimizations"), {
     "persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("do not recalculate conds from prompts if prompts have not changed since previous calculation"),
     "batch_cond_uncond": OptionInfo(True, "Batch cond/uncond").info("do both conditional and unconditional denoising in one batch; uses a bit more VRAM during sampling, but improves speed; previously this was controlled by --always-batch-cond-uncond comandline argument"),
     "fp8_storage": OptionInfo("Disable", "FP8 weight", gr.Dropdown, {"choices": ["Disable", "Enable for SDXL", "Enable"]}).info("Use FP8 to store Linear/Conv layers' weight. Require pytorch>=2.1.0."),
+    "cache_fp16_weight": OptionInfo(False, "Cache FP16 weight for LoRA").info("Cache fp16 weight when enabling FP8, will increase the quality of LoRA. Use more system ram."),
 }))
 
 options_templates.update(options_section(('compatibility', "Compatibility"), {
-- 
cgit v1.2.3


From 22e23dbf29b0bbc807daa57318c31145f8dd0774 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 4 Dec 2023 15:56:03 +0300
Subject: add hypertile infotext

---
 .../hypertile/scripts/hypertile_script.py          | 53 +++++++++++++++++-----
 1 file changed, 42 insertions(+), 11 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/hypertile/scripts/hypertile_script.py b/extensions-builtin/hypertile/scripts/hypertile_script.py
index d3ab6091..395d584b 100644
--- a/extensions-builtin/hypertile/scripts/hypertile_script.py
+++ b/extensions-builtin/hypertile/scripts/hypertile_script.py
@@ -17,11 +17,42 @@ class ScriptHypertile(scripts.Script):
 
         configure_hypertile(p.width, p.height, enable_unet=shared.opts.hypertile_enable_unet)
 
+        self.add_infotext(p)
+
     def before_hr(self, p, *args):
+
+        enable = shared.opts.hypertile_enable_unet_secondpass or shared.opts.hypertile_enable_unet
+
         # exclusive hypertile seed for the second pass
-        if not shared.opts.hypertile_enable_unet:
+        if enable:
             hypertile.set_hypertile_seed(p.all_seeds[0])
-        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=shared.opts.hypertile_enable_unet_secondpass)
+
+        configure_hypertile(p.hr_upscale_to_x, p.hr_upscale_to_y, enable_unet=enable)
+
+        if enable and not shared.opts.hypertile_enable_unet:
+            p.extra_generation_params["Hypertile U-Net second pass"] = True
+
+            self.add_infotext(p, add_unet_params=True)
+
+    def add_infotext(self, p, add_unet_params=False):
+        def option(name):
+            value = getattr(shared.opts, name)
+            default_value = shared.opts.get_default(name)
+            return None if value == default_value else value
+
+        if shared.opts.hypertile_enable_unet:
+            p.extra_generation_params["Hypertile U-Net"] = True
+
+        if shared.opts.hypertile_enable_unet or add_unet_params:
+            p.extra_generation_params["Hypertile U-Net max depth"] = option('hypertile_max_depth_unet')
+            p.extra_generation_params["Hypertile U-Net max tile size"] = option('hypertile_max_tile_unet')
+            p.extra_generation_params["Hypertile U-Net swap size"] = option('hypertile_swap_size_unet')
+
+        if shared.opts.hypertile_enable_vae:
+            p.extra_generation_params["Hypertile VAE"] = True
+            p.extra_generation_params["Hypertile VAE max depth"] = option('hypertile_max_depth_vae')
+            p.extra_generation_params["Hypertile VAE max tile size"] = option('hypertile_max_tile_vae')
+            p.extra_generation_params["Hypertile VAE swap size"] = option('hypertile_swap_size_vae')
 
 
 def configure_hypertile(width, height, enable_unet=True):
@@ -57,16 +88,16 @@ def on_ui_settings():
     benefit.
     """),
 
-        "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net").info("noticeable change in details of the generated picture; if enabled, overrides the setting below"),
-        "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass"),
-        "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
-        "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
+        "hypertile_enable_unet": shared.OptionInfo(False, "Enable Hypertile U-Net", infotext="Hypertile U-Net").info("enables hypertile for all modes, including hires fix second pass; noticeable change in details of the generated picture"),
+        "hypertile_enable_unet_secondpass": shared.OptionInfo(False, "Enable Hypertile U-Net for hires fix second pass", infotext="Hypertile U-Net second pass").info("enables hypertile just for hires fix second pass - regardless of whether the above setting is enabled"),
+        "hypertile_max_depth_unet": shared.OptionInfo(3, "Hypertile U-Net max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile U-Net max depth").info("larger = more neural network layers affected; minor effect on performance"),
+        "hypertile_max_tile_unet": shared.OptionInfo(256, "Hypertile U-Net max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile U-Net max tile size").info("larger = worse performance"),
+        "hypertile_swap_size_unet": shared.OptionInfo(3, "Hypertile U-Net swap size", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile U-Net swap size"),
 
-        "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE").info("minimal change in the generated picture"),
-        "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}),
-        "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
+        "hypertile_enable_vae": shared.OptionInfo(False, "Enable Hypertile VAE", infotext="Hypertile VAE").info("minimal change in the generated picture"),
+        "hypertile_max_depth_vae": shared.OptionInfo(3, "Hypertile VAE max depth", gr.Slider, {"minimum": 0, "maximum": 3, "step": 1}, infotext="Hypertile VAE max depth"),
+        "hypertile_max_tile_vae": shared.OptionInfo(128, "Hypertile VAE max tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, infotext="Hypertile VAE max tile size"),
+        "hypertile_swap_size_vae": shared.OptionInfo(3, "Hypertile VAE swap size ", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, infotext="Hypertile VAE swap size"),
     }
 
     for name, opt in options.items():
-- 
cgit v1.2.3


From 16bdcce92d5b482d50cdc32a8f308040d320b6c9 Mon Sep 17 00:00:00 2001
From: Rene Kroon <kroon.r.w@gmail.com>
Date: Fri, 8 Dec 2023 21:19:29 +0100
Subject: #13354: solve lora loading issue

---
 extensions-builtin/Lora/networks.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 7f814706..629bf853 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -159,7 +159,8 @@ def load_network(name, network_on_disk):
     bundle_embeddings = {}
 
     for key_network, weight in sd.items():
-        key_network_without_network_parts, network_part = key_network.split(".", 1)
+        key_network_without_network_parts, _, network_part = key_network.partition(".")
+
         if key_network_without_network_parts == "bundle_emb":
             emb_name, vec_name = network_part.split(".", 1)
             emb_dict = bundle_embeddings.get(emb_name, {})
-- 
cgit v1.2.3


From 1a79a5049bdfef285235e83f37b201e39dd54f81 Mon Sep 17 00:00:00 2001
From: kaalibro <konstantin.adamovich@gmail.com>
Date: Sat, 9 Dec 2023 22:35:31 +0600
Subject: Assign id for "extra_options". Replace numeric field with slider in
 Settings.

---
 .../extra-options-section/scripts/extra_options_section.py           | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
index a903df62..b9867fe6 100644
--- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py
+++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -23,11 +23,12 @@ class ExtraOptionsSection(scripts.Script):
         self.setting_names = []
         self.infotext_fields = []
         extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
+        elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img")
 
         mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
 
         with gr.Blocks() as interface:
-            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
+            with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):
 
                 row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
 
@@ -70,7 +71,7 @@ This page allows you to add some settings to the main interface of txt2img and i
 """),
     "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
     "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
-    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Number, {"precision": 0}).needs_reload_ui(),
+    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 6}).needs_reload_ui(),
     "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui()
 }))
 
-- 
cgit v1.2.3


From 6b8143a84e112f029ee1868b6ab98b1d2c773ead Mon Sep 17 00:00:00 2001
From: kaalibro <konstantin.adamovich@gmail.com>
Date: Sun, 10 Dec 2023 15:35:06 +0600
Subject: Number of columns slider: max count set to 20, add description info

---
 .../extra-options-section/scripts/extra_options_section.py              | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
index b9867fe6..ac2c3de4 100644
--- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py
+++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -71,7 +71,7 @@ This page allows you to add some settings to the main interface of txt2img and i
 """),
     "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
     "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
-    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 6}).needs_reload_ui(),
+    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 20}).info("displayed amount will depend on the actual browser window width").needs_reload_ui(),
     "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui()
 }))
 
-- 
cgit v1.2.3


From 735c9e8059384d4f640e5582413c30871f83eac5 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Thu, 14 Dec 2023 01:38:32 +0800
Subject: Fix network_oft

---
 extensions-builtin/Lora/network_oft.py | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index 05c37811..44465f7a 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -53,12 +53,17 @@ class NetworkModuleOFT(network.NetworkModule):
             self.constraint = None
             self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
 
-    def calc_updown_kb(self, orig_weight, multiplier):
+    def calc_updown(self, orig_weight):
+        I = torch.eye(self.block_size, device=self.oft_blocks.device)
         oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-        oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
+        if self.is_kohya:
+            block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
+            norm_Q = torch.norm(block_Q.flatten())
+            new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
+            block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
+            oft_blocks = torch.matmul(I + block_Q, (I - block_Q).float().inverse())
 
         R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-        R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
 
         # This errors out for MultiheadAttention, might need to be handled up-stream
         merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
@@ -70,15 +75,10 @@ class NetworkModuleOFT(network.NetworkModule):
         merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
 
         updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
+        print(torch.norm(updown))
         output_shape = orig_weight.shape
         return self.finalize_updown(updown, orig_weight, output_shape)
 
-    def calc_updown(self, orig_weight):
-        # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it
-        multiplier = self.multiplier()
-        return self.calc_updown_kb(orig_weight, multiplier)
-
-    # override to remove the multiplier/scale factor; it's already multiplied in get_weight
     def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
         if self.bias is not None:
             updown = updown.reshape(self.bias.shape)
@@ -94,4 +94,5 @@ class NetworkModuleOFT(network.NetworkModule):
         if ex_bias is not None:
             ex_bias = ex_bias * self.multiplier()
 
-        return updown, ex_bias
+        # Ignore calc_scale, which is not used in OFT.
+        return updown * self.multiplier(), ex_bias
-- 
cgit v1.2.3


From 265bc26c21264d63956e8f30f1ce31dec917fc76 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Thu, 14 Dec 2023 01:43:24 +0800
Subject: Use self.scale instead of custom finalize

---
 extensions-builtin/Lora/network_oft.py | 20 ++------------------
 1 file changed, 2 insertions(+), 18 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index 44465f7a..e3ae61a2 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -21,6 +21,8 @@ class NetworkModuleOFT(network.NetworkModule):
         self.lin_module = None
         self.org_module: list[torch.Module] = [self.sd_module]
 
+        self.scale = 1.0
+
         # kohya-ss
         if "oft_blocks" in weights.w.keys():
             self.is_kohya = True
@@ -78,21 +80,3 @@ class NetworkModuleOFT(network.NetworkModule):
         print(torch.norm(updown))
         output_shape = orig_weight.shape
         return self.finalize_updown(updown, orig_weight, output_shape)
-
-    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
-        if self.bias is not None:
-            updown = updown.reshape(self.bias.shape)
-            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
-            updown = updown.reshape(output_shape)
-
-        if len(output_shape) == 4:
-            updown = updown.reshape(output_shape)
-
-        if orig_weight.size().numel() == updown.size().numel():
-            updown = updown.reshape(orig_weight.shape)
-
-        if ex_bias is not None:
-            ex_bias = ex_bias * self.multiplier()
-
-        # Ignore calc_scale, which is not used in OFT.
-        return updown * self.multiplier(), ex_bias
-- 
cgit v1.2.3


From 8fc67f3851babd4575d3312b931d5e7c2b0c78c6 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Thu, 14 Dec 2023 01:44:49 +0800
Subject: remove debug print

---
 extensions-builtin/Lora/network_oft.py | 1 -
 1 file changed, 1 deletion(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index e3ae61a2..ff4eb59b 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -77,6 +77,5 @@ class NetworkModuleOFT(network.NetworkModule):
         merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
 
         updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
-        print(torch.norm(updown))
         output_shape = orig_weight.shape
         return self.finalize_updown(updown, orig_weight, output_shape)
-- 
cgit v1.2.3


From 3772a82a70769fe1aac884a75bf5a3313fb83328 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Thu, 14 Dec 2023 01:47:13 +0800
Subject: better naming and correct order for device.

---
 extensions-builtin/Lora/network_oft.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index ff4eb59b..fa647020 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -56,14 +56,15 @@ class NetworkModuleOFT(network.NetworkModule):
             self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
 
     def calc_updown(self, orig_weight):
-        I = torch.eye(self.block_size, device=self.oft_blocks.device)
         oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        eye = torch.eye(self.block_size, device=self.oft_blocks.device)
+
         if self.is_kohya:
             block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
             norm_Q = torch.norm(block_Q.flatten())
             new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
             block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
-            oft_blocks = torch.matmul(I + block_Q, (I - block_Q).float().inverse())
+            oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
 
         R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
 
-- 
cgit v1.2.3


From 93eae69895c34361a71dbed17348bcfd132fbc6a Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Sat, 16 Dec 2023 11:00:42 +0300
Subject: move soft inpainting to a built-in extension

---
 .../soft-inpainting/scripts/soft_inpainting.py     | 747 +++++++++++++++++++++
 scripts/soft_inpainting.py                         | 747 ---------------------
 2 files changed, 747 insertions(+), 747 deletions(-)
 create mode 100644 extensions-builtin/soft-inpainting/scripts/soft_inpainting.py
 delete mode 100644 scripts/soft_inpainting.py

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py
new file mode 100644
index 00000000..d9024344
--- /dev/null
+++ b/extensions-builtin/soft-inpainting/scripts/soft_inpainting.py
@@ -0,0 +1,747 @@
+import numpy as np
+import gradio as gr
+import math
+from modules.ui_components import InputAccordion
+import modules.scripts as scripts
+
+
+class SoftInpaintingSettings:
+    def __init__(self,
+                 mask_blend_power,
+                 mask_blend_scale,
+                 inpaint_detail_preservation,
+                 composite_mask_influence,
+                 composite_difference_threshold,
+                 composite_difference_contrast):
+        self.mask_blend_power = mask_blend_power
+        self.mask_blend_scale = mask_blend_scale
+        self.inpaint_detail_preservation = inpaint_detail_preservation
+        self.composite_mask_influence = composite_mask_influence
+        self.composite_difference_threshold = composite_difference_threshold
+        self.composite_difference_contrast = composite_difference_contrast
+
+    def add_generation_params(self, dest):
+        dest[enabled_gen_param_label] = True
+        dest[gen_param_labels.mask_blend_power] = self.mask_blend_power
+        dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale
+        dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation
+        dest[gen_param_labels.composite_mask_influence] = self.composite_mask_influence
+        dest[gen_param_labels.composite_difference_threshold] = self.composite_difference_threshold
+        dest[gen_param_labels.composite_difference_contrast] = self.composite_difference_contrast
+
+
+# ------------------- Methods -------------------
+
+def processing_uses_inpainting(p):
+    # TODO: Figure out a better way to determine if inpainting is being used by p
+    if getattr(p, "image_mask", None) is not None:
+        return True
+
+    if getattr(p, "mask", None) is not None:
+        return True
+
+    if getattr(p, "nmask", None) is not None:
+        return True
+
+    return False
+
+
+def latent_blend(settings, a, b, t):
+    """
+    Interpolates two latent image representations according to the parameter t,
+    where the interpolated vectors' magnitudes are also interpolated separately.
+    The "detail_preservation" factor biases the magnitude interpolation towards
+    the larger of the two magnitudes.
+    """
+    import torch
+
+    # NOTE: We use inplace operations wherever possible.
+
+    # [4][w][h] to [1][4][w][h]
+    t2 = t.unsqueeze(0)
+    # [4][w][h] to [1][1][w][h] - the [4] seem redundant.
+    t3 = t[0].unsqueeze(0).unsqueeze(0)
+
+    one_minus_t2 = 1 - t2
+    one_minus_t3 = 1 - t3
+
+    # Linearly interpolate the image vectors.
+    a_scaled = a * one_minus_t2
+    b_scaled = b * t2
+    image_interp = a_scaled
+    image_interp.add_(b_scaled)
+    result_type = image_interp.dtype
+    del a_scaled, b_scaled, t2, one_minus_t2
+
+    # Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
+    # 64-bit operations are used here to allow large exponents.
+    current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
+
+    # Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
+    a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
+        settings.inpaint_detail_preservation) * one_minus_t3
+    b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
+        settings.inpaint_detail_preservation) * t3
+    desired_magnitude = a_magnitude
+    desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
+    del a_magnitude, b_magnitude, t3, one_minus_t3
+
+    # Change the linearly interpolated image vectors' magnitudes to the value we want.
+    # This is the last 64-bit operation.
+    image_interp_scaling_factor = desired_magnitude
+    image_interp_scaling_factor.div_(current_magnitude)
+    image_interp_scaling_factor = image_interp_scaling_factor.to(result_type)
+    image_interp_scaled = image_interp
+    image_interp_scaled.mul_(image_interp_scaling_factor)
+    del current_magnitude
+    del desired_magnitude
+    del image_interp
+    del image_interp_scaling_factor
+    del result_type
+
+    return image_interp_scaled
+
+
+def get_modified_nmask(settings, nmask, sigma):
+    """
+    Converts a negative mask representing the transparency of the original latent vectors being overlayed
+    to a mask that is scaled according to the denoising strength for this step.
+
+    Where:
+        0 = fully opaque, infinite density, fully masked
+        1 = fully transparent, zero density, fully unmasked
+
+    We bring this transparency to a power, as this allows one to simulate N number of blending operations
+    where N can be any positive real value. Using this one can control the balance of influence between
+    the denoiser and the original latents according to the sigma value.
+
+    NOTE: "mask" is not used
+    """
+    import torch
+    return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale)
+
+
+def apply_adaptive_masks(
+        settings: SoftInpaintingSettings,
+        nmask,
+        latent_orig,
+        latent_processed,
+        overlay_images,
+        width, height,
+        paste_to):
+    import torch
+    import modules.processing as proc
+    import modules.images as images
+    from PIL import Image, ImageOps, ImageFilter
+
+    # TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control.
+    latent_mask = nmask[0].float()
+    # convert the original mask into a form we use to scale distances for thresholding
+    mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2))
+    mask_scalar = (0.5 * (1 - settings.composite_mask_influence)
+                   + mask_scalar * settings.composite_mask_influence)
+    mask_scalar = mask_scalar / (1.00001 - mask_scalar)
+    mask_scalar = mask_scalar.cpu().numpy()
+
+    latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
+
+    kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
+
+    masks_for_overlay = []
+
+    for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)):
+        converted_mask = distance_map.float().cpu().numpy()
+        converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
+                                                   percentile_min=0.9, percentile_max=1, min_width=1)
+        converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
+                                                   percentile_min=0.25, percentile_max=0.75, min_width=1)
+
+        # The distance at which opacity of original decreases to 50%
+        half_weighted_distance = settings.composite_difference_threshold * mask_scalar
+        converted_mask = converted_mask / half_weighted_distance
+
+        converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast)
+        converted_mask = smootherstep(converted_mask)
+        converted_mask = 1 - converted_mask
+        converted_mask = 255. * converted_mask
+        converted_mask = converted_mask.astype(np.uint8)
+        converted_mask = Image.fromarray(converted_mask)
+        converted_mask = images.resize_image(2, converted_mask, width, height)
+        converted_mask = proc.create_binary_mask(converted_mask, round=False)
+
+        # Remove aliasing artifacts using a gaussian blur.
+        converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
+
+        # Expand the mask to fit the whole image if needed.
+        if paste_to is not None:
+            converted_mask = proc.uncrop(converted_mask,
+                                         (overlay_image.width, overlay_image.height),
+                                         paste_to)
+
+        masks_for_overlay.append(converted_mask)
+
+        image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
+        image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
+                           mask=ImageOps.invert(converted_mask.convert('L')))
+
+        overlay_images[i] = image_masked.convert('RGBA')
+
+    return masks_for_overlay
+
+
+def apply_masks(
+        settings,
+        nmask,
+        overlay_images,
+        width, height,
+        paste_to):
+    import torch
+    import modules.processing as proc
+    import modules.images as images
+    from PIL import Image, ImageOps, ImageFilter
+
+    converted_mask = nmask[0].float()
+    converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2)
+    converted_mask = 255. * converted_mask
+    converted_mask = converted_mask.cpu().numpy().astype(np.uint8)
+    converted_mask = Image.fromarray(converted_mask)
+    converted_mask = images.resize_image(2, converted_mask, width, height)
+    converted_mask = proc.create_binary_mask(converted_mask, round=False)
+
+    # Remove aliasing artifacts using a gaussian blur.
+    converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
+
+    # Expand the mask to fit the whole image if needed.
+    if paste_to is not None:
+        converted_mask = proc.uncrop(converted_mask,
+                                     (width, height),
+                                     paste_to)
+
+    masks_for_overlay = []
+
+    for i, overlay_image in enumerate(overlay_images):
+        masks_for_overlay[i] = converted_mask
+
+        image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
+        image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
+                           mask=ImageOps.invert(converted_mask.convert('L')))
+
+        overlay_images[i] = image_masked.convert('RGBA')
+
+    return masks_for_overlay
+
+
+def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0):
+    """
+    Generalization convolution filter capable of applying
+    weighted mean, median, maximum, and minimum filters
+    parametrically using an arbitrary kernel.
+
+    Args:
+        img (nparray):
+            The image, a 2-D array of floats, to which the filter is being applied.
+        kernel (nparray):
+            The kernel, a 2-D array of floats.
+        kernel_center (nparray):
+            The kernel center coordinate, a 1-D array with two elements.
+        percentile_min (float):
+            The lower bound of the histogram window used by the filter,
+            from 0 to 1.
+        percentile_max (float):
+            The upper bound of the histogram window used by the filter,
+            from 0 to 1.
+        min_width (float):
+            The minimum size of the histogram window bounds, in weight units.
+            Must be greater than 0.
+
+    Returns:
+        (nparray): A filtered copy of the input image "img", a 2-D array of floats.
+    """
+
+    # Converts an index tuple into a vector.
+    def vec(x):
+        return np.array(x)
+
+    kernel_min = -kernel_center
+    kernel_max = vec(kernel.shape) - kernel_center
+
+    def weighted_histogram_filter_single(idx):
+        idx = vec(idx)
+        min_index = np.maximum(0, idx + kernel_min)
+        max_index = np.minimum(vec(img.shape), idx + kernel_max)
+        window_shape = max_index - min_index
+
+        class WeightedElement:
+            """
+            An element of the histogram, its weight
+            and bounds.
+            """
+
+            def __init__(self, value, weight):
+                self.value: float = value
+                self.weight: float = weight
+                self.window_min: float = 0.0
+                self.window_max: float = 1.0
+
+        # Collect the values in the image as WeightedElements,
+        # weighted by their corresponding kernel values.
+        values = []
+        for window_tup in np.ndindex(tuple(window_shape)):
+            window_index = vec(window_tup)
+            image_index = window_index + min_index
+            centered_kernel_index = image_index - idx
+            kernel_index = centered_kernel_index + kernel_center
+            element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)])
+            values.append(element)
+
+        def sort_key(x: WeightedElement):
+            return x.value
+
+        values.sort(key=sort_key)
+
+        # Calculate the height of the stack (sum)
+        # and each sample's range they occupy in the stack
+        sum = 0
+        for i in range(len(values)):
+            values[i].window_min = sum
+            sum += values[i].weight
+            values[i].window_max = sum
+
+        # Calculate what range of this stack ("window")
+        # we want to get the weighted average across.
+        window_min = sum * percentile_min
+        window_max = sum * percentile_max
+        window_width = window_max - window_min
+
+        # Ensure the window is within the stack and at least a certain size.
+        if window_width < min_width:
+            window_center = (window_min + window_max) / 2
+            window_min = window_center - min_width / 2
+            window_max = window_center + min_width / 2
+
+            if window_max > sum:
+                window_max = sum
+                window_min = sum - min_width
+
+            if window_min < 0:
+                window_min = 0
+                window_max = min_width
+
+        value = 0
+        value_weight = 0
+
+        # Get the weighted average of all the samples
+        # that overlap with the window, weighted
+        # by the size of their overlap.
+        for i in range(len(values)):
+            if window_min >= values[i].window_max:
+                continue
+            if window_max <= values[i].window_min:
+                break
+
+            s = max(window_min, values[i].window_min)
+            e = min(window_max, values[i].window_max)
+            w = e - s
+
+            value += values[i].value * w
+            value_weight += w
+
+        return value / value_weight if value_weight != 0 else 0
+
+    img_out = img.copy()
+
+    # Apply the kernel operation over each pixel.
+    for index in np.ndindex(img.shape):
+        img_out[index] = weighted_histogram_filter_single(index)
+
+    return img_out
+
+
+def smoothstep(x):
+    """
+    The smoothstep function, input should be clamped to 0-1 range.
+    Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
+    """
+    return x * x * (3 - 2 * x)
+
+
+def smootherstep(x):
+    """
+    The smootherstep function, input should be clamped to 0-1 range.
+    Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
+    """
+    return x * x * x * (x * (6 * x - 15) + 10)
+
+
+def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
+    """
+    Creates a Gaussian kernel with thresholded edges.
+
+    Args:
+        stddev_radius (float):
+            Standard deviation of the gaussian kernel, in pixels.
+        max_radius (int):
+            The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2.
+            The kernel is thresholded so that any values one pixel beyond this radius
+            is weighted at 0.
+
+    Returns:
+        (nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
+    """
+
+    # Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
+    def gaussian(sqr_mag):
+        return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
+
+    # Helper function for converting a tuple to an array.
+    def vec(x):
+        return np.array(x)
+
+    """
+    Since a gaussian is unbounded, we need to limit ourselves
+    to a finite range.
+    We taper the ends off at the end of that range so they equal zero
+    while preserving the maximum value of 1 at the mean.
+    """
+    zero_radius = max_radius + 1.0
+    gauss_zero = gaussian(zero_radius * zero_radius)
+    gauss_kernel_scale = 1 / (1 - gauss_zero)
+
+    def gaussian_kernel_func(coordinate):
+        x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0
+        x = gaussian(x)
+        x -= gauss_zero
+        x *= gauss_kernel_scale
+        x = max(0.0, x)
+        return x
+
+    size = max_radius * 2 + 1
+    kernel_center = max_radius
+    kernel = np.zeros((size, size))
+
+    for index in np.ndindex(kernel.shape):
+        kernel[index] = gaussian_kernel_func(vec(index) - kernel_center)
+
+    return kernel, kernel_center
+
+
+# ------------------- Constants -------------------
+
+
+default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2)
+
+enabled_ui_label = "Soft inpainting"
+enabled_gen_param_label = "Soft inpainting enabled"
+enabled_el_id = "soft_inpainting_enabled"
+
+ui_labels = SoftInpaintingSettings(
+    "Schedule bias",
+    "Preservation strength",
+    "Transition contrast boost",
+    "Mask influence",
+    "Difference threshold",
+    "Difference contrast")
+
+ui_info = SoftInpaintingSettings(
+    "Shifts when preservation of original content occurs during denoising.",
+    "How strongly partially masked content should be preserved.",
+    "Amplifies the contrast that may be lost in partially masked regions.",
+    "How strongly the original mask should bias the difference threshold.",
+    "How much an image region can change before the original pixels are not blended in anymore.",
+    "How sharp the transition should be between blended and not blended.")
+
+gen_param_labels = SoftInpaintingSettings(
+    "Soft inpainting schedule bias",
+    "Soft inpainting preservation strength",
+    "Soft inpainting transition contrast boost",
+    "Soft inpainting mask influence",
+    "Soft inpainting difference threshold",
+    "Soft inpainting difference contrast")
+
+el_ids = SoftInpaintingSettings(
+    "mask_blend_power",
+    "mask_blend_scale",
+    "inpaint_detail_preservation",
+    "composite_mask_influence",
+    "composite_difference_threshold",
+    "composite_difference_contrast")
+
+
+# ------------------- Script -------------------
+
+
+class Script(scripts.Script):
+    def __init__(self):
+        self.section = "inpaint"
+        self.masks_for_overlay = None
+        self.overlay_images = None
+
+    def title(self):
+        return "Soft Inpainting"
+
+    def show(self, is_img2img):
+        return scripts.AlwaysVisible if is_img2img else False
+
+    def ui(self, is_img2img):
+        if not is_img2img:
+            return
+
+        with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled:
+            with gr.Group():
+                gr.Markdown(
+                    """
+                    Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity.
+                    **High _Mask blur_** values are recommended!
+                    """)
+
+                power = \
+                    gr.Slider(label=ui_labels.mask_blend_power,
+                              info=ui_info.mask_blend_power,
+                              minimum=0,
+                              maximum=8,
+                              step=0.1,
+                              value=default.mask_blend_power,
+                              elem_id=el_ids.mask_blend_power)
+                scale = \
+                    gr.Slider(label=ui_labels.mask_blend_scale,
+                              info=ui_info.mask_blend_scale,
+                              minimum=0,
+                              maximum=8,
+                              step=0.05,
+                              value=default.mask_blend_scale,
+                              elem_id=el_ids.mask_blend_scale)
+                detail = \
+                    gr.Slider(label=ui_labels.inpaint_detail_preservation,
+                              info=ui_info.inpaint_detail_preservation,
+                              minimum=1,
+                              maximum=32,
+                              step=0.5,
+                              value=default.inpaint_detail_preservation,
+                              elem_id=el_ids.inpaint_detail_preservation)
+
+                gr.Markdown(
+                    """
+                    ### Pixel Composite Settings
+                    """)
+
+                mask_inf = \
+                    gr.Slider(label=ui_labels.composite_mask_influence,
+                              info=ui_info.composite_mask_influence,
+                              minimum=0,
+                              maximum=1,
+                              step=0.05,
+                              value=default.composite_mask_influence,
+                              elem_id=el_ids.composite_mask_influence)
+
+                dif_thresh = \
+                    gr.Slider(label=ui_labels.composite_difference_threshold,
+                              info=ui_info.composite_difference_threshold,
+                              minimum=0,
+                              maximum=8,
+                              step=0.25,
+                              value=default.composite_difference_threshold,
+                              elem_id=el_ids.composite_difference_threshold)
+
+                dif_contr = \
+                    gr.Slider(label=ui_labels.composite_difference_contrast,
+                              info=ui_info.composite_difference_contrast,
+                              minimum=0,
+                              maximum=8,
+                              step=0.25,
+                              value=default.composite_difference_contrast,
+                              elem_id=el_ids.composite_difference_contrast)
+
+                with gr.Accordion("Help", open=False):
+                    gr.Markdown(
+                        f"""
+                        ### {ui_labels.mask_blend_power}
+
+                        The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas).
+                        This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step.
+                        This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation.
+
+                        - **Below 1**: Stronger preservation near the end (with low sigma)
+                        - **1**: Balanced (proportional to sigma)
+                        - **Above 1**: Stronger preservation in the beginning (with high sigma)
+                        """)
+                    gr.Markdown(
+                        f"""
+                        ### {ui_labels.mask_blend_scale}
+
+                        Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content.
+                        This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength.
+
+                        - **Low values**: Favors generated content.
+                        - **High values**: Favors original content.
+                        """)
+                    gr.Markdown(
+                        f"""
+                        ### {ui_labels.inpaint_detail_preservation}
+
+                        This parameter controls how the original latent vectors and denoised latent vectors are interpolated.
+                        With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors.
+                        This can prevent the loss of contrast that occurs with linear interpolation.
+
+                        - **Low values**: Softer blending, details may fade.
+                        - **High values**: Stronger contrast, may over-saturate colors.
+                        """)
+
+                    gr.Markdown(
+                        """
+                        ## Pixel Composite Settings
+
+                        Masks are generated based on how much a part of the image changed after denoising.
+                        These masks are used to blend the original and final images together.
+                        If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process.
+                        """)
+
+                    gr.Markdown(
+                        f"""
+                        ### {ui_labels.composite_mask_influence}
+
+                        This parameter controls how much the mask should bias this sensitivity to difference.
+
+                        - **0**: Ignore the mask, only consider differences in image content.
+                        - **1**: Follow the mask closely despite image content changes.
+                        """)
+
+                    gr.Markdown(
+                        f"""
+                        ### {ui_labels.composite_difference_threshold}
+
+                        This value represents the difference at which the original pixels will have less than 50% opacity.
+
+                        - **Low values**: Two images patches must be almost the same in order to retain original pixels.
+                        - **High values**: Two images patches can be very different and still retain original pixels.
+                        """)
+
+                    gr.Markdown(
+                        f"""
+                        ### {ui_labels.composite_difference_contrast}
+
+                        This value represents the contrast between the opacity of the original and inpainted content.
+
+                        - **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting.
+                        - **High values**: Ghosting will be less common, but transitions may be very sudden.
+                        """)
+
+        self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label),
+                                (power, gen_param_labels.mask_blend_power),
+                                (scale, gen_param_labels.mask_blend_scale),
+                                (detail, gen_param_labels.inpaint_detail_preservation),
+                                (mask_inf, gen_param_labels.composite_mask_influence),
+                                (dif_thresh, gen_param_labels.composite_difference_threshold),
+                                (dif_contr, gen_param_labels.composite_difference_contrast)]
+
+        self.paste_field_names = []
+        for _, field_name in self.infotext_fields:
+            self.paste_field_names.append(field_name)
+
+        return [soft_inpainting_enabled,
+                power,
+                scale,
+                detail,
+                mask_inf,
+                dif_thresh,
+                dif_contr]
+
+    def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
+        if not enabled:
+            return
+
+        if not processing_uses_inpainting(p):
+            return
+
+        # Shut off the rounding it normally does.
+        p.mask_round = False
+
+        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
+
+        # p.extra_generation_params["Mask rounding"] = False
+        settings.add_generation_params(p.extra_generation_params)
+
+    def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf,
+                      dif_thresh, dif_contr):
+        if not enabled:
+            return
+
+        if not processing_uses_inpainting(p):
+            return
+
+        if mba.is_final_blend:
+            mba.blended_latent = mba.current_latent
+            return
+
+        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
+
+        # todo: Why is sigma 2D? Both values are the same.
+        mba.blended_latent = latent_blend(settings,
+                                          mba.init_latent,
+                                          mba.current_latent,
+                                          get_modified_nmask(settings, mba.nmask, mba.sigma[0]))
+
+    def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf,
+                    dif_thresh, dif_contr):
+        if not enabled:
+            return
+
+        if not processing_uses_inpainting(p):
+            return
+
+        nmask = getattr(p, "nmask", None)
+        if nmask is None:
+            return
+
+        from modules import images
+        from modules.shared import opts
+
+        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
+
+        # since the original code puts holes in the existing overlay images,
+        # we have to rebuild them.
+        self.overlay_images = []
+        for img in p.init_images:
+
+            image = images.flatten(img, opts.img2img_background_color)
+
+            if p.paste_to is None and p.resize_mode != 3:
+                image = images.resize_image(p.resize_mode, image, p.width, p.height)
+
+            self.overlay_images.append(image.convert('RGBA'))
+
+        if len(p.init_images) == 1:
+            self.overlay_images = self.overlay_images * p.batch_size
+
+        if getattr(ps.samples, 'already_decoded', False):
+            self.masks_for_overlay = apply_masks(settings=settings,
+                                                 nmask=nmask,
+                                                 overlay_images=self.overlay_images,
+                                                 width=p.width,
+                                                 height=p.height,
+                                                 paste_to=p.paste_to)
+        else:
+            self.masks_for_overlay = apply_adaptive_masks(settings=settings,
+                                                          nmask=nmask,
+                                                          latent_orig=p.init_latent,
+                                                          latent_processed=ps.samples,
+                                                          overlay_images=self.overlay_images,
+                                                          width=p.width,
+                                                          height=p.height,
+                                                          paste_to=p.paste_to)
+
+    def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale,
+                                detail_preservation, mask_inf, dif_thresh, dif_contr):
+        if not enabled:
+            return
+
+        if not processing_uses_inpainting(p):
+            return
+
+        if self.masks_for_overlay is None:
+            return
+
+        if self.overlay_images is None:
+            return
+
+        ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index]
+        ppmo.overlay_image = self.overlay_images[ppmo.index]
diff --git a/scripts/soft_inpainting.py b/scripts/soft_inpainting.py
deleted file mode 100644
index d9024344..00000000
--- a/scripts/soft_inpainting.py
+++ /dev/null
@@ -1,747 +0,0 @@
-import numpy as np
-import gradio as gr
-import math
-from modules.ui_components import InputAccordion
-import modules.scripts as scripts
-
-
-class SoftInpaintingSettings:
-    def __init__(self,
-                 mask_blend_power,
-                 mask_blend_scale,
-                 inpaint_detail_preservation,
-                 composite_mask_influence,
-                 composite_difference_threshold,
-                 composite_difference_contrast):
-        self.mask_blend_power = mask_blend_power
-        self.mask_blend_scale = mask_blend_scale
-        self.inpaint_detail_preservation = inpaint_detail_preservation
-        self.composite_mask_influence = composite_mask_influence
-        self.composite_difference_threshold = composite_difference_threshold
-        self.composite_difference_contrast = composite_difference_contrast
-
-    def add_generation_params(self, dest):
-        dest[enabled_gen_param_label] = True
-        dest[gen_param_labels.mask_blend_power] = self.mask_blend_power
-        dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale
-        dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation
-        dest[gen_param_labels.composite_mask_influence] = self.composite_mask_influence
-        dest[gen_param_labels.composite_difference_threshold] = self.composite_difference_threshold
-        dest[gen_param_labels.composite_difference_contrast] = self.composite_difference_contrast
-
-
-# ------------------- Methods -------------------
-
-def processing_uses_inpainting(p):
-    # TODO: Figure out a better way to determine if inpainting is being used by p
-    if getattr(p, "image_mask", None) is not None:
-        return True
-
-    if getattr(p, "mask", None) is not None:
-        return True
-
-    if getattr(p, "nmask", None) is not None:
-        return True
-
-    return False
-
-
-def latent_blend(settings, a, b, t):
-    """
-    Interpolates two latent image representations according to the parameter t,
-    where the interpolated vectors' magnitudes are also interpolated separately.
-    The "detail_preservation" factor biases the magnitude interpolation towards
-    the larger of the two magnitudes.
-    """
-    import torch
-
-    # NOTE: We use inplace operations wherever possible.
-
-    # [4][w][h] to [1][4][w][h]
-    t2 = t.unsqueeze(0)
-    # [4][w][h] to [1][1][w][h] - the [4] seem redundant.
-    t3 = t[0].unsqueeze(0).unsqueeze(0)
-
-    one_minus_t2 = 1 - t2
-    one_minus_t3 = 1 - t3
-
-    # Linearly interpolate the image vectors.
-    a_scaled = a * one_minus_t2
-    b_scaled = b * t2
-    image_interp = a_scaled
-    image_interp.add_(b_scaled)
-    result_type = image_interp.dtype
-    del a_scaled, b_scaled, t2, one_minus_t2
-
-    # Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
-    # 64-bit operations are used here to allow large exponents.
-    current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
-
-    # Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
-    a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
-        settings.inpaint_detail_preservation) * one_minus_t3
-    b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
-        settings.inpaint_detail_preservation) * t3
-    desired_magnitude = a_magnitude
-    desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
-    del a_magnitude, b_magnitude, t3, one_minus_t3
-
-    # Change the linearly interpolated image vectors' magnitudes to the value we want.
-    # This is the last 64-bit operation.
-    image_interp_scaling_factor = desired_magnitude
-    image_interp_scaling_factor.div_(current_magnitude)
-    image_interp_scaling_factor = image_interp_scaling_factor.to(result_type)
-    image_interp_scaled = image_interp
-    image_interp_scaled.mul_(image_interp_scaling_factor)
-    del current_magnitude
-    del desired_magnitude
-    del image_interp
-    del image_interp_scaling_factor
-    del result_type
-
-    return image_interp_scaled
-
-
-def get_modified_nmask(settings, nmask, sigma):
-    """
-    Converts a negative mask representing the transparency of the original latent vectors being overlayed
-    to a mask that is scaled according to the denoising strength for this step.
-
-    Where:
-        0 = fully opaque, infinite density, fully masked
-        1 = fully transparent, zero density, fully unmasked
-
-    We bring this transparency to a power, as this allows one to simulate N number of blending operations
-    where N can be any positive real value. Using this one can control the balance of influence between
-    the denoiser and the original latents according to the sigma value.
-
-    NOTE: "mask" is not used
-    """
-    import torch
-    return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale)
-
-
-def apply_adaptive_masks(
-        settings: SoftInpaintingSettings,
-        nmask,
-        latent_orig,
-        latent_processed,
-        overlay_images,
-        width, height,
-        paste_to):
-    import torch
-    import modules.processing as proc
-    import modules.images as images
-    from PIL import Image, ImageOps, ImageFilter
-
-    # TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control.
-    latent_mask = nmask[0].float()
-    # convert the original mask into a form we use to scale distances for thresholding
-    mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2))
-    mask_scalar = (0.5 * (1 - settings.composite_mask_influence)
-                   + mask_scalar * settings.composite_mask_influence)
-    mask_scalar = mask_scalar / (1.00001 - mask_scalar)
-    mask_scalar = mask_scalar.cpu().numpy()
-
-    latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
-
-    kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
-
-    masks_for_overlay = []
-
-    for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)):
-        converted_mask = distance_map.float().cpu().numpy()
-        converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
-                                                   percentile_min=0.9, percentile_max=1, min_width=1)
-        converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
-                                                   percentile_min=0.25, percentile_max=0.75, min_width=1)
-
-        # The distance at which opacity of original decreases to 50%
-        half_weighted_distance = settings.composite_difference_threshold * mask_scalar
-        converted_mask = converted_mask / half_weighted_distance
-
-        converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast)
-        converted_mask = smootherstep(converted_mask)
-        converted_mask = 1 - converted_mask
-        converted_mask = 255. * converted_mask
-        converted_mask = converted_mask.astype(np.uint8)
-        converted_mask = Image.fromarray(converted_mask)
-        converted_mask = images.resize_image(2, converted_mask, width, height)
-        converted_mask = proc.create_binary_mask(converted_mask, round=False)
-
-        # Remove aliasing artifacts using a gaussian blur.
-        converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
-
-        # Expand the mask to fit the whole image if needed.
-        if paste_to is not None:
-            converted_mask = proc.uncrop(converted_mask,
-                                         (overlay_image.width, overlay_image.height),
-                                         paste_to)
-
-        masks_for_overlay.append(converted_mask)
-
-        image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
-        image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
-                           mask=ImageOps.invert(converted_mask.convert('L')))
-
-        overlay_images[i] = image_masked.convert('RGBA')
-
-    return masks_for_overlay
-
-
-def apply_masks(
-        settings,
-        nmask,
-        overlay_images,
-        width, height,
-        paste_to):
-    import torch
-    import modules.processing as proc
-    import modules.images as images
-    from PIL import Image, ImageOps, ImageFilter
-
-    converted_mask = nmask[0].float()
-    converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2)
-    converted_mask = 255. * converted_mask
-    converted_mask = converted_mask.cpu().numpy().astype(np.uint8)
-    converted_mask = Image.fromarray(converted_mask)
-    converted_mask = images.resize_image(2, converted_mask, width, height)
-    converted_mask = proc.create_binary_mask(converted_mask, round=False)
-
-    # Remove aliasing artifacts using a gaussian blur.
-    converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
-
-    # Expand the mask to fit the whole image if needed.
-    if paste_to is not None:
-        converted_mask = proc.uncrop(converted_mask,
-                                     (width, height),
-                                     paste_to)
-
-    masks_for_overlay = []
-
-    for i, overlay_image in enumerate(overlay_images):
-        masks_for_overlay[i] = converted_mask
-
-        image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
-        image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
-                           mask=ImageOps.invert(converted_mask.convert('L')))
-
-        overlay_images[i] = image_masked.convert('RGBA')
-
-    return masks_for_overlay
-
-
-def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0):
-    """
-    Generalization convolution filter capable of applying
-    weighted mean, median, maximum, and minimum filters
-    parametrically using an arbitrary kernel.
-
-    Args:
-        img (nparray):
-            The image, a 2-D array of floats, to which the filter is being applied.
-        kernel (nparray):
-            The kernel, a 2-D array of floats.
-        kernel_center (nparray):
-            The kernel center coordinate, a 1-D array with two elements.
-        percentile_min (float):
-            The lower bound of the histogram window used by the filter,
-            from 0 to 1.
-        percentile_max (float):
-            The upper bound of the histogram window used by the filter,
-            from 0 to 1.
-        min_width (float):
-            The minimum size of the histogram window bounds, in weight units.
-            Must be greater than 0.
-
-    Returns:
-        (nparray): A filtered copy of the input image "img", a 2-D array of floats.
-    """
-
-    # Converts an index tuple into a vector.
-    def vec(x):
-        return np.array(x)
-
-    kernel_min = -kernel_center
-    kernel_max = vec(kernel.shape) - kernel_center
-
-    def weighted_histogram_filter_single(idx):
-        idx = vec(idx)
-        min_index = np.maximum(0, idx + kernel_min)
-        max_index = np.minimum(vec(img.shape), idx + kernel_max)
-        window_shape = max_index - min_index
-
-        class WeightedElement:
-            """
-            An element of the histogram, its weight
-            and bounds.
-            """
-
-            def __init__(self, value, weight):
-                self.value: float = value
-                self.weight: float = weight
-                self.window_min: float = 0.0
-                self.window_max: float = 1.0
-
-        # Collect the values in the image as WeightedElements,
-        # weighted by their corresponding kernel values.
-        values = []
-        for window_tup in np.ndindex(tuple(window_shape)):
-            window_index = vec(window_tup)
-            image_index = window_index + min_index
-            centered_kernel_index = image_index - idx
-            kernel_index = centered_kernel_index + kernel_center
-            element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)])
-            values.append(element)
-
-        def sort_key(x: WeightedElement):
-            return x.value
-
-        values.sort(key=sort_key)
-
-        # Calculate the height of the stack (sum)
-        # and each sample's range they occupy in the stack
-        sum = 0
-        for i in range(len(values)):
-            values[i].window_min = sum
-            sum += values[i].weight
-            values[i].window_max = sum
-
-        # Calculate what range of this stack ("window")
-        # we want to get the weighted average across.
-        window_min = sum * percentile_min
-        window_max = sum * percentile_max
-        window_width = window_max - window_min
-
-        # Ensure the window is within the stack and at least a certain size.
-        if window_width < min_width:
-            window_center = (window_min + window_max) / 2
-            window_min = window_center - min_width / 2
-            window_max = window_center + min_width / 2
-
-            if window_max > sum:
-                window_max = sum
-                window_min = sum - min_width
-
-            if window_min < 0:
-                window_min = 0
-                window_max = min_width
-
-        value = 0
-        value_weight = 0
-
-        # Get the weighted average of all the samples
-        # that overlap with the window, weighted
-        # by the size of their overlap.
-        for i in range(len(values)):
-            if window_min >= values[i].window_max:
-                continue
-            if window_max <= values[i].window_min:
-                break
-
-            s = max(window_min, values[i].window_min)
-            e = min(window_max, values[i].window_max)
-            w = e - s
-
-            value += values[i].value * w
-            value_weight += w
-
-        return value / value_weight if value_weight != 0 else 0
-
-    img_out = img.copy()
-
-    # Apply the kernel operation over each pixel.
-    for index in np.ndindex(img.shape):
-        img_out[index] = weighted_histogram_filter_single(index)
-
-    return img_out
-
-
-def smoothstep(x):
-    """
-    The smoothstep function, input should be clamped to 0-1 range.
-    Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
-    """
-    return x * x * (3 - 2 * x)
-
-
-def smootherstep(x):
-    """
-    The smootherstep function, input should be clamped to 0-1 range.
-    Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
-    """
-    return x * x * x * (x * (6 * x - 15) + 10)
-
-
-def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
-    """
-    Creates a Gaussian kernel with thresholded edges.
-
-    Args:
-        stddev_radius (float):
-            Standard deviation of the gaussian kernel, in pixels.
-        max_radius (int):
-            The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2.
-            The kernel is thresholded so that any values one pixel beyond this radius
-            is weighted at 0.
-
-    Returns:
-        (nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
-    """
-
-    # Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
-    def gaussian(sqr_mag):
-        return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
-
-    # Helper function for converting a tuple to an array.
-    def vec(x):
-        return np.array(x)
-
-    """
-    Since a gaussian is unbounded, we need to limit ourselves
-    to a finite range.
-    We taper the ends off at the end of that range so they equal zero
-    while preserving the maximum value of 1 at the mean.
-    """
-    zero_radius = max_radius + 1.0
-    gauss_zero = gaussian(zero_radius * zero_radius)
-    gauss_kernel_scale = 1 / (1 - gauss_zero)
-
-    def gaussian_kernel_func(coordinate):
-        x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0
-        x = gaussian(x)
-        x -= gauss_zero
-        x *= gauss_kernel_scale
-        x = max(0.0, x)
-        return x
-
-    size = max_radius * 2 + 1
-    kernel_center = max_radius
-    kernel = np.zeros((size, size))
-
-    for index in np.ndindex(kernel.shape):
-        kernel[index] = gaussian_kernel_func(vec(index) - kernel_center)
-
-    return kernel, kernel_center
-
-
-# ------------------- Constants -------------------
-
-
-default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2)
-
-enabled_ui_label = "Soft inpainting"
-enabled_gen_param_label = "Soft inpainting enabled"
-enabled_el_id = "soft_inpainting_enabled"
-
-ui_labels = SoftInpaintingSettings(
-    "Schedule bias",
-    "Preservation strength",
-    "Transition contrast boost",
-    "Mask influence",
-    "Difference threshold",
-    "Difference contrast")
-
-ui_info = SoftInpaintingSettings(
-    "Shifts when preservation of original content occurs during denoising.",
-    "How strongly partially masked content should be preserved.",
-    "Amplifies the contrast that may be lost in partially masked regions.",
-    "How strongly the original mask should bias the difference threshold.",
-    "How much an image region can change before the original pixels are not blended in anymore.",
-    "How sharp the transition should be between blended and not blended.")
-
-gen_param_labels = SoftInpaintingSettings(
-    "Soft inpainting schedule bias",
-    "Soft inpainting preservation strength",
-    "Soft inpainting transition contrast boost",
-    "Soft inpainting mask influence",
-    "Soft inpainting difference threshold",
-    "Soft inpainting difference contrast")
-
-el_ids = SoftInpaintingSettings(
-    "mask_blend_power",
-    "mask_blend_scale",
-    "inpaint_detail_preservation",
-    "composite_mask_influence",
-    "composite_difference_threshold",
-    "composite_difference_contrast")
-
-
-# ------------------- Script -------------------
-
-
-class Script(scripts.Script):
-    def __init__(self):
-        self.section = "inpaint"
-        self.masks_for_overlay = None
-        self.overlay_images = None
-
-    def title(self):
-        return "Soft Inpainting"
-
-    def show(self, is_img2img):
-        return scripts.AlwaysVisible if is_img2img else False
-
-    def ui(self, is_img2img):
-        if not is_img2img:
-            return
-
-        with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled:
-            with gr.Group():
-                gr.Markdown(
-                    """
-                    Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity.
-                    **High _Mask blur_** values are recommended!
-                    """)
-
-                power = \
-                    gr.Slider(label=ui_labels.mask_blend_power,
-                              info=ui_info.mask_blend_power,
-                              minimum=0,
-                              maximum=8,
-                              step=0.1,
-                              value=default.mask_blend_power,
-                              elem_id=el_ids.mask_blend_power)
-                scale = \
-                    gr.Slider(label=ui_labels.mask_blend_scale,
-                              info=ui_info.mask_blend_scale,
-                              minimum=0,
-                              maximum=8,
-                              step=0.05,
-                              value=default.mask_blend_scale,
-                              elem_id=el_ids.mask_blend_scale)
-                detail = \
-                    gr.Slider(label=ui_labels.inpaint_detail_preservation,
-                              info=ui_info.inpaint_detail_preservation,
-                              minimum=1,
-                              maximum=32,
-                              step=0.5,
-                              value=default.inpaint_detail_preservation,
-                              elem_id=el_ids.inpaint_detail_preservation)
-
-                gr.Markdown(
-                    """
-                    ### Pixel Composite Settings
-                    """)
-
-                mask_inf = \
-                    gr.Slider(label=ui_labels.composite_mask_influence,
-                              info=ui_info.composite_mask_influence,
-                              minimum=0,
-                              maximum=1,
-                              step=0.05,
-                              value=default.composite_mask_influence,
-                              elem_id=el_ids.composite_mask_influence)
-
-                dif_thresh = \
-                    gr.Slider(label=ui_labels.composite_difference_threshold,
-                              info=ui_info.composite_difference_threshold,
-                              minimum=0,
-                              maximum=8,
-                              step=0.25,
-                              value=default.composite_difference_threshold,
-                              elem_id=el_ids.composite_difference_threshold)
-
-                dif_contr = \
-                    gr.Slider(label=ui_labels.composite_difference_contrast,
-                              info=ui_info.composite_difference_contrast,
-                              minimum=0,
-                              maximum=8,
-                              step=0.25,
-                              value=default.composite_difference_contrast,
-                              elem_id=el_ids.composite_difference_contrast)
-
-                with gr.Accordion("Help", open=False):
-                    gr.Markdown(
-                        f"""
-                        ### {ui_labels.mask_blend_power}
-
-                        The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas).
-                        This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step.
-                        This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation.
-
-                        - **Below 1**: Stronger preservation near the end (with low sigma)
-                        - **1**: Balanced (proportional to sigma)
-                        - **Above 1**: Stronger preservation in the beginning (with high sigma)
-                        """)
-                    gr.Markdown(
-                        f"""
-                        ### {ui_labels.mask_blend_scale}
-
-                        Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content.
-                        This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength.
-
-                        - **Low values**: Favors generated content.
-                        - **High values**: Favors original content.
-                        """)
-                    gr.Markdown(
-                        f"""
-                        ### {ui_labels.inpaint_detail_preservation}
-
-                        This parameter controls how the original latent vectors and denoised latent vectors are interpolated.
-                        With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors.
-                        This can prevent the loss of contrast that occurs with linear interpolation.
-
-                        - **Low values**: Softer blending, details may fade.
-                        - **High values**: Stronger contrast, may over-saturate colors.
-                        """)
-
-                    gr.Markdown(
-                        """
-                        ## Pixel Composite Settings
-
-                        Masks are generated based on how much a part of the image changed after denoising.
-                        These masks are used to blend the original and final images together.
-                        If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process.
-                        """)
-
-                    gr.Markdown(
-                        f"""
-                        ### {ui_labels.composite_mask_influence}
-
-                        This parameter controls how much the mask should bias this sensitivity to difference.
-
-                        - **0**: Ignore the mask, only consider differences in image content.
-                        - **1**: Follow the mask closely despite image content changes.
-                        """)
-
-                    gr.Markdown(
-                        f"""
-                        ### {ui_labels.composite_difference_threshold}
-
-                        This value represents the difference at which the original pixels will have less than 50% opacity.
-
-                        - **Low values**: Two images patches must be almost the same in order to retain original pixels.
-                        - **High values**: Two images patches can be very different and still retain original pixels.
-                        """)
-
-                    gr.Markdown(
-                        f"""
-                        ### {ui_labels.composite_difference_contrast}
-
-                        This value represents the contrast between the opacity of the original and inpainted content.
-
-                        - **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting.
-                        - **High values**: Ghosting will be less common, but transitions may be very sudden.
-                        """)
-
-        self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label),
-                                (power, gen_param_labels.mask_blend_power),
-                                (scale, gen_param_labels.mask_blend_scale),
-                                (detail, gen_param_labels.inpaint_detail_preservation),
-                                (mask_inf, gen_param_labels.composite_mask_influence),
-                                (dif_thresh, gen_param_labels.composite_difference_threshold),
-                                (dif_contr, gen_param_labels.composite_difference_contrast)]
-
-        self.paste_field_names = []
-        for _, field_name in self.infotext_fields:
-            self.paste_field_names.append(field_name)
-
-        return [soft_inpainting_enabled,
-                power,
-                scale,
-                detail,
-                mask_inf,
-                dif_thresh,
-                dif_contr]
-
-    def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
-        if not enabled:
-            return
-
-        if not processing_uses_inpainting(p):
-            return
-
-        # Shut off the rounding it normally does.
-        p.mask_round = False
-
-        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
-
-        # p.extra_generation_params["Mask rounding"] = False
-        settings.add_generation_params(p.extra_generation_params)
-
-    def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf,
-                      dif_thresh, dif_contr):
-        if not enabled:
-            return
-
-        if not processing_uses_inpainting(p):
-            return
-
-        if mba.is_final_blend:
-            mba.blended_latent = mba.current_latent
-            return
-
-        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
-
-        # todo: Why is sigma 2D? Both values are the same.
-        mba.blended_latent = latent_blend(settings,
-                                          mba.init_latent,
-                                          mba.current_latent,
-                                          get_modified_nmask(settings, mba.nmask, mba.sigma[0]))
-
-    def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf,
-                    dif_thresh, dif_contr):
-        if not enabled:
-            return
-
-        if not processing_uses_inpainting(p):
-            return
-
-        nmask = getattr(p, "nmask", None)
-        if nmask is None:
-            return
-
-        from modules import images
-        from modules.shared import opts
-
-        settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
-
-        # since the original code puts holes in the existing overlay images,
-        # we have to rebuild them.
-        self.overlay_images = []
-        for img in p.init_images:
-
-            image = images.flatten(img, opts.img2img_background_color)
-
-            if p.paste_to is None and p.resize_mode != 3:
-                image = images.resize_image(p.resize_mode, image, p.width, p.height)
-
-            self.overlay_images.append(image.convert('RGBA'))
-
-        if len(p.init_images) == 1:
-            self.overlay_images = self.overlay_images * p.batch_size
-
-        if getattr(ps.samples, 'already_decoded', False):
-            self.masks_for_overlay = apply_masks(settings=settings,
-                                                 nmask=nmask,
-                                                 overlay_images=self.overlay_images,
-                                                 width=p.width,
-                                                 height=p.height,
-                                                 paste_to=p.paste_to)
-        else:
-            self.masks_for_overlay = apply_adaptive_masks(settings=settings,
-                                                          nmask=nmask,
-                                                          latent_orig=p.init_latent,
-                                                          latent_processed=ps.samples,
-                                                          overlay_images=self.overlay_images,
-                                                          width=p.width,
-                                                          height=p.height,
-                                                          paste_to=p.paste_to)
-
-    def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale,
-                                detail_preservation, mask_inf, dif_thresh, dif_contr):
-        if not enabled:
-            return
-
-        if not processing_uses_inpainting(p):
-            return
-
-        if self.masks_for_overlay is None:
-            return
-
-        if self.overlay_images is None:
-            return
-
-        ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index]
-        ppmo.overlay_image = self.overlay_images[ppmo.index]
-- 
cgit v1.2.3


From 59d060fd5ea93fcc3fdbfbd13b6e20fda06ecf94 Mon Sep 17 00:00:00 2001
From: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date: Sat, 30 Dec 2023 17:11:03 +0900
Subject: More lora not found warning

---
 extensions-builtin/Lora/networks.py            | 8 +++++++-
 extensions-builtin/Lora/scripts/lora_script.py | 2 ++
 2 files changed, 9 insertions(+), 1 deletion(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 985b2753..72ebd624 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -1,3 +1,4 @@
+import gradio as gr
 import logging
 import os
 import re
@@ -314,7 +315,12 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
                 emb_db.skipped_embeddings[name] = embedding
 
     if failed_to_load_networks:
-        sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
+        lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
+        sd_hijack.model_hijack.comments.append(lora_not_found_message)
+        if shared.opts.lora_not_found_warning_console:
+            print(f'\n{lora_not_found_message}\n')
+        if shared.opts.lora_not_found_gradio_warning:
+            gr.Warning(lora_not_found_message)
 
     purge_networks_from_memory()
 
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index ef23968c..1518f7e5 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -39,6 +39,8 @@ shared.options_templates.update(shared.options_section(('extra_networks', "Extra
     "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
     "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
     "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
+    "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"),
+    "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"),
 }))
 
 
-- 
cgit v1.2.3


From b0f59342346b1c8b405f97c0e0bb01c6ae05c601 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Mon, 25 Dec 2023 14:43:51 +0200
Subject: Use Spandrel for upscaling and face restoration architectures (aside
 from GFPGAN and LDSR)

---
 extensions-builtin/ScuNET/scripts/scunet_model.py |   13 +-
 extensions-builtin/ScuNET/scunet_model_arch.py    |  268 ------
 extensions-builtin/SwinIR/scripts/swinir_model.py |  126 ++-
 extensions-builtin/SwinIR/swinir_model_arch.py    |  867 ------------------
 extensions-builtin/SwinIR/swinir_model_arch_v2.py | 1017 ---------------------
 modules/codeformer/codeformer_arch.py             |  276 ------
 modules/codeformer/vqgan_arch.py                  |  435 ---------
 modules/codeformer_model.py                       |  195 ++--
 modules/esrgan_model.py                           |  153 +---
 modules/esrgan_model_arch.py                      |  465 ----------
 modules/gfpgan_model.py                           |   13 +-
 modules/launch_utils.py                           |    7 -
 modules/modelloader.py                            |   16 +
 modules/paths.py                                  |    1 -
 modules/realesrgan_model.py                       |  153 ++--
 modules/sysinfo.py                                |    2 -
 modules/upscaler.py                               |    3 +
 requirements.txt                                  |    3 +-
 requirements_versions.txt                         |    4 +-
 19 files changed, 263 insertions(+), 3754 deletions(-)
 delete mode 100644 extensions-builtin/ScuNET/scunet_model_arch.py
 delete mode 100644 extensions-builtin/SwinIR/swinir_model_arch.py
 delete mode 100644 extensions-builtin/SwinIR/swinir_model_arch_v2.py
 delete mode 100644 modules/codeformer/codeformer_arch.py
 delete mode 100644 modules/codeformer/vqgan_arch.py
 delete mode 100644 modules/esrgan_model_arch.py

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index 167d2f64..18cf8e1a 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -7,9 +7,7 @@ from tqdm import tqdm
 
 import modules.upscaler
 from modules import devices, modelloader, script_callbacks, errors
-from scunet_model_arch import SCUNet
 
-from modules.modelloader import load_file_from_url
 from modules.shared import opts
 
 
@@ -120,17 +118,10 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         device = devices.get_device_for('scunet')
         if path.startswith("http"):
             # TODO: this doesn't use `path` at all?
-            filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
+            filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
         else:
             filename = path
-        model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
-        model.load_state_dict(torch.load(filename), strict=True)
-        model.eval()
-        for _, v in model.named_parameters():
-            v.requires_grad = False
-        model = model.to(device)
-
-        return model
+        return modelloader.load_spandrel_model(filename, device=device)
 
 
 def on_ui_settings():
diff --git a/extensions-builtin/ScuNET/scunet_model_arch.py b/extensions-builtin/ScuNET/scunet_model_arch.py
deleted file mode 100644
index b51a8806..00000000
--- a/extensions-builtin/ScuNET/scunet_model_arch.py
+++ /dev/null
@@ -1,268 +0,0 @@
-# -*- coding: utf-8 -*-
-import numpy as np
-import torch
-import torch.nn as nn
-from einops import rearrange
-from einops.layers.torch import Rearrange
-from timm.models.layers import trunc_normal_, DropPath
-
-
-class WMSA(nn.Module):
-    """ Self-attention module in Swin Transformer
-    """
-
-    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
-        super(WMSA, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.head_dim = head_dim
-        self.scale = self.head_dim ** -0.5
-        self.n_heads = input_dim // head_dim
-        self.window_size = window_size
-        self.type = type
-        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
-
-        self.relative_position_params = nn.Parameter(
-            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
-
-        self.linear = nn.Linear(self.input_dim, self.output_dim)
-
-        trunc_normal_(self.relative_position_params, std=.02)
-        self.relative_position_params = torch.nn.Parameter(
-            self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
-                                                                                                                 2).transpose(
-                0, 1))
-
-    def generate_mask(self, h, w, p, shift):
-        """ generating the mask of SW-MSA
-        Args:
-            shift: shift parameters in CyclicShift.
-        Returns:
-            attn_mask: should be (1 1 w p p),
-        """
-        # supporting square.
-        attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
-        if self.type == 'W':
-            return attn_mask
-
-        s = p - shift
-        attn_mask[-1, :, :s, :, s:, :] = True
-        attn_mask[-1, :, s:, :, :s, :] = True
-        attn_mask[:, -1, :, :s, :, s:] = True
-        attn_mask[:, -1, :, s:, :, :s] = True
-        attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
-        return attn_mask
-
-    def forward(self, x):
-        """ Forward pass of Window Multi-head Self-attention module.
-        Args:
-            x: input tensor with shape of [b h w c];
-            attn_mask: attention mask, fill -inf where the value is True;
-        Returns:
-            output: tensor shape [b h w c]
-        """
-        if self.type != 'W':
-            x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
-
-        x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
-        h_windows = x.size(1)
-        w_windows = x.size(2)
-        # square validation
-        # assert h_windows == w_windows
-
-        x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
-        qkv = self.embedding_layer(x)
-        q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
-        sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
-        # Adding learnable relative embedding
-        sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
-        # Using Attn Mask to distinguish different subwindows.
-        if self.type != 'W':
-            attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
-            sim = sim.masked_fill_(attn_mask, float("-inf"))
-
-        probs = nn.functional.softmax(sim, dim=-1)
-        output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
-        output = rearrange(output, 'h b w p c -> b w p (h c)')
-        output = self.linear(output)
-        output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
-
-        if self.type != 'W':
-            output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
-
-        return output
-
-    def relative_embedding(self):
-        cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
-        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
-        # negative is allowed
-        return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
-
-
-class Block(nn.Module):
-    def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
-        """ SwinTransformer Block
-        """
-        super(Block, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        assert type in ['W', 'SW']
-        self.type = type
-        if input_resolution <= window_size:
-            self.type = 'W'
-
-        self.ln1 = nn.LayerNorm(input_dim)
-        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.ln2 = nn.LayerNorm(input_dim)
-        self.mlp = nn.Sequential(
-            nn.Linear(input_dim, 4 * input_dim),
-            nn.GELU(),
-            nn.Linear(4 * input_dim, output_dim),
-        )
-
-    def forward(self, x):
-        x = x + self.drop_path(self.msa(self.ln1(x)))
-        x = x + self.drop_path(self.mlp(self.ln2(x)))
-        return x
-
-
-class ConvTransBlock(nn.Module):
-    def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
-        """ SwinTransformer and Conv Block
-        """
-        super(ConvTransBlock, self).__init__()
-        self.conv_dim = conv_dim
-        self.trans_dim = trans_dim
-        self.head_dim = head_dim
-        self.window_size = window_size
-        self.drop_path = drop_path
-        self.type = type
-        self.input_resolution = input_resolution
-
-        assert self.type in ['W', 'SW']
-        if self.input_resolution <= self.window_size:
-            self.type = 'W'
-
-        self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
-                                 self.type, self.input_resolution)
-        self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
-        self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
-
-        self.conv_block = nn.Sequential(
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
-            nn.ReLU(True),
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
-        )
-
-    def forward(self, x):
-        conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
-        conv_x = self.conv_block(conv_x) + conv_x
-        trans_x = Rearrange('b c h w -> b h w c')(trans_x)
-        trans_x = self.trans_block(trans_x)
-        trans_x = Rearrange('b h w c -> b c h w')(trans_x)
-        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
-        x = x + res
-
-        return x
-
-
-class SCUNet(nn.Module):
-    # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
-    def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
-        super(SCUNet, self).__init__()
-        if config is None:
-            config = [2, 2, 2, 2, 2, 2, 2]
-        self.config = config
-        self.dim = dim
-        self.head_dim = 32
-        self.window_size = 8
-
-        # drop path rate for each layer
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
-
-        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
-
-        begin = 0
-        self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution)
-                        for i in range(config[0])] + \
-                       [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[0]
-        self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution // 2)
-                        for i in range(config[1])] + \
-                       [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[1]
-        self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                       'W' if not i % 2 else 'SW', input_resolution // 4)
-                        for i in range(config[2])] + \
-                       [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[2]
-        self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                      'W' if not i % 2 else 'SW', input_resolution // 8)
-                       for i in range(config[3])]
-
-        begin += config[3]
-        self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution // 4)
-                      for i in range(config[4])]
-
-        begin += config[4]
-        self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution // 2)
-                      for i in range(config[5])]
-
-        begin += config[5]
-        self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
-                     [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
-                                     'W' if not i % 2 else 'SW', input_resolution)
-                      for i in range(config[6])]
-
-        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
-
-        self.m_head = nn.Sequential(*self.m_head)
-        self.m_down1 = nn.Sequential(*self.m_down1)
-        self.m_down2 = nn.Sequential(*self.m_down2)
-        self.m_down3 = nn.Sequential(*self.m_down3)
-        self.m_body = nn.Sequential(*self.m_body)
-        self.m_up3 = nn.Sequential(*self.m_up3)
-        self.m_up2 = nn.Sequential(*self.m_up2)
-        self.m_up1 = nn.Sequential(*self.m_up1)
-        self.m_tail = nn.Sequential(*self.m_tail)
-        # self.apply(self._init_weights)
-
-    def forward(self, x0):
-
-        h, w = x0.size()[-2:]
-        paddingBottom = int(np.ceil(h / 64) * 64 - h)
-        paddingRight = int(np.ceil(w / 64) * 64 - w)
-        x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
-
-        x1 = self.m_head(x0)
-        x2 = self.m_down1(x1)
-        x3 = self.m_down2(x2)
-        x4 = self.m_down3(x3)
-        x = self.m_body(x4)
-        x = self.m_up3(x + x4)
-        x = self.m_up2(x + x3)
-        x = self.m_up1(x + x2)
-        x = self.m_tail(x + x1)
-
-        x = x[..., :h, :w]
-
-        return x
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index ae0d0e6a..85c18b9e 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -1,5 +1,5 @@
+import logging
 import sys
-import platform
 
 import numpy as np
 import torch
@@ -8,13 +8,11 @@ from tqdm import tqdm
 
 from modules import modelloader, devices, script_callbacks, shared
 from modules.shared import opts, state
-from swinir_model_arch import SwinIR
-from swinir_model_arch_v2 import Swin2SR
 from modules.upscaler import Upscaler, UpscalerData
 
 SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
 
-device_swinir = devices.get_device_for('swinir')
+logger = logging.getLogger(__name__)
 
 
 class UpscalerSwinIR(Upscaler):
@@ -37,26 +35,29 @@ class UpscalerSwinIR(Upscaler):
             scalers.append(model_data)
         self.scalers = scalers
 
-    def do_upscale(self, img, model_file):
-        use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
-            and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
+    def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
         current_config = (model_file, opts.SWIN_tile)
 
-        if use_compile and self._cached_model_config == current_config:
+        device = self._get_device()
+
+        if self._cached_model_config == current_config:
             model = self._cached_model
         else:
-            self._cached_model = None
             try:
                 model = self.load_model(model_file)
             except Exception as e:
                 print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
                 return img
-            model = model.to(device_swinir, dtype=devices.dtype)
-            if use_compile:
-                model = torch.compile(model)
-                self._cached_model = model
-                self._cached_model_config = current_config
-        img = upscale(img, model)
+            self._cached_model = model
+            self._cached_model_config = current_config
+
+        img = upscale(
+            img,
+            model,
+            tile=opts.SWIN_tile,
+            tile_overlap=opts.SWIN_tile_overlap,
+            device=device,
+        )
         devices.torch_gc()
         return img
 
@@ -69,69 +70,54 @@ class UpscalerSwinIR(Upscaler):
             )
         else:
             filename = path
-        if filename.endswith(".v2.pth"):
-            model = Swin2SR(
-                upscale=scale,
-                in_chans=3,
-                img_size=64,
-                window_size=8,
-                img_range=1.0,
-                depths=[6, 6, 6, 6, 6, 6],
-                embed_dim=180,
-                num_heads=[6, 6, 6, 6, 6, 6],
-                mlp_ratio=2,
-                upsampler="nearest+conv",
-                resi_connection="1conv",
-            )
-            params = None
-        else:
-            model = SwinIR(
-                upscale=scale,
-                in_chans=3,
-                img_size=64,
-                window_size=8,
-                img_range=1.0,
-                depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
-                embed_dim=240,
-                num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
-                mlp_ratio=2,
-                upsampler="nearest+conv",
-                resi_connection="3conv",
-            )
-            params = "params_ema"
 
-        pretrained_model = torch.load(filename)
-        if params is not None:
-            model.load_state_dict(pretrained_model[params], strict=True)
-        else:
-            model.load_state_dict(pretrained_model, strict=True)
+        model = modelloader.load_spandrel_model(
+            filename,
+            device=self._get_device(),
+            dtype=devices.dtype,
+        )
+        if getattr(opts, 'SWIN_torch_compile', False):
+            try:
+                model = torch.compile(model)
+            except Exception:
+                logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
         return model
 
+    def _get_device(self):
+        return devices.get_device_for('swinir')
+
 
 def upscale(
-        img,
-        model,
-        tile=None,
-        tile_overlap=None,
-        window_size=8,
-        scale=4,
+    img,
+    model,
+    *,
+    tile: int,
+    tile_overlap: int,
+    window_size=8,
+    scale=4,
+    device,
 ):
-    tile = tile or opts.SWIN_tile
-    tile_overlap = tile_overlap or opts.SWIN_tile_overlap
-
 
     img = np.array(img)
     img = img[:, :, ::-1]
     img = np.moveaxis(img, 2, 0) / 255
     img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
+    img = img.unsqueeze(0).to(device, dtype=devices.dtype)
     with torch.no_grad(), devices.autocast():
         _, _, h_old, w_old = img.size()
         h_pad = (h_old // window_size + 1) * window_size - h_old
         w_pad = (w_old // window_size + 1) * window_size - w_old
         img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
         img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
-        output = inference(img, model, tile, tile_overlap, window_size, scale)
+        output = inference(
+            img,
+            model,
+            tile=tile,
+            tile_overlap=tile_overlap,
+            window_size=window_size,
+            scale=scale,
+            device=device,
+        )
         output = output[..., : h_old * scale, : w_old * scale]
         output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
         if output.ndim == 3:
@@ -142,7 +128,16 @@ def upscale(
         return Image.fromarray(output, "RGB")
 
 
-def inference(img, model, tile, tile_overlap, window_size, scale):
+def inference(
+    img,
+    model,
+    *,
+    tile: int,
+    tile_overlap: int,
+    window_size: int,
+    scale: int,
+    device,
+):
     # test the image tile by tile
     b, c, h, w = img.size()
     tile = min(tile, h, w)
@@ -152,8 +147,8 @@ def inference(img, model, tile, tile_overlap, window_size, scale):
     stride = tile - tile_overlap
     h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
     w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-    E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
-    W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
+    E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device).type_as(img)
+    W = torch.zeros_like(E, dtype=devices.dtype, device=device)
 
     with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
         for h_idx in h_idx_list:
@@ -185,8 +180,7 @@ def on_ui_settings():
 
     shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
     shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
-    if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows":    # torch.compile() require pytorch 2.0 or above, and not on Windows
-        shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
+    shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
 
 
 script_callbacks.on_ui_settings(on_ui_settings)
diff --git a/extensions-builtin/SwinIR/swinir_model_arch.py b/extensions-builtin/SwinIR/swinir_model_arch.py
deleted file mode 100644
index 93b93274..00000000
--- a/extensions-builtin/SwinIR/swinir_model_arch.py
+++ /dev/null
@@ -1,867 +0,0 @@
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class SwinIR(nn.Module):
-    r""" SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(SwinIR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            if self.upscale == 4:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            if self.upscale == 4:
-                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for layer in self.layers:
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = SwinIR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/extensions-builtin/SwinIR/swinir_model_arch_v2.py b/extensions-builtin/SwinIR/swinir_model_arch_v2.py
deleted file mode 100644
index dad22cca..00000000
--- a/extensions-builtin/SwinIR/swinir_model_arch_v2.py
+++ /dev/null
@@ -1,1017 +0,0 @@
-# -----------------------------------------------------------------------------------
-# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
-# Written by Conde and Choi et al.
-# -----------------------------------------------------------------------------------
-
-import math
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
-                 pretrained_window_size=(0, 0)):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.pretrained_window_size = pretrained_window_size
-        self.num_heads = num_heads
-
-        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
-
-        # mlp to generate continuous relative position bias
-        self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
-                                     nn.ReLU(inplace=True),
-                                     nn.Linear(512, num_heads, bias=False))
-
-        # get relative_coords_table
-        relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
-        relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
-        relative_coords_table = torch.stack(
-            torch.meshgrid([relative_coords_h,
-                            relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0)  # 1, 2*Wh-1, 2*Ww-1, 2
-        if pretrained_window_size[0] > 0:
-            relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
-        else:
-            relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
-            relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
-        relative_coords_table *= 8  # normalize to -8, 8
-        relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
-            torch.abs(relative_coords_table) + 1.0) / np.log2(8)
-
-        self.register_buffer("relative_coords_table", relative_coords_table)
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(dim))
-            self.v_bias = nn.Parameter(torch.zeros(dim))
-        else:
-            self.q_bias = None
-            self.v_bias = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        # cosine attention
-        attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
-        logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
-        attn = attn * logit_scale
-
-        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
-        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, ' \
-               f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-        pretrained_window_size (int): Window size in pre-training.
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
-            pretrained_window_size=to_2tuple(pretrained_window_size))
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        #assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-        x = shortcut + self.drop_path(self.norm1(x))
-
-        # FFN
-        x = x + self.drop_path(self.norm2(self.mlp(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(2 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.reduction(x)
-        x = self.norm(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        flops += H * W * self.dim // 2
-        return flops
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        pretrained_window_size (int): Local window size in pre-training.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 pretrained_window_size=0):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer,
-                                 pretrained_window_size=pretrained_window_size)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-    def _init_respostnorm(self):
-        for blk in self.blocks:
-            nn.init.constant_(blk.norm1.bias, 0)
-            nn.init.constant_(blk.norm1.weight, 0)
-            nn.init.constant_(blk.norm2.bias, 0)
-            nn.init.constant_(blk.norm2.weight, 0)
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1],
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias,
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-class Upsample_hf(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample_hf, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-
-class Swin2SR(nn.Module):
-    r""" Swin2SR
-        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=(6, 6, 6, 6), num_heads=(6, 6, 6, 6),
-                 window_size=7, mlp_ratio=4., qkv_bias=True,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(Swin2SR, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias,
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-
-        if self.upsampler == 'pixelshuffle_hf':
-            self.layers_hf = nn.ModuleList()
-            for i_layer in range(self.num_layers):
-                layer = RSTB(dim=embed_dim,
-                             input_resolution=(patches_resolution[0],
-                                               patches_resolution[1]),
-                             depth=depths[i_layer],
-                             num_heads=num_heads[i_layer],
-                             window_size=window_size,
-                             mlp_ratio=self.mlp_ratio,
-                             qkv_bias=qkv_bias,
-                             drop=drop_rate, attn_drop=attn_drop_rate,
-                             drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                             norm_layer=norm_layer,
-                             downsample=None,
-                             use_checkpoint=use_checkpoint,
-                             img_size=img_size,
-                             patch_size=patch_size,
-                             resi_connection=resi_connection
-
-                             )
-                self.layers_hf.append(layer)
-
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffle_aux':
-            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))
-            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_after_aux = nn.Sequential(
-                nn.Conv2d(3, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        elif self.upsampler == 'pixelshuffle_hf':
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.upsample = Upsample(upscale, num_feat)
-            self.upsample_hf = Upsample_hf(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-            self.conv_before_upsample_hf = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                nn.LeakyReLU(inplace=True))
-            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, 'only support x4 now.'
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.LeakyReLU(inplace=True))
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward_features_hf(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers_hf:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffle_aux':
-            bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
-            bicubic = self.conv_bicubic(bicubic)
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            aux = self.conv_aux(x) # b, 3, LR_H, LR_W
-            x = self.conv_after_aux(aux)
-            x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
-            x = self.conv_last(x)
-            aux = aux / self.img_range + self.mean
-        elif self.upsampler == 'pixelshuffle_hf':
-            # for classical SR with HF
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x_before = self.conv_before_upsample(x)
-            x_out = self.conv_last(self.upsample(x_before))
-
-            x_hf = self.conv_first_hf(x_before)
-            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
-            x_hf = self.conv_before_upsample_hf(x_hf)
-            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
-            x = x_out + x_hf
-            x_hf = x_hf / self.img_range + self.mean
-
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-        if self.upsampler == "pixelshuffle_aux":
-            return x[:, :, :H*self.upscale, :W*self.upscale], aux
-
-        elif self.upsampler == "pixelshuffle_hf":
-            x_out = x_out / self.img_range + self.mean
-            return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
-
-        else:
-            return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for layer in self.layers:
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 8
-    height = (1024 // upscale // window_size + 1) * window_size
-    width = (720 // upscale // window_size + 1) * window_size
-    model = Swin2SR(upscale=2, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
-    print(model)
-    print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/modules/codeformer/codeformer_arch.py b/modules/codeformer/codeformer_arch.py
deleted file mode 100644
index 12db6814..00000000
--- a/modules/codeformer/codeformer_arch.py
+++ /dev/null
@@ -1,276 +0,0 @@
-# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
-
-import math
-import torch
-from torch import nn, Tensor
-import torch.nn.functional as F
-from typing import Optional
-
-from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
-from basicsr.utils.registry import ARCH_REGISTRY
-
-def calc_mean_std(feat, eps=1e-5):
-    """Calculate mean and std for adaptive_instance_normalization.
-
-    Args:
-        feat (Tensor): 4D tensor.
-        eps (float): A small value added to the variance to avoid
-            divide-by-zero. Default: 1e-5.
-    """
-    size = feat.size()
-    assert len(size) == 4, 'The input feature should be 4D tensor.'
-    b, c = size[:2]
-    feat_var = feat.view(b, c, -1).var(dim=2) + eps
-    feat_std = feat_var.sqrt().view(b, c, 1, 1)
-    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
-    return feat_mean, feat_std
-
-
-def adaptive_instance_normalization(content_feat, style_feat):
-    """Adaptive instance normalization.
-
-    Adjust the reference features to have the similar color and illuminations
-    as those in the degradate features.
-
-    Args:
-        content_feat (Tensor): The reference feature.
-        style_feat (Tensor): The degradate features.
-    """
-    size = content_feat.size()
-    style_mean, style_std = calc_mean_std(style_feat)
-    content_mean, content_std = calc_mean_std(content_feat)
-    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
-    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, x, mask=None):
-        if mask is None:
-            mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
-        not_mask = ~mask
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
-
-
-class TransformerSALayer(nn.Module):
-    def __init__(self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
-        # Implementation of Feedforward model - MLP
-        self.linear1 = nn.Linear(embed_dim, dim_mlp)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_mlp, embed_dim)
-
-        self.norm1 = nn.LayerNorm(embed_dim)
-        self.norm2 = nn.LayerNorm(embed_dim)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward(self, tgt,
-                tgt_mask: Optional[Tensor] = None,
-                tgt_key_padding_mask: Optional[Tensor] = None,
-                query_pos: Optional[Tensor] = None):
-
-        # self attention
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
-                              key_padding_mask=tgt_key_padding_mask)[0]
-        tgt = tgt + self.dropout1(tgt2)
-
-        # ffn
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout2(tgt2)
-        return tgt
-
-class Fuse_sft_block(nn.Module):
-    def __init__(self, in_ch, out_ch):
-        super().__init__()
-        self.encode_enc = ResBlock(2*in_ch, out_ch)
-
-        self.scale = nn.Sequential(
-                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
-
-        self.shift = nn.Sequential(
-                    nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1))
-
-    def forward(self, enc_feat, dec_feat, w=1):
-        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
-        scale = self.scale(enc_feat)
-        shift = self.shift(enc_feat)
-        residual = w * (dec_feat * scale + shift)
-        out = dec_feat + residual
-        return out
-
-
-@ARCH_REGISTRY.register()
-class CodeFormer(VQAutoEncoder):
-    def __init__(self, dim_embd=512, n_head=8, n_layers=9,
-                codebook_size=1024, latent_size=256,
-                connect_list=('32', '64', '128', '256'),
-                fix_modules=('quantize', 'generator')):
-        super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
-
-        if fix_modules is not None:
-            for module in fix_modules:
-                for param in getattr(self, module).parameters():
-                    param.requires_grad = False
-
-        self.connect_list = connect_list
-        self.n_layers = n_layers
-        self.dim_embd = dim_embd
-        self.dim_mlp = dim_embd*2
-
-        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))
-        self.feat_emb = nn.Linear(256, self.dim_embd)
-
-        # transformer
-        self.ft_layers = nn.Sequential(*[TransformerSALayer(embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0)
-                                    for _ in range(self.n_layers)])
-
-        # logits_predict head
-        self.idx_pred_layer = nn.Sequential(
-            nn.LayerNorm(dim_embd),
-            nn.Linear(dim_embd, codebook_size, bias=False))
-
-        self.channels = {
-            '16': 512,
-            '32': 256,
-            '64': 256,
-            '128': 128,
-            '256': 128,
-            '512': 64,
-        }
-
-        # after second residual block for > 16, before attn layer for ==16
-        self.fuse_encoder_block = {'512':2, '256':5, '128':8, '64':11, '32':14, '16':18}
-        # after first residual block for > 16, before attn layer for ==16
-        self.fuse_generator_block = {'16':6, '32': 9, '64':12, '128':15, '256':18, '512':21}
-
-        # fuse_convs_dict
-        self.fuse_convs_dict = nn.ModuleDict()
-        for f_size in self.connect_list:
-            in_ch = self.channels[f_size]
-            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, x, w=0, detach_16=True, code_only=False, adain=False):
-        # ################### Encoder #####################
-        enc_feat_dict = {}
-        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
-        for i, block in enumerate(self.encoder.blocks):
-            x = block(x)
-            if i in out_list:
-                enc_feat_dict[str(x.shape[-1])] = x.clone()
-
-        lq_feat = x
-        # ################# Transformer ###################
-        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
-        pos_emb = self.position_emb.unsqueeze(1).repeat(1,x.shape[0],1)
-        # BCHW -> BC(HW) -> (HW)BC
-        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2,0,1))
-        query_emb = feat_emb
-        # Transformer encoder
-        for layer in self.ft_layers:
-            query_emb = layer(query_emb, query_pos=pos_emb)
-
-        # output logits
-        logits = self.idx_pred_layer(query_emb) # (hw)bn
-        logits = logits.permute(1,0,2) # (hw)bn -> b(hw)n
-
-        if code_only: # for training stage II
-          # logits doesn't need softmax before cross_entropy loss
-            return logits, lq_feat
-
-        # ################# Quantization ###################
-        # if self.training:
-        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
-        #     # b(hw)c -> bc(hw) -> bchw
-        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
-        # ------------
-        soft_one_hot = F.softmax(logits, dim=2)
-        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
-        quant_feat = self.quantize.get_codebook_feat(top_idx, shape=[x.shape[0],16,16,256])
-        # preserve gradients
-        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
-
-        if detach_16:
-            quant_feat = quant_feat.detach() # for training stage III
-        if adain:
-            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
-
-        # ################## Generator ####################
-        x = quant_feat
-        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
-
-        for i, block in enumerate(self.generator.blocks):
-            x = block(x)
-            if i in fuse_list: # fuse after i-th block
-                f_size = str(x.shape[-1])
-                if w>0:
-                    x = self.fuse_convs_dict[f_size](enc_feat_dict[f_size].detach(), x, w)
-        out = x
-        # logits doesn't need softmax before cross_entropy loss
-        return out, logits, lq_feat
diff --git a/modules/codeformer/vqgan_arch.py b/modules/codeformer/vqgan_arch.py
deleted file mode 100644
index 09ee6660..00000000
--- a/modules/codeformer/vqgan_arch.py
+++ /dev/null
@@ -1,435 +0,0 @@
-# this file is copied from CodeFormer repository. Please see comment in modules/codeformer_model.py
-
-'''
-VQGAN code, adapted from the original created by the Unleashing Transformers authors:
-https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
-
-'''
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from basicsr.utils import get_root_logger
-from basicsr.utils.registry import ARCH_REGISTRY
-
-def normalize(in_channels):
-    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-
-
-@torch.jit.script
-def swish(x):
-    return x*torch.sigmoid(x)
-
-
-#  Define VQVAE classes
-class VectorQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.codebook_size, 1.0 / self.codebook_size)
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.emb_dim)
-
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = (z_flattened ** 2).sum(dim=1, keepdim=True) + (self.embedding.weight**2).sum(1) - \
-            2 * torch.matmul(z_flattened, self.embedding.weight.t())
-
-        mean_distance = torch.mean(d)
-        # find closest encodings
-        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-        min_encoding_scores, min_encoding_indices = torch.topk(d, 1, dim=1, largest=False)
-        # [0-1], higher score, higher confidence
-        min_encoding_scores = torch.exp(-min_encoding_scores/10)
-
-        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.codebook_size).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach()-z)**2) + self.beta * torch.mean((z_q - z.detach()) ** 2)
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, {
-            "perplexity": perplexity,
-            "min_encodings": min_encodings,
-            "min_encoding_indices": min_encoding_indices,
-            "min_encoding_scores": min_encoding_scores,
-            "mean_distance": mean_distance
-            }
-
-    def get_codebook_feat(self, indices, shape):
-        # input indices: batch*token_num -> (batch*token_num)*1
-        # shape: batch, height, width, channel
-        indices = indices.view(-1,1)
-        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
-        min_encodings.scatter_(1, indices, 1)
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:  # reshape back to match original input shape
-            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, num_hiddens, straight_through=False, kl_weight=5e-4, temp_init=1.0):
-        super().__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-        self.proj = nn.Conv2d(num_hiddens, codebook_size, 1)  # projects last encoder layer to quantized logits
-        self.embed = nn.Embedding(codebook_size, emb_dim)
-
-    def forward(self, z):
-        hard = self.straight_through if self.training else True
-
-        logits = self.proj(z)
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
-
-        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = self.kl_weight * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
-        min_encoding_indices = soft_one_hot.argmax(dim=1)
-
-        return z_q, diff, {
-            "min_encoding_indices": min_encoding_indices
-        }
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
-
-    def forward(self, x):
-        pad = (0, 1, 0, 1)
-        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1)
-
-    def forward(self, x):
-        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-
-        return x
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channels, out_channels=None):
-        super(ResBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels if out_channels is None else out_channels
-        self.norm1 = normalize(in_channels)
-        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        self.norm2 = normalize(out_channels)
-        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
-        if self.in_channels != self.out_channels:
-            self.conv_out = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
-
-    def forward(self, x_in):
-        x = x_in
-        x = self.norm1(x)
-        x = swish(x)
-        x = self.conv1(x)
-        x = self.norm2(x)
-        x = swish(x)
-        x = self.conv2(x)
-        if self.in_channels != self.out_channels:
-            x_in = self.conv_out(x_in)
-
-        return x + x_in
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = normalize(in_channels)
-        self.q = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=1,
-            stride=1,
-            padding=0
-        )
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)
-        k = k.reshape(b, c, h*w)
-        w_ = torch.bmm(q, k)
-        w_ = w_ * (int(c)**(-0.5))
-        w_ = F.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1)
-        h_ = torch.bmm(v, w_)
-        h_ = h_.reshape(b, c, h, w)
-
-        h_ = self.proj_out(h_)
-
-        return x+h_
-
-
-class Encoder(nn.Module):
-    def __init__(self, in_channels, nf, emb_dim, ch_mult, num_res_blocks, resolution, attn_resolutions):
-        super().__init__()
-        self.nf = nf
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.attn_resolutions = attn_resolutions
-
-        curr_res = self.resolution
-        in_ch_mult = (1,)+tuple(ch_mult)
-
-        blocks = []
-        # initial convultion
-        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
-
-        # residual and downsampling blocks, with attention on smaller res (16x16)
-        for i in range(self.num_resolutions):
-            block_in_ch = nf * in_ch_mult[i]
-            block_out_ch = nf * ch_mult[i]
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-                if curr_res in attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != self.num_resolutions - 1:
-                blocks.append(Downsample(block_in_ch))
-                curr_res = curr_res // 2
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        # normalise and convert to latent size
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, emb_dim, kernel_size=3, stride=1, padding=1))
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
-        super().__init__()
-        self.nf = nf
-        self.ch_mult = ch_mult
-        self.num_resolutions = len(self.ch_mult)
-        self.num_res_blocks = res_blocks
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.in_channels = emb_dim
-        self.out_channels = 3
-        block_in_ch = self.nf * self.ch_mult[-1]
-        curr_res = self.resolution // 2 ** (self.num_resolutions-1)
-
-        blocks = []
-        # initial conv
-        blocks.append(nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1))
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        for i in reversed(range(self.num_resolutions)):
-            block_out_ch = self.nf * self.ch_mult[i]
-
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-
-                if curr_res in self.attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != 0:
-                blocks.append(Upsample(block_in_ch))
-                curr_res = curr_res * 2
-
-        blocks.append(normalize(block_in_ch))
-        blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
-
-        self.blocks = nn.ModuleList(blocks)
-
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-
-        return x
-
-
-@ARCH_REGISTRY.register()
-class VQAutoEncoder(nn.Module):
-    def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
-                beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
-        super().__init__()
-        logger = get_root_logger()
-        self.in_channels = 3
-        self.nf = nf
-        self.n_blocks = res_blocks
-        self.codebook_size = codebook_size
-        self.embed_dim = emb_dim
-        self.ch_mult = ch_mult
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions or [16]
-        self.quantizer_type = quantizer
-        self.encoder = Encoder(
-            self.in_channels,
-            self.nf,
-            self.embed_dim,
-            self.ch_mult,
-            self.n_blocks,
-            self.resolution,
-            self.attn_resolutions
-        )
-        if self.quantizer_type == "nearest":
-            self.beta = beta #0.25
-            self.quantize = VectorQuantizer(self.codebook_size, self.embed_dim, self.beta)
-        elif self.quantizer_type == "gumbel":
-            self.gumbel_num_hiddens = emb_dim
-            self.straight_through = gumbel_straight_through
-            self.kl_weight = gumbel_kl_weight
-            self.quantize = GumbelQuantizer(
-                self.codebook_size,
-                self.embed_dim,
-                self.gumbel_num_hiddens,
-                self.straight_through,
-                self.kl_weight
-            )
-        self.generator = Generator(
-            self.nf,
-            self.embed_dim,
-            self.ch_mult,
-            self.n_blocks,
-            self.resolution,
-            self.attn_resolutions
-        )
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_ema' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_ema'])
-                logger.info(f'vqgan is loaded from: {model_path} [params_ema]')
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-                logger.info(f'vqgan is loaded from: {model_path} [params]')
-            else:
-                raise ValueError('Wrong params!')
-
-
-    def forward(self, x):
-        x = self.encoder(x)
-        quant, codebook_loss, quant_stats = self.quantize(x)
-        x = self.generator(quant)
-        return x, codebook_loss, quant_stats
-
-
-
-# patch based discriminator
-@ARCH_REGISTRY.register()
-class VQGANDiscriminator(nn.Module):
-    def __init__(self, nc=3, ndf=64, n_layers=4, model_path=None):
-        super().__init__()
-
-        layers = [nn.Conv2d(nc, ndf, kernel_size=4, stride=2, padding=1), nn.LeakyReLU(0.2, True)]
-        ndf_mult = 1
-        ndf_mult_prev = 1
-        for n in range(1, n_layers):  # gradually increase the number of filters
-            ndf_mult_prev = ndf_mult
-            ndf_mult = min(2 ** n, 8)
-            layers += [
-                nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=2, padding=1, bias=False),
-                nn.BatchNorm2d(ndf * ndf_mult),
-                nn.LeakyReLU(0.2, True)
-            ]
-
-        ndf_mult_prev = ndf_mult
-        ndf_mult = min(2 ** n_layers, 8)
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult_prev, ndf * ndf_mult, kernel_size=4, stride=1, padding=1, bias=False),
-            nn.BatchNorm2d(ndf * ndf_mult),
-            nn.LeakyReLU(0.2, True)
-        ]
-
-        layers += [
-            nn.Conv2d(ndf * ndf_mult, 1, kernel_size=4, stride=1, padding=1)]  # output 1 channel prediction map
-        self.main = nn.Sequential(*layers)
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location='cpu')
-            if 'params_d' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params_d'])
-            elif 'params' in chkpt:
-                self.load_state_dict(torch.load(model_path, map_location='cpu')['params'])
-            else:
-                raise ValueError('Wrong params!')
-
-    def forward(self, x):
-        return self.main(x)
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index da42b5e9..517eadfd 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -8,9 +8,6 @@ import modules.shared
 from modules import shared, devices, modelloader, errors
 from modules.paths import models_path
 
-# codeformer people made a choice to include modified basicsr library to their project which makes
-# it utterly impossible to use it alongside with other libraries that also use basicsr, like GFPGAN.
-# I am making a choice to include some files from codeformer to work around this issue.
 model_dir = "Codeformer"
 model_path = os.path.join(models_path, model_dir)
 model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
@@ -18,115 +15,127 @@ model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codef
 codeformer = None
 
 
-def setup_model(dirname):
-    os.makedirs(model_path, exist_ok=True)
-
-    path = modules.paths.paths.get("CodeFormer", None)
-    if path is None:
-        return
-
-    try:
+class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
+    def name(self):
+        return "CodeFormer"
+
+    def __init__(self, dirname):
+        self.net = None
+        self.face_helper = None
+        self.cmd_dir = dirname
+
+    def create_models(self):
+        from facexlib.detection import retinaface
+        from facexlib.utils.face_restoration_helper import FaceRestoreHelper
+
+        if self.net is not None and self.face_helper is not None:
+            self.net.to(devices.device_codeformer)
+            return self.net, self.face_helper
+        model_paths = modelloader.load_models(
+            model_path,
+            model_url,
+            self.cmd_dir,
+            download_name='codeformer-v0.1.0.pth',
+            ext_filter=['.pth'],
+        )
+
+        if len(model_paths) != 0:
+            ckpt_path = model_paths[0]
+        else:
+            print("Unable to load codeformer model.")
+            return None, None
+        net = modelloader.load_spandrel_model(ckpt_path, device=devices.device_codeformer)
+
+        if hasattr(retinaface, 'device'):
+            retinaface.device = devices.device_codeformer
+
+        face_helper = FaceRestoreHelper(
+            upscale_factor=1,
+            face_size=512,
+            crop_ratio=(1, 1),
+            det_model='retinaface_resnet50',
+            save_ext='png',
+            use_parse=True,
+            device=devices.device_codeformer,
+        )
+
+        self.net = net
+        self.face_helper = face_helper
+
+    def send_model_to(self, device):
+        self.net.to(device)
+        self.face_helper.face_det.to(device)
+        self.face_helper.face_parse.to(device)
+
+    def restore(self, np_image, w=None):
         from torchvision.transforms.functional import normalize
-        from modules.codeformer.codeformer_arch import CodeFormer
         from basicsr.utils import img2tensor, tensor2img
-        from facelib.utils.face_restoration_helper import FaceRestoreHelper
-        from facelib.detection.retinaface import retinaface
-
-        net_class = CodeFormer
-
-        class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration):
-            def name(self):
-                return "CodeFormer"
-
-            def __init__(self, dirname):
-                self.net = None
-                self.face_helper = None
-                self.cmd_dir = dirname
-
-            def create_models(self):
-
-                if self.net is not None and self.face_helper is not None:
-                    self.net.to(devices.device_codeformer)
-                    return self.net, self.face_helper
-                model_paths = modelloader.load_models(model_path, model_url, self.cmd_dir, download_name='codeformer-v0.1.0.pth', ext_filter=['.pth'])
-                if len(model_paths) != 0:
-                    ckpt_path = model_paths[0]
-                else:
-                    print("Unable to load codeformer model.")
-                    return None, None
-                net = net_class(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(devices.device_codeformer)
-                checkpoint = torch.load(ckpt_path)['params_ema']
-                net.load_state_dict(checkpoint)
-                net.eval()
+        np_image = np_image[:, :, ::-1]
 
-                if hasattr(retinaface, 'device'):
-                    retinaface.device = devices.device_codeformer
-                face_helper = FaceRestoreHelper(1, face_size=512, crop_ratio=(1, 1), det_model='retinaface_resnet50', save_ext='png', use_parse=True, device=devices.device_codeformer)
+        original_resolution = np_image.shape[0:2]
 
-                self.net = net
-                self.face_helper = face_helper
+        self.create_models()
+        if self.net is None or self.face_helper is None:
+            return np_image
 
-                return net, face_helper
+        self.send_model_to(devices.device_codeformer)
 
-            def send_model_to(self, device):
-                self.net.to(device)
-                self.face_helper.face_det.to(device)
-                self.face_helper.face_parse.to(device)
+        self.face_helper.clean_all()
+        self.face_helper.read_image(np_image)
+        self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
+        self.face_helper.align_warp_face()
 
-            def restore(self, np_image, w=None):
-                np_image = np_image[:, :, ::-1]
+        for cropped_face in self.face_helper.cropped_faces:
+            cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
+            normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
+            cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
 
-                original_resolution = np_image.shape[0:2]
+            try:
+                with torch.no_grad():
+                    res = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)
+                    if isinstance(res, tuple):
+                        output = res[0]
+                    else:
+                        output = res
+                    if not isinstance(res, torch.Tensor):
+                        raise TypeError(f"Expected torch.Tensor, got {type(res)}")
+                    restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
+                del output
+                devices.torch_gc()
+            except Exception:
+                errors.report('Failed inference for CodeFormer', exc_info=True)
+                restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
 
-                self.create_models()
-                if self.net is None or self.face_helper is None:
-                    return np_image
+            restored_face = restored_face.astype('uint8')
+            self.face_helper.add_restored_face(restored_face)
 
-                self.send_model_to(devices.device_codeformer)
+        self.face_helper.get_inverse_affine(None)
 
-                self.face_helper.clean_all()
-                self.face_helper.read_image(np_image)
-                self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
-                self.face_helper.align_warp_face()
+        restored_img = self.face_helper.paste_faces_to_input_image()
+        restored_img = restored_img[:, :, ::-1]
 
-                for cropped_face in self.face_helper.cropped_faces:
-                    cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
-                    normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
-                    cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)
+        if original_resolution != restored_img.shape[0:2]:
+            restored_img = cv2.resize(
+                restored_img,
+                (0, 0),
+                fx=original_resolution[1]/restored_img.shape[1],
+                fy=original_resolution[0]/restored_img.shape[0],
+                interpolation=cv2.INTER_LINEAR,
+            )
 
-                    try:
-                        with torch.no_grad():
-                            output = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True)[0]
-                            restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
-                        del output
-                        devices.torch_gc()
-                    except Exception:
-                        errors.report('Failed inference for CodeFormer', exc_info=True)
-                        restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
+        self.face_helper.clean_all()
 
-                    restored_face = restored_face.astype('uint8')
-                    self.face_helper.add_restored_face(restored_face)
+        if shared.opts.face_restoration_unload:
+            self.send_model_to(devices.cpu)
 
-                self.face_helper.get_inverse_affine(None)
+        return restored_img
 
-                restored_img = self.face_helper.paste_faces_to_input_image()
-                restored_img = restored_img[:, :, ::-1]
-
-                if original_resolution != restored_img.shape[0:2]:
-                    restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
-
-                self.face_helper.clean_all()
-
-                if shared.opts.face_restoration_unload:
-                    self.send_model_to(devices.cpu)
-
-                return restored_img
 
+def setup_model(dirname):
+    os.makedirs(model_path, exist_ok=True)
+    try:
         global codeformer
         codeformer = FaceRestorerCodeFormer(dirname)
         shared.face_restorers.append(codeformer)
-
     except Exception:
         errors.report("Error setting up CodeFormer", exc_info=True)
-
-   # sys.path = stored_sys_path
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index c0d22a99..a7c7c9e3 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -1,122 +1,9 @@
-import sys
-
-import torch
-
-import modules.esrgan_model_arch as arch
-from modules import modelloader, devices
+from modules import modelloader, devices, errors
 from modules.shared import opts
 from modules.upscaler import Upscaler, UpscalerData
 from modules.upscaler_utils import upscale_with_model
 
 
-def mod2normal(state_dict):
-    # this code is copied from https://github.com/victorca25/iNNfer
-    if 'conv_first.weight' in state_dict:
-        crt_net = {}
-        items = list(state_dict)
-
-        crt_net['model.0.weight'] = state_dict['conv_first.weight']
-        crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
-        for k in items.copy():
-            if 'RDB' in k:
-                ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
-                if '.weight' in k:
-                    ori_k = ori_k.replace('.weight', '.0.weight')
-                elif '.bias' in k:
-                    ori_k = ori_k.replace('.bias', '.0.bias')
-                crt_net[ori_k] = state_dict[k]
-                items.remove(k)
-
-        crt_net['model.1.sub.23.weight'] = state_dict['trunk_conv.weight']
-        crt_net['model.1.sub.23.bias'] = state_dict['trunk_conv.bias']
-        crt_net['model.3.weight'] = state_dict['upconv1.weight']
-        crt_net['model.3.bias'] = state_dict['upconv1.bias']
-        crt_net['model.6.weight'] = state_dict['upconv2.weight']
-        crt_net['model.6.bias'] = state_dict['upconv2.bias']
-        crt_net['model.8.weight'] = state_dict['HRconv.weight']
-        crt_net['model.8.bias'] = state_dict['HRconv.bias']
-        crt_net['model.10.weight'] = state_dict['conv_last.weight']
-        crt_net['model.10.bias'] = state_dict['conv_last.bias']
-        state_dict = crt_net
-    return state_dict
-
-
-def resrgan2normal(state_dict, nb=23):
-    # this code is copied from https://github.com/victorca25/iNNfer
-    if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
-        re8x = 0
-        crt_net = {}
-        items = list(state_dict)
-
-        crt_net['model.0.weight'] = state_dict['conv_first.weight']
-        crt_net['model.0.bias'] = state_dict['conv_first.bias']
-
-        for k in items.copy():
-            if "rdb" in k:
-                ori_k = k.replace('body.', 'model.1.sub.')
-                ori_k = ori_k.replace('.rdb', '.RDB')
-                if '.weight' in k:
-                    ori_k = ori_k.replace('.weight', '.0.weight')
-                elif '.bias' in k:
-                    ori_k = ori_k.replace('.bias', '.0.bias')
-                crt_net[ori_k] = state_dict[k]
-                items.remove(k)
-
-        crt_net[f'model.1.sub.{nb}.weight'] = state_dict['conv_body.weight']
-        crt_net[f'model.1.sub.{nb}.bias'] = state_dict['conv_body.bias']
-        crt_net['model.3.weight'] = state_dict['conv_up1.weight']
-        crt_net['model.3.bias'] = state_dict['conv_up1.bias']
-        crt_net['model.6.weight'] = state_dict['conv_up2.weight']
-        crt_net['model.6.bias'] = state_dict['conv_up2.bias']
-
-        if 'conv_up3.weight' in state_dict:
-            # modification supporting: https://github.com/ai-forever/Real-ESRGAN/blob/main/RealESRGAN/rrdbnet_arch.py
-            re8x = 3
-            crt_net['model.9.weight'] = state_dict['conv_up3.weight']
-            crt_net['model.9.bias'] = state_dict['conv_up3.bias']
-
-        crt_net[f'model.{8+re8x}.weight'] = state_dict['conv_hr.weight']
-        crt_net[f'model.{8+re8x}.bias'] = state_dict['conv_hr.bias']
-        crt_net[f'model.{10+re8x}.weight'] = state_dict['conv_last.weight']
-        crt_net[f'model.{10+re8x}.bias'] = state_dict['conv_last.bias']
-
-        state_dict = crt_net
-    return state_dict
-
-
-def infer_params(state_dict):
-    # this code is copied from https://github.com/victorca25/iNNfer
-    scale2x = 0
-    scalemin = 6
-    n_uplayer = 0
-    plus = False
-
-    for block in list(state_dict):
-        parts = block.split(".")
-        n_parts = len(parts)
-        if n_parts == 5 and parts[2] == "sub":
-            nb = int(parts[3])
-        elif n_parts == 3:
-            part_num = int(parts[1])
-            if (part_num > scalemin
-                and parts[0] == "model"
-                and parts[2] == "weight"):
-                scale2x += 1
-            if part_num > n_uplayer:
-                n_uplayer = part_num
-                out_nc = state_dict[block].shape[0]
-        if not plus and "conv1x1" in block:
-            plus = True
-
-    nf = state_dict["model.0.weight"].shape[0]
-    in_nc = state_dict["model.0.weight"].shape[1]
-    out_nc = out_nc
-    scale = 2 ** scale2x
-
-    return in_nc, out_nc, nf, nb, plus, scale
-
-
 class UpscalerESRGAN(Upscaler):
     def __init__(self, dirname):
         self.name = "ESRGAN"
@@ -142,12 +29,11 @@ class UpscalerESRGAN(Upscaler):
     def do_upscale(self, img, selected_model):
         try:
             model = self.load_model(selected_model)
-        except Exception as e:
-            print(f"Unable to load ESRGAN model {selected_model}: {e}", file=sys.stderr)
+        except Exception:
+            errors.report(f"Unable to load ESRGAN model {selected_model}", exc_info=True)
             return img
         model.to(devices.device_esrgan)
-        img = esrgan_upscale(model, img)
-        return img
+        return esrgan_upscale(model, img)
 
     def load_model(self, path: str):
         if path.startswith("http"):
@@ -160,33 +46,10 @@ class UpscalerESRGAN(Upscaler):
         else:
             filename = path
 
-        state_dict = torch.load(filename, map_location='cpu' if devices.device_esrgan.type == 'mps' else None)
-
-        if "params_ema" in state_dict:
-            state_dict = state_dict["params_ema"]
-        elif "params" in state_dict:
-            state_dict = state_dict["params"]
-            num_conv = 16 if "realesr-animevideov3" in filename else 32
-            model = arch.SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=num_conv, upscale=4, act_type='prelu')
-            model.load_state_dict(state_dict)
-            model.eval()
-            return model
-
-        if "body.0.rdb1.conv1.weight" in state_dict and "conv_first.weight" in state_dict:
-            nb = 6 if "RealESRGAN_x4plus_anime_6B" in filename else 23
-            state_dict = resrgan2normal(state_dict, nb)
-        elif "conv_first.weight" in state_dict:
-            state_dict = mod2normal(state_dict)
-        elif "model.0.weight" not in state_dict:
-            raise Exception("The file is not a recognized ESRGAN model.")
-
-        in_nc, out_nc, nf, nb, plus, mscale = infer_params(state_dict)
-
-        model = arch.RRDBNet(in_nc=in_nc, out_nc=out_nc, nf=nf, nb=nb, upscale=mscale, plus=plus)
-        model.load_state_dict(state_dict)
-        model.eval()
-
-        return model
+        return modelloader.load_spandrel_model(
+            filename,
+            device=('cpu' if devices.device_esrgan.type == 'mps' else None),
+        )
 
 
 def esrgan_upscale(model, img):
diff --git a/modules/esrgan_model_arch.py b/modules/esrgan_model_arch.py
deleted file mode 100644
index 2b9888ba..00000000
--- a/modules/esrgan_model_arch.py
+++ /dev/null
@@ -1,465 +0,0 @@
-# this file is adapted from https://github.com/victorca25/iNNfer
-
-from collections import OrderedDict
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-####################
-# RRDBNet Generator
-####################
-
-class RRDBNet(nn.Module):
-    def __init__(self, in_nc, out_nc, nf, nb, nr=3, gc=32, upscale=4, norm_type=None,
-            act_type='leakyrelu', mode='CNA', upsample_mode='upconv', convtype='Conv2D',
-            finalact=None, gaussian_noise=False, plus=False):
-        super(RRDBNet, self).__init__()
-        n_upscale = int(math.log(upscale, 2))
-        if upscale == 3:
-            n_upscale = 1
-
-        self.resrgan_scale = 0
-        if in_nc % 16 == 0:
-            self.resrgan_scale = 1
-        elif in_nc != 4 and in_nc % 4 == 0:
-            self.resrgan_scale = 2
-
-        fea_conv = conv_block(in_nc, nf, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
-        rb_blocks = [RRDB(nf, nr, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
-            norm_type=norm_type, act_type=act_type, mode='CNA', convtype=convtype,
-            gaussian_noise=gaussian_noise, plus=plus) for _ in range(nb)]
-        LR_conv = conv_block(nf, nf, kernel_size=3, norm_type=norm_type, act_type=None, mode=mode, convtype=convtype)
-
-        if upsample_mode == 'upconv':
-            upsample_block = upconv_block
-        elif upsample_mode == 'pixelshuffle':
-            upsample_block = pixelshuffle_block
-        else:
-            raise NotImplementedError(f'upsample mode [{upsample_mode}] is not found')
-        if upscale == 3:
-            upsampler = upsample_block(nf, nf, 3, act_type=act_type, convtype=convtype)
-        else:
-            upsampler = [upsample_block(nf, nf, act_type=act_type, convtype=convtype) for _ in range(n_upscale)]
-        HR_conv0 = conv_block(nf, nf, kernel_size=3, norm_type=None, act_type=act_type, convtype=convtype)
-        HR_conv1 = conv_block(nf, out_nc, kernel_size=3, norm_type=None, act_type=None, convtype=convtype)
-
-        outact = act(finalact) if finalact else None
-
-        self.model = sequential(fea_conv, ShortcutBlock(sequential(*rb_blocks, LR_conv)),
-            *upsampler, HR_conv0, HR_conv1, outact)
-
-    def forward(self, x, outm=None):
-        if self.resrgan_scale == 1:
-            feat = pixel_unshuffle(x, scale=4)
-        elif self.resrgan_scale == 2:
-            feat = pixel_unshuffle(x, scale=2)
-        else:
-            feat = x
-
-        return self.model(feat)
-
-
-class RRDB(nn.Module):
-    """
-    Residual in Residual Dense Block
-    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
-    """
-
-    def __init__(self, nf, nr=3, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
-            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
-            spectral_norm=False, gaussian_noise=False, plus=False):
-        super(RRDB, self).__init__()
-        # This is for backwards compatibility with existing models
-        if nr == 3:
-            self.RDB1 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                    gaussian_noise=gaussian_noise, plus=plus)
-            self.RDB2 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                    gaussian_noise=gaussian_noise, plus=plus)
-            self.RDB3 = ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                    norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                    gaussian_noise=gaussian_noise, plus=plus)
-        else:
-            RDB_list = [ResidualDenseBlock_5C(nf, kernel_size, gc, stride, bias, pad_type,
-                                              norm_type, act_type, mode, convtype, spectral_norm=spectral_norm,
-                                              gaussian_noise=gaussian_noise, plus=plus) for _ in range(nr)]
-            self.RDBs = nn.Sequential(*RDB_list)
-
-    def forward(self, x):
-        if hasattr(self, 'RDB1'):
-            out = self.RDB1(x)
-            out = self.RDB2(out)
-            out = self.RDB3(out)
-        else:
-            out = self.RDBs(x)
-        return out * 0.2 + x
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    """
-    Residual Dense Block
-    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
-    Modified options that can be used:
-        - "Partial Convolution based Padding" arXiv:1811.11718
-        - "Spectral normalization" arXiv:1802.05957
-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
-            {Rakotonirina} and A. {Rasoanaivo}
-    """
-
-    def __init__(self, nf=64, kernel_size=3, gc=32, stride=1, bias=1, pad_type='zero',
-            norm_type=None, act_type='leakyrelu', mode='CNA', convtype='Conv2D',
-            spectral_norm=False, gaussian_noise=False, plus=False):
-        super(ResidualDenseBlock_5C, self).__init__()
-
-        self.noise = GaussianNoise() if gaussian_noise else None
-        self.conv1x1 = conv1x1(nf, gc) if plus else None
-
-        self.conv1 = conv_block(nf, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        self.conv2 = conv_block(nf+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        self.conv3 = conv_block(nf+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        self.conv4 = conv_block(nf+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=act_type, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-        if mode == 'CNA':
-            last_act = None
-        else:
-            last_act = act_type
-        self.conv5 = conv_block(nf+4*gc, nf, 3, stride, bias=bias, pad_type=pad_type,
-            norm_type=norm_type, act_type=last_act, mode=mode, convtype=convtype,
-            spectral_norm=spectral_norm)
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv2(torch.cat((x, x1), 1))
-        if self.conv1x1:
-            x2 = x2 + self.conv1x1(x)
-        x3 = self.conv3(torch.cat((x, x1, x2), 1))
-        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
-        if self.conv1x1:
-            x4 = x4 + x2
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        if self.noise:
-            return self.noise(x5.mul(0.2) + x)
-        else:
-            return x5 * 0.2 + x
-
-
-####################
-# ESRGANplus
-####################
-
-class GaussianNoise(nn.Module):
-    def __init__(self, sigma=0.1, is_relative_detach=False):
-        super().__init__()
-        self.sigma = sigma
-        self.is_relative_detach = is_relative_detach
-        self.noise = torch.tensor(0, dtype=torch.float)
-
-    def forward(self, x):
-        if self.training and self.sigma != 0:
-            self.noise = self.noise.to(x.device)
-            scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
-            sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
-            x = x + sampled_noise
-        return x
-
-def conv1x1(in_planes, out_planes, stride=1):
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-####################
-# SRVGGNetCompact
-####################
-
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-    This class is copied from https://github.com/xinntao/Real-ESRGAN
-    """
-
-    def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu'):
-        super(SRVGGNetCompact, self).__init__()
-        self.num_in_ch = num_in_ch
-        self.num_out_ch = num_out_ch
-        self.num_feat = num_feat
-        self.num_conv = num_conv
-        self.upscale = upscale
-        self.act_type = act_type
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(num_in_ch, num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == 'relu':
-            activation = nn.ReLU(inplace=True)
-        elif act_type == 'prelu':
-            activation = nn.PReLU(num_parameters=num_feat)
-        elif act_type == 'leakyrelu':
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)
-
-        # the body structure
-        for _ in range(num_conv):
-            self.body.append(nn.Conv2d(num_feat, num_feat, 3, 1, 1))
-            # activation
-            if act_type == 'relu':
-                activation = nn.ReLU(inplace=True)
-            elif act_type == 'prelu':
-                activation = nn.PReLU(num_parameters=num_feat)
-            elif act_type == 'leakyrelu':
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)
-
-        # the last conv
-        self.body.append(nn.Conv2d(num_feat, num_out_ch * upscale * upscale, 3, 1, 1))
-        # upsample
-        self.upsampler = nn.PixelShuffle(upscale)
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.upscale, mode='nearest')
-        out += base
-        return out
-
-
-####################
-# Upsampler
-####################
-
-class Upsample(nn.Module):
-    r"""Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.
-    The input data is assumed to be of the form
-    `minibatch x channels x [optional depth] x [optional height] x width`.
-    """
-
-    def __init__(self, size=None, scale_factor=None, mode="nearest", align_corners=None):
-        super(Upsample, self).__init__()
-        if isinstance(scale_factor, tuple):
-            self.scale_factor = tuple(float(factor) for factor in scale_factor)
-        else:
-            self.scale_factor = float(scale_factor) if scale_factor else None
-        self.mode = mode
-        self.size = size
-        self.align_corners = align_corners
-
-    def forward(self, x):
-        return nn.functional.interpolate(x, size=self.size, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners)
-
-    def extra_repr(self):
-        if self.scale_factor is not None:
-            info = f'scale_factor={self.scale_factor}'
-        else:
-            info = f'size={self.size}'
-        info += f', mode={self.mode}'
-        return info
-
-
-def pixel_unshuffle(x, scale):
-    """ Pixel unshuffle.
-    Args:
-        x (Tensor): Input feature with shape (b, c, hh, hw).
-        scale (int): Downsample ratio.
-    Returns:
-        Tensor: the pixel unshuffled feature.
-    """
-    b, c, hh, hw = x.size()
-    out_channel = c * (scale**2)
-    assert hh % scale == 0 and hw % scale == 0
-    h = hh // scale
-    w = hw // scale
-    x_view = x.view(b, c, h, scale, w, scale)
-    return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
-
-
-def pixelshuffle_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
-                        pad_type='zero', norm_type=None, act_type='relu', convtype='Conv2D'):
-    """
-    Pixel shuffle layer
-    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
-    Neural Network, CVPR17)
-    """
-    conv = conv_block(in_nc, out_nc * (upscale_factor ** 2), kernel_size, stride, bias=bias,
-                        pad_type=pad_type, norm_type=None, act_type=None, convtype=convtype)
-    pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
-    n = norm(norm_type, out_nc) if norm_type else None
-    a = act(act_type) if act_type else None
-    return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_block(in_nc, out_nc, upscale_factor=2, kernel_size=3, stride=1, bias=True,
-                pad_type='zero', norm_type=None, act_type='relu', mode='nearest', convtype='Conv2D'):
-    """ Upconv layer """
-    upscale_factor = (1, upscale_factor, upscale_factor) if convtype == 'Conv3D' else upscale_factor
-    upsample = Upsample(scale_factor=upscale_factor, mode=mode)
-    conv = conv_block(in_nc, out_nc, kernel_size, stride, bias=bias,
-                        pad_type=pad_type, norm_type=norm_type, act_type=act_type, convtype=convtype)
-    return sequential(upsample, conv)
-
-
-
-
-
-
-
-
-####################
-# Basic blocks
-####################
-
-
-def make_layer(basic_block, num_basic_block, **kwarg):
-    """Make layers by stacking the same blocks.
-    Args:
-        basic_block (nn.module): nn.module class for basic block. (block)
-        num_basic_block (int): number of blocks. (n_layers)
-    Returns:
-        nn.Sequential: Stacked blocks in nn.Sequential.
-    """
-    layers = []
-    for _ in range(num_basic_block):
-        layers.append(basic_block(**kwarg))
-    return nn.Sequential(*layers)
-
-
-def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1, beta=1.0):
-    """ activation helper """
-    act_type = act_type.lower()
-    if act_type == 'relu':
-        layer = nn.ReLU(inplace)
-    elif act_type in ('leakyrelu', 'lrelu'):
-        layer = nn.LeakyReLU(neg_slope, inplace)
-    elif act_type == 'prelu':
-        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
-    elif act_type == 'tanh':  # [-1, 1] range output
-        layer = nn.Tanh()
-    elif act_type == 'sigmoid':  # [0, 1] range output
-        layer = nn.Sigmoid()
-    else:
-        raise NotImplementedError(f'activation layer [{act_type}] is not found')
-    return layer
-
-
-class Identity(nn.Module):
-    def __init__(self, *kwargs):
-        super(Identity, self).__init__()
-
-    def forward(self, x, *kwargs):
-        return x
-
-
-def norm(norm_type, nc):
-    """ Return a normalization layer """
-    norm_type = norm_type.lower()
-    if norm_type == 'batch':
-        layer = nn.BatchNorm2d(nc, affine=True)
-    elif norm_type == 'instance':
-        layer = nn.InstanceNorm2d(nc, affine=False)
-    elif norm_type == 'none':
-        def norm_layer(x): return Identity()
-    else:
-        raise NotImplementedError(f'normalization layer [{norm_type}] is not found')
-    return layer
-
-
-def pad(pad_type, padding):
-    """ padding layer helper """
-    pad_type = pad_type.lower()
-    if padding == 0:
-        return None
-    if pad_type == 'reflect':
-        layer = nn.ReflectionPad2d(padding)
-    elif pad_type == 'replicate':
-        layer = nn.ReplicationPad2d(padding)
-    elif pad_type == 'zero':
-        layer = nn.ZeroPad2d(padding)
-    else:
-        raise NotImplementedError(f'padding layer [{pad_type}] is not implemented')
-    return layer
-
-
-def get_valid_padding(kernel_size, dilation):
-    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
-    padding = (kernel_size - 1) // 2
-    return padding
-
-
-class ShortcutBlock(nn.Module):
-    """ Elementwise sum the output of a submodule to its input """
-    def __init__(self, submodule):
-        super(ShortcutBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = x + self.sub(x)
-        return output
-
-    def __repr__(self):
-        return 'Identity + \n|' + self.sub.__repr__().replace('\n', '\n|')
-
-
-def sequential(*args):
-    """ Flatten Sequential. It unwraps nn.Sequential. """
-    if len(args) == 1:
-        if isinstance(args[0], OrderedDict):
-            raise NotImplementedError('sequential does not support OrderedDict input.')
-        return args[0]  # No sequential is needed.
-    modules = []
-    for module in args:
-        if isinstance(module, nn.Sequential):
-            for submodule in module.children():
-                modules.append(submodule)
-        elif isinstance(module, nn.Module):
-            modules.append(module)
-    return nn.Sequential(*modules)
-
-
-def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=True,
-               pad_type='zero', norm_type=None, act_type='relu', mode='CNA', convtype='Conv2D',
-               spectral_norm=False):
-    """ Conv layer with padding, normalization, activation """
-    assert mode in ['CNA', 'NAC', 'CNAC'], f'Wrong conv mode [{mode}]'
-    padding = get_valid_padding(kernel_size, dilation)
-    p = pad(pad_type, padding) if pad_type and pad_type != 'zero' else None
-    padding = padding if pad_type == 'zero' else 0
-
-    if convtype=='PartialConv2D':
-        from torchvision.ops import PartialConv2d  # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
-        c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-               dilation=dilation, bias=bias, groups=groups)
-    elif convtype=='DeformConv2D':
-        from torchvision.ops import DeformConv2d  # not tested
-        c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-               dilation=dilation, bias=bias, groups=groups)
-    elif convtype=='Conv3D':
-        c = nn.Conv3d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-                dilation=dilation, bias=bias, groups=groups)
-    else:
-        c = nn.Conv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
-                dilation=dilation, bias=bias, groups=groups)
-
-    if spectral_norm:
-        c = nn.utils.spectral_norm(c)
-
-    a = act(act_type) if act_type else None
-    if 'CNA' in mode:
-        n = norm(norm_type, out_nc) if norm_type else None
-        return sequential(p, c, n, a)
-    elif mode == 'NAC':
-        if norm_type is None and act_type is not None:
-            a = act(act_type, inplace=False)
-        n = norm(norm_type, in_nc) if norm_type else None
-        return sequential(n, a, p, c)
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 01d668ec..6b6f17c4 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -1,8 +1,5 @@
 import os
 
-import facexlib
-import gfpgan
-
 import modules.face_restoration
 from modules import paths, shared, devices, modelloader, errors
 
@@ -41,6 +38,8 @@ def gfpgann():
         print("Unable to load gfpgan model!")
         return None
 
+    import facexlib.detection.retinaface
+
     if hasattr(facexlib.detection.retinaface, 'device'):
         facexlib.detection.retinaface.device = devices.device_gfpgan
     model_file_path = model_file
@@ -81,8 +80,10 @@ gfpgan_constructor = None
 def setup_model(dirname):
     try:
         os.makedirs(model_path, exist_ok=True)
-        from gfpgan import GFPGANer
-        from facexlib import detection, parsing  # noqa: F401
+        import gfpgan
+        import facexlib.detection
+        import facexlib.parsing
+
         global user_path
         global have_gfpgan
         global gfpgan_constructor
@@ -111,7 +112,7 @@ def setup_model(dirname):
         facexlib.parsing.load_file_from_url = facex_load_file_from_url2
         user_path = dirname
         have_gfpgan = True
-        gfpgan_constructor = GFPGANer
+        gfpgan_constructor = gfpgan.GFPGANer
 
         class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
             def name(self):
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index dabef0f5..c2cbd8ce 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -345,13 +345,11 @@ def prepare_environment():
     stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
     stable_diffusion_xl_repo = os.environ.get('STABLE_DIFFUSION_XL_REPO', "https://github.com/Stability-AI/generative-models.git")
     k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
-    codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
     blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
     stable_diffusion_xl_commit_hash = os.environ.get('STABLE_DIFFUSION_XL_COMMIT_HASH', "45c443b316737a4ab6e40413d7794a7f5657c19f")
     k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "ab527a9a6d347f364e3d185ba6d714e22d80cb3c")
-    codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
     blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
 
     try:
@@ -408,15 +406,10 @@ def prepare_environment():
     git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
     git_clone(stable_diffusion_xl_repo, repo_dir('generative-models'), "Stable Diffusion XL", stable_diffusion_xl_commit_hash)
     git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
     git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     startup_timer.record("clone repositores")
 
-    if not is_installed("lpips"):
-        run_pip(f"install -r \"{os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}\"", "requirements for CodeFormer")
-        startup_timer.record("install CodeFormer requirements")
-
     if not os.path.isfile(requirements_file):
         requirements_file = os.path.join(script_path, requirements_file)
 
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 098bcb79..30116932 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 import os
 import shutil
 import importlib
@@ -10,6 +11,9 @@ from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, Upscale
 from modules.paths import script_path, models_path
 
 
+logger = logging.getLogger(__name__)
+
+
 def load_file_from_url(
     url: str,
     *,
@@ -177,3 +181,15 @@ def load_upscalers():
         # Special case for UpscalerNone keeps it at the beginning of the list.
         key=lambda x: x.name.lower() if not isinstance(x.scaler, (UpscalerNone, UpscalerLanczos, UpscalerNearest)) else ""
     )
+
+
+def load_spandrel_model(path, *, device, half: bool = False, dtype=None):
+    import spandrel
+    model = spandrel.ModelLoader(device=device).load_from_file(path)
+    if half:
+        model = model.model.half()
+    if dtype:
+        model = model.model.to(dtype=dtype)
+    model.eval()
+    logger.debug("Loaded %s from %s (device=%s, half=%s, dtype=%s)", model, path, device, half, dtype)
+    return model
diff --git a/modules/paths.py b/modules/paths.py
index 187b9496..03064651 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -38,7 +38,6 @@ mute_sdxl_imports()
 path_dirs = [
     (sd_path, 'ldm', 'Stable Diffusion', []),
     (os.path.join(sd_path, '../generative-models'), 'sgm', 'Stable Diffusion XL', ["sgm"]),
-    (os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
     (os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
     (os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
 ]
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index 02841c30..332d8f4b 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -1,9 +1,6 @@
 import os
 
-import numpy as np
-from PIL import Image
-from realesrgan import RealESRGANer
-
+from modules.upscaler_utils import upscale_with_model
 from modules.upscaler import Upscaler, UpscalerData
 from modules.shared import cmd_opts, opts
 from modules import modelloader, errors
@@ -14,29 +11,20 @@ class UpscalerRealESRGAN(Upscaler):
         self.name = "RealESRGAN"
         self.user_path = path
         super().__init__()
-        try:
-            from basicsr.archs.rrdbnet_arch import RRDBNet  # noqa: F401
-            from realesrgan import RealESRGANer  # noqa: F401
-            from realesrgan.archs.srvgg_arch import SRVGGNetCompact  # noqa: F401
-            self.enable = True
-            self.scalers = []
-            scalers = self.load_models(path)
+        self.enable = True
+        self.scalers = []
+        scalers = get_realesrgan_models(self)
 
-            local_model_paths = self.find_models(ext_filter=[".pth"])
-            for scaler in scalers:
-                if scaler.local_data_path.startswith("http"):
-                    filename = modelloader.friendly_name(scaler.local_data_path)
-                    local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
-                    if local_model_candidates:
-                        scaler.local_data_path = local_model_candidates[0]
+        local_model_paths = self.find_models(ext_filter=[".pth"])
+        for scaler in scalers:
+            if scaler.local_data_path.startswith("http"):
+                filename = modelloader.friendly_name(scaler.local_data_path)
+                local_model_candidates = [local_model for local_model in local_model_paths if local_model.endswith(f"{filename}.pth")]
+                if local_model_candidates:
+                    scaler.local_data_path = local_model_candidates[0]
 
-                if scaler.name in opts.realesrgan_enabled_models:
-                    self.scalers.append(scaler)
-
-        except Exception:
-            errors.report("Error importing Real-ESRGAN", exc_info=True)
-            self.enable = False
-            self.scalers = []
+            if scaler.name in opts.realesrgan_enabled_models:
+                self.scalers.append(scaler)
 
     def do_upscale(self, img, path):
         if not self.enable:
@@ -48,20 +36,18 @@ class UpscalerRealESRGAN(Upscaler):
             errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
             return img
 
-        upsampler = RealESRGANer(
-            scale=info.scale,
-            model_path=info.local_data_path,
-            model=info.model(),
-            half=not cmd_opts.no_half and not cmd_opts.upcast_sampling,
-            tile=opts.ESRGAN_tile,
-            tile_pad=opts.ESRGAN_tile_overlap,
+        mod = modelloader.load_spandrel_model(
+            info.local_data_path,
             device=self.device,
+            half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
+        )
+        return upscale_with_model(
+            mod,
+            img,
+            tile_size=opts.ESRGAN_tile,
+            tile_overlap=opts.ESRGAN_tile_overlap,
+            # TODO: `outscale`?
         )
-
-        upsampled = upsampler.enhance(np.array(img), outscale=info.scale)[0]
-
-        image = Image.fromarray(upsampled)
-        return image
 
     def load_model(self, path):
         for scaler in self.scalers:
@@ -76,58 +62,43 @@ class UpscalerRealESRGAN(Upscaler):
                 return scaler
         raise ValueError(f"Unable to find model info: {path}")
 
-    def load_models(self, _):
-        return get_realesrgan_models(self)
-
 
-def get_realesrgan_models(scaler):
-    try:
-        from basicsr.archs.rrdbnet_arch import RRDBNet
-        from realesrgan.archs.srvgg_arch import SRVGGNetCompact
-        models = [
-            UpscalerData(
-                name="R-ESRGAN General 4xV3",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
-                scale=4,
-                upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-            ),
-            UpscalerData(
-                name="R-ESRGAN General WDN 4xV3",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
-                scale=4,
-                upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-            ),
-            UpscalerData(
-                name="R-ESRGAN AnimeVideo",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
-                scale=4,
-                upscaler=scaler,
-                model=lambda: SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=16, upscale=4, act_type='prelu')
-            ),
-            UpscalerData(
-                name="R-ESRGAN 4x+",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
-                scale=4,
-                upscaler=scaler,
-                model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
-            ),
-            UpscalerData(
-                name="R-ESRGAN 4x+ Anime6B",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-                scale=4,
-                upscaler=scaler,
-                model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
-            ),
-            UpscalerData(
-                name="R-ESRGAN 2x+",
-                path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-                scale=2,
-                upscaler=scaler,
-                model=lambda: RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-            ),
-        ]
-        return models
-    except Exception:
-        errors.report("Error making Real-ESRGAN models list", exc_info=True)
+def get_realesrgan_models(scaler: UpscalerRealESRGAN):
+    return [
+        UpscalerData(
+            name="R-ESRGAN General 4xV3",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+            scale=4,
+            upscaler=scaler,
+        ),
+        UpscalerData(
+            name="R-ESRGAN General WDN 4xV3",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+            scale=4,
+            upscaler=scaler,
+        ),
+        UpscalerData(
+            name="R-ESRGAN AnimeVideo",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+            scale=4,
+            upscaler=scaler,
+        ),
+        UpscalerData(
+            name="R-ESRGAN 4x+",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+            scale=4,
+            upscaler=scaler,
+        ),
+        UpscalerData(
+            name="R-ESRGAN 4x+ Anime6B",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+            scale=4,
+            upscaler=scaler,
+        ),
+        UpscalerData(
+            name="R-ESRGAN 2x+",
+            path="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+            scale=2,
+            upscaler=scaler,
+        ),
+    ]
diff --git a/modules/sysinfo.py b/modules/sysinfo.py
index b669edd0..5abf616b 100644
--- a/modules/sysinfo.py
+++ b/modules/sysinfo.py
@@ -26,11 +26,9 @@ environment_whitelist = {
     "OPENCLIP_PACKAGE",
     "STABLE_DIFFUSION_REPO",
     "K_DIFFUSION_REPO",
-    "CODEFORMER_REPO",
     "BLIP_REPO",
     "STABLE_DIFFUSION_COMMIT_HASH",
     "K_DIFFUSION_COMMIT_HASH",
-    "CODEFORMER_COMMIT_HASH",
     "BLIP_COMMIT_HASH",
     "COMMANDLINE_ARGS",
     "IGNORE_CMD_ARGS_ERRORS",
diff --git a/modules/upscaler.py b/modules/upscaler.py
index b256e085..3aee69db 100644
--- a/modules/upscaler.py
+++ b/modules/upscaler.py
@@ -98,6 +98,9 @@ class UpscalerData:
         self.scale = scale
         self.model = model
 
+    def __repr__(self):
+        return f"<UpscalerData name={self.name} path={self.data_path} scale={self.scale}>"
+
 
 class UpscalerNone(Upscaler):
     name = "None"
diff --git a/requirements.txt b/requirements.txt
index 80b43845..36f5674a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,6 +6,7 @@ basicsr
 blendmodes
 clean-fid
 einops
+facexlib
 fastapi>=0.90.1
 gfpgan
 gradio==3.41.2
@@ -20,13 +21,11 @@ open-clip-torch
 piexif
 psutil
 pytorch_lightning
-realesrgan
 requests
 resize-right
 
 safetensors
 scikit-image>=0.19
-timm
 tomesd
 torch
 torchdiffeq
diff --git a/requirements_versions.txt b/requirements_versions.txt
index cb7403a9..042fa708 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -5,6 +5,7 @@ basicsr==1.4.2
 blendmodes==2022
 clean-fid==0.1.35
 einops==0.4.1
+facexlib==0.3.0
 fastapi==0.94.0
 gfpgan==1.3.8
 gradio==3.41.2
@@ -19,11 +20,10 @@ open-clip-torch==2.20.0
 piexif==1.1.3
 psutil==5.9.5
 pytorch_lightning==1.9.4
-realesrgan==0.3.0
 resize-right==0.0.2
 safetensors==0.3.1
 scikit-image==0.21.0
-timm==0.9.2
+spandrel==0.1.6
 tomesd==0.1.3
 torch
 torchdiffeq==0.2.3
-- 
cgit v1.2.3


From 4ad0c0c0a805da4bac03cff86ea17c25a1291546 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sat, 30 Dec 2023 16:37:03 +0200
Subject: Verify architecture for loaded Spandrel models

---
 extensions-builtin/ScuNET/scripts/scunet_model.py |  2 +-
 extensions-builtin/SwinIR/scripts/swinir_model.py |  1 +
 modules/codeformer_model.py                       |  1 +
 modules/esrgan_model.py                           |  1 +
 modules/gfpgan_model.py                           |  1 +
 modules/hat_model.py                              |  1 +
 modules/modelloader.py                            | 13 ++++++++++++-
 modules/realesrgan_model.py                       |  7 ++++---
 8 files changed, 22 insertions(+), 5 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index 18cf8e1a..5f3dd08b 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -121,7 +121,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
             filename = modelloader.load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
         else:
             filename = path
-        return modelloader.load_spandrel_model(filename, device=device)
+        return modelloader.load_spandrel_model(filename, device=device, expected_architecture='SCUNet')
 
 
 def on_ui_settings():
diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index 85c18b9e..aae159af 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -75,6 +75,7 @@ class UpscalerSwinIR(Upscaler):
             filename,
             device=self._get_device(),
             dtype=devices.dtype,
+            expected_architecture="SwinIR",
         )
         if getattr(opts, 'SWIN_torch_compile', False):
             try:
diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py
index ceda4bab..44b84618 100644
--- a/modules/codeformer_model.py
+++ b/modules/codeformer_model.py
@@ -37,6 +37,7 @@ class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration):
             return modelloader.load_spandrel_model(
                 model_path,
                 device=devices.device_codeformer,
+                expected_architecture='CodeFormer',
             ).model
         raise ValueError("No codeformer model found")
 
diff --git a/modules/esrgan_model.py b/modules/esrgan_model.py
index a7c7c9e3..70041ab0 100644
--- a/modules/esrgan_model.py
+++ b/modules/esrgan_model.py
@@ -49,6 +49,7 @@ class UpscalerESRGAN(Upscaler):
         return modelloader.load_spandrel_model(
             filename,
             device=('cpu' if devices.device_esrgan.type == 'mps' else None),
+            expected_architecture='ESRGAN',
         )
 
 
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index a356b56f..48f8ad5e 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -37,6 +37,7 @@ class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
                 net = modelloader.load_spandrel_model(
                     model_path,
                     device=self.get_device(),
+                    expected_architecture='GFPGAN',
                 ).model
                 net.different_w = True  # see https://github.com/chaiNNer-org/spandrel/pull/81
                 return net
diff --git a/modules/hat_model.py b/modules/hat_model.py
index 553e1941..7f2abb41 100644
--- a/modules/hat_model.py
+++ b/modules/hat_model.py
@@ -39,4 +39,5 @@ class UpscalerHAT(Upscaler):
         return modelloader.load_spandrel_model(
             path,
             device=devices.device_esrgan,  # TODO: should probably be device_hat
+            expected_architecture='HAT',
         )
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 30116932..f4182559 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -6,6 +6,8 @@ import shutil
 import importlib
 from urllib.parse import urlparse
 
+import torch
+
 from modules import shared
 from modules.upscaler import Upscaler, UpscalerLanczos, UpscalerNearest, UpscalerNone
 from modules.paths import script_path, models_path
@@ -183,9 +185,18 @@ def load_upscalers():
     )
 
 
-def load_spandrel_model(path, *, device, half: bool = False, dtype=None):
+def load_spandrel_model(
+    path: str,
+    *,
+    device: str | torch.device | None,
+    half: bool = False,
+    dtype: str | None = None,
+    expected_architecture: str | None = None,
+):
     import spandrel
     model = spandrel.ModelLoader(device=device).load_from_file(path)
+    if expected_architecture and model.architecture != expected_architecture:
+        raise TypeError(f"Model {path} is not a {expected_architecture} model")
     if half:
         model = model.model.half()
     if dtype:
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index 332d8f4b..2a2be5ad 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -1,9 +1,9 @@
 import os
 
-from modules.upscaler_utils import upscale_with_model
-from modules.upscaler import Upscaler, UpscalerData
-from modules.shared import cmd_opts, opts
 from modules import modelloader, errors
+from modules.shared import cmd_opts, opts
+from modules.upscaler import Upscaler, UpscalerData
+from modules.upscaler_utils import upscale_with_model
 
 
 class UpscalerRealESRGAN(Upscaler):
@@ -40,6 +40,7 @@ class UpscalerRealESRGAN(Upscaler):
             info.local_data_path,
             device=self.device,
             half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
+            expected_architecture="RealESRGAN",
         )
         return upscale_with_model(
             mod,
-- 
cgit v1.2.3


From bc5ae74c7d8949bab37e260b16e76889b9968099 Mon Sep 17 00:00:00 2001
From: Learwin <6223515+Learwin@users.noreply.github.com>
Date: Sat, 30 Dec 2023 21:52:27 +0100
Subject: Added negative prompts to extra networks lora

---
 extensions-builtin/Lora/ui_edit_user_metadata.py  | 14 ++++++++--
 extensions-builtin/Lora/ui_extra_networks_lora.py |  9 +++++++
 javascript/extraNetworks.js                       | 31 +++++++++++++++--------
 modules/ui_extra_networks.py                      |  5 +++-
 4 files changed, 46 insertions(+), 13 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/ui_edit_user_metadata.py b/extensions-builtin/Lora/ui_edit_user_metadata.py
index c7011909..f7859b21 100644
--- a/extensions-builtin/Lora/ui_edit_user_metadata.py
+++ b/extensions-builtin/Lora/ui_edit_user_metadata.py
@@ -54,12 +54,14 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
         self.slider_preferred_weight = None
         self.edit_notes = None
 
-    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
+    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, negative_weight, notes):
         user_metadata = self.get_user_metadata(name)
         user_metadata["description"] = desc
         user_metadata["sd version"] = sd_version
         user_metadata["activation text"] = activation_text
         user_metadata["preferred weight"] = preferred_weight
+        user_metadata["negative text"] = negative_text
+        user_metadata["negative weight"] = negative_weight
         user_metadata["notes"] = notes
 
         self.write_user_metadata(name, user_metadata)
@@ -127,6 +129,8 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
             user_metadata.get('activation text', ''),
             float(user_metadata.get('preferred weight', 0.0)),
+            user_metadata.get('negative text', ''),
+            float(user_metadata.get('negative weight', 0.0)),
             gr.update(visible=True if tags else False),
             gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
         ]
@@ -162,7 +166,8 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
         self.taginfo = gr.HighlightedText(label="Training dataset tags")
         self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
         self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
-
+        self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts")
+        self.slider_negative_weight = gr.Slider(label='Preferred negative weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
         with gr.Row() as row_random_prompt:
             with gr.Column(scale=8):
                 random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
@@ -198,6 +203,8 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             self.taginfo,
             self.edit_activation_text,
             self.slider_preferred_weight,
+            self.edit_negative_text,
+            self.slider_negative_weight,
             row_random_prompt,
             random_prompt,
         ]
@@ -211,7 +218,10 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             self.select_sd_version,
             self.edit_activation_text,
             self.slider_preferred_weight,
+            self.edit_negative_text,
+            self.slider_negative_weight,
             self.edit_notes,
         ]
 
+
         self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py
index df02c663..09ce2a05 100644
--- a/extensions-builtin/Lora/ui_extra_networks_lora.py
+++ b/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -45,6 +45,15 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
         if activation_text:
             item["prompt"] += " + " + quote_js(" " + activation_text)
 
+        negative_prompt = item["user_metadata"].get("negative text")
+        preferred_negative_weight = item["user_metadata"].get("negative weight")
+        item["negative_prompt"] = quote_js("")
+        if negative_prompt:
+            neg_prompt = negative_prompt
+            if (preferred_negative_weight > 0):
+                neg_prompt = '(' + negative_prompt + ':' + str(preferred_negative_weight) + ')'
+            item["negative_prompt"] = quote_js(neg_prompt)  
+            
         sd_version = item["user_metadata"].get("sd version")
         if sd_version in network.SdVersion.__members__:
             item["sd_version"] = sd_version
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 98a7abb7..2bb9795d 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -185,8 +185,10 @@ onUiLoaded(setupExtraNetworks);
 var re_extranet = /<([^:^>]+:[^:]+):[\d.]+>(.*)/;
 var re_extranet_g = /<([^:^>]+:[^:]+):[\d.]+>/g;
 
-function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
-    var m = text.match(re_extranet);
+var re_extranet_neg = /\(([^:^>]+:[\d.]+)\)/;
+var re_extranet_g_neg = /\(([^:^>]+:[\d.]+)\)/g;
+function tryToRemoveExtraNetworkFromPrompt(textarea, text, isNeg) {
+    var m = text.match(isNeg ? re_extranet_neg : re_extranet);
     var replaced = false;
     var newTextareaText;
     if (m) {
@@ -194,8 +196,8 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
         var extraTextAfterNet = m[2];
         var partToSearch = m[1];
         var foundAtPosition = -1;
-        newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found, net, pos) {
-            m = found.match(re_extranet);
+        newTextareaText = textarea.value.replaceAll(isNeg ? re_extranet_g_neg : re_extranet_g, function(found, net, pos) {
+            m = found.match(isNeg ? re_extranet_neg : re_extranet);
             if (m[1] == partToSearch) {
                 replaced = true;
                 foundAtPosition = pos;
@@ -205,7 +207,7 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
         });
 
         if (foundAtPosition >= 0) {
-            if (newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
+            if (extraTextAfterNet && newTextareaText.substr(foundAtPosition, extraTextAfterNet.length) == extraTextAfterNet) {
                 newTextareaText = newTextareaText.substr(0, foundAtPosition) + newTextareaText.substr(foundAtPosition + extraTextAfterNet.length);
             }
             if (newTextareaText.substr(foundAtPosition - extraTextBeforeNet.length, extraTextBeforeNet.length) == extraTextBeforeNet) {
@@ -230,14 +232,23 @@ function tryToRemoveExtraNetworkFromPrompt(textarea, text) {
     return false;
 }
 
-function cardClicked(tabname, textToAdd, allowNegativePrompt) {
-    var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
+function updatePromptArea(text, textArea, isNeg) {
 
-    if (!tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)) {
-        textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd;
+    if (!tryToRemoveExtraNetworkFromPrompt(textArea, text, isNeg)) {
+        textArea.value = textArea.value + opts.extra_networks_add_text_separator + text;
     }
 
-    updateInput(textarea);
+    updateInput(textArea);
+}
+
+function cardClicked(tabname, textToAdd, textToAddNegative, allowNegativePrompt) {
+    if (textToAddNegative.length > 0) {
+        updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"))
+        updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true)
+    } else {
+        var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
+        updatePromptArea(textToAdd, textarea)
+    }
 }
 
 function saveCardPreview(event, tabname, filename) {
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index fe5d3ba3..b8c02241 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -223,7 +223,10 @@ class ExtraNetworksPage:
 
         onclick = item.get("onclick", None)
         if onclick is None:
-            onclick = '"' + html.escape(f"""return cardClicked({quote_js(tabname)}, {item["prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
+            if "negative_prompt" in item:
+                onclick = '"' + html.escape(f"""return cardClicked({quote_js(tabname)}, {item["prompt"]}, {item["negative_prompt"]}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
+            else:
+                onclick = '"' + html.escape(f"""return cardClicked({quote_js(tabname)}, {item["prompt"]}, {'""'}, {"true" if self.allow_negative_prompt else "false"})""") + '"'
 
         height = f"height: {shared.opts.extra_networks_card_height}px;" if shared.opts.extra_networks_card_height else ''
         width = f"width: {shared.opts.extra_networks_card_width}px;" if shared.opts.extra_networks_card_width else ''
-- 
cgit v1.2.3


From a2f23f9d22dde87bf2529dcb2854a6a5d3d44278 Mon Sep 17 00:00:00 2001
From: Learwin <6223515+Learwin@users.noreply.github.com>
Date: Sat, 30 Dec 2023 22:16:51 +0100
Subject: Code Style fixes

---
 extensions-builtin/Lora/ui_extra_networks_lora.py | 4 ++--
 javascript/extraNetworks.js                       | 6 +++---
 modules/upscaler_utils.py                         | 2 +-
 3 files changed, 6 insertions(+), 6 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py
index 09ce2a05..9a6624e3 100644
--- a/extensions-builtin/Lora/ui_extra_networks_lora.py
+++ b/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -52,8 +52,8 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
             neg_prompt = negative_prompt
             if (preferred_negative_weight > 0):
                 neg_prompt = '(' + negative_prompt + ':' + str(preferred_negative_weight) + ')'
-            item["negative_prompt"] = quote_js(neg_prompt)  
-            
+            item["negative_prompt"] = quote_js(neg_prompt)
+
         sd_version = item["user_metadata"].get("sd version")
         if sd_version in network.SdVersion.__members__:
             item["sd_version"] = sd_version
diff --git a/javascript/extraNetworks.js b/javascript/extraNetworks.js
index 2bb9795d..f1ad19a6 100644
--- a/javascript/extraNetworks.js
+++ b/javascript/extraNetworks.js
@@ -243,11 +243,11 @@ function updatePromptArea(text, textArea, isNeg) {
 
 function cardClicked(tabname, textToAdd, textToAddNegative, allowNegativePrompt) {
     if (textToAddNegative.length > 0) {
-        updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"))
-        updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true)
+        updatePromptArea(textToAdd, gradioApp().querySelector("#" + tabname + "_prompt > label > textarea"));
+        updatePromptArea(textToAddNegative, gradioApp().querySelector("#" + tabname + "_neg_prompt > label > textarea"), true);
     } else {
         var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea");
-        updatePromptArea(textToAdd, textarea)
+        updatePromptArea(textToAdd, textarea);
     }
 }
 
diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 39f78a0b..1d610dbf 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -6,7 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import devices, images
+from modules import images
 
 logger = logging.getLogger(__name__)
 
-- 
cgit v1.2.3


From 777af661a21821994993df3ef566b01df2bb61a0 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sun, 31 Dec 2023 00:09:51 +0200
Subject: Be more clear about Spandrel model nomenclature

---
 extensions-builtin/SwinIR/scripts/swinir_model.py |  6 +++---
 modules/gfpgan_model.py                           | 10 ++++++----
 modules/modelloader.py                            |  2 +-
 modules/realesrgan_model.py                       |  4 ++--
 modules/upscaler_utils.py                         |  2 +-
 5 files changed, 13 insertions(+), 11 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index aae159af..95c7ec64 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -71,7 +71,7 @@ class UpscalerSwinIR(Upscaler):
         else:
             filename = path
 
-        model = modelloader.load_spandrel_model(
+        model_descriptor = modelloader.load_spandrel_model(
             filename,
             device=self._get_device(),
             dtype=devices.dtype,
@@ -79,10 +79,10 @@ class UpscalerSwinIR(Upscaler):
         )
         if getattr(opts, 'SWIN_torch_compile', False):
             try:
-                model = torch.compile(model)
+                model_descriptor.model.compile()
             except Exception:
                 logger.warning("Failed to compile SwinIR model, fallback to JIT", exc_info=True)
-        return model
+        return model_descriptor
 
     def _get_device(self):
         return devices.get_device_for('swinir')
diff --git a/modules/gfpgan_model.py b/modules/gfpgan_model.py
index 48f8ad5e..445b0409 100644
--- a/modules/gfpgan_model.py
+++ b/modules/gfpgan_model.py
@@ -3,6 +3,8 @@ from __future__ import annotations
 import logging
 import os
 
+import torch
+
 from modules import (
     devices,
     errors,
@@ -25,7 +27,7 @@ class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
     def get_device(self):
         return devices.device_gfpgan
 
-    def load_net(self) -> None:
+    def load_net(self) -> torch.Module:
         for model_path in modelloader.load_models(
             model_path=self.model_path,
             model_url=model_url,
@@ -34,13 +36,13 @@ class FaceRestorerGFPGAN(face_restoration_utils.CommonFaceRestoration):
             ext_filter=['.pth'],
         ):
             if 'GFPGAN' in os.path.basename(model_path):
-                net = modelloader.load_spandrel_model(
+                model = modelloader.load_spandrel_model(
                     model_path,
                     device=self.get_device(),
                     expected_architecture='GFPGAN',
                 ).model
-                net.different_w = True  # see https://github.com/chaiNNer-org/spandrel/pull/81
-                return net
+                model.different_w = True  # see https://github.com/chaiNNer-org/spandrel/pull/81
+                return model
         raise ValueError("No GFPGAN model found")
 
     def restore(self, np_image):
diff --git a/modules/modelloader.py b/modules/modelloader.py
index 8bcee08c..a7194137 100644
--- a/modules/modelloader.py
+++ b/modules/modelloader.py
@@ -143,7 +143,7 @@ def load_spandrel_model(
     *,
     device: str | torch.device | None,
     half: bool = False,
-    dtype: str | None = None,
+    dtype: str | torch.dtype | None = None,
     expected_architecture: str | None = None,
 ) -> spandrel.ModelDescriptor:
     import spandrel
diff --git a/modules/realesrgan_model.py b/modules/realesrgan_model.py
index 65f2e880..4d35b695 100644
--- a/modules/realesrgan_model.py
+++ b/modules/realesrgan_model.py
@@ -36,14 +36,14 @@ class UpscalerRealESRGAN(Upscaler):
             errors.report(f"Unable to load RealESRGAN model {path}", exc_info=True)
             return img
 
-        mod = modelloader.load_spandrel_model(
+        model_descriptor = modelloader.load_spandrel_model(
             info.local_data_path,
             device=self.device,
             half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
             expected_architecture="ESRGAN",  # "RealESRGAN" isn't a specific thing for Spandrel
         )
         return upscale_with_model(
-            mod,
+            model_descriptor,
             img,
             tile_size=opts.ESRGAN_tile,
             tile_overlap=opts.ESRGAN_tile_overlap,
diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index dde5d7ad..174c9bc3 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -6,7 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import devices, images
+from modules import images
 
 logger = logging.getLogger(__name__)
 
-- 
cgit v1.2.3


From 6f86b62a1be7993073ba3a789d522e0b8870605a Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sat, 30 Dec 2023 22:53:49 +0200
Subject: Deduplicate tiled inference code from SwinIR/ScuNET

---
 extensions-builtin/ScuNET/scripts/scunet_model.py | 55 ++++-------------
 extensions-builtin/SwinIR/scripts/swinir_model.py | 57 ++----------------
 modules/upscaler_utils.py                         | 72 ++++++++++++++++++++++-
 3 files changed, 87 insertions(+), 97 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index 5f3dd08b..f799cb76 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -3,12 +3,11 @@ import sys
 import PIL.Image
 import numpy as np
 import torch
-from tqdm import tqdm
 
 import modules.upscaler
 from modules import devices, modelloader, script_callbacks, errors
-
 from modules.shared import opts
+from modules.upscaler_utils import tiled_upscale_2
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -40,47 +39,6 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
             scalers.append(scaler_data2)
         self.scalers = scalers
 
-    @staticmethod
-    @torch.no_grad()
-    def tiled_inference(img, model):
-        # test the image tile by tile
-        h, w = img.shape[2:]
-        tile = opts.SCUNET_tile
-        tile_overlap = opts.SCUNET_tile_overlap
-        if tile == 0:
-            return model(img)
-
-        device = devices.get_device_for('scunet')
-        assert tile % 8 == 0, "tile size should be a multiple of window_size"
-        sf = 1
-
-        stride = tile - tile_overlap
-        h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
-        w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-        E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
-        W = torch.zeros_like(E, dtype=devices.dtype, device=device)
-
-        with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
-            for h_idx in h_idx_list:
-
-                for w_idx in w_idx_list:
-
-                    in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
-
-                    out_patch = model(in_patch)
-                    out_patch_mask = torch.ones_like(out_patch)
-
-                    E[
-                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                    ].add_(out_patch)
-                    W[
-                        ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                    ].add_(out_patch_mask)
-                    pbar.update(1)
-        output = E.div_(W)
-
-        return output
-
     def do_upscale(self, img: PIL.Image.Image, selected_file):
 
         devices.torch_gc()
@@ -104,7 +62,16 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
             _img[:, :, :h, :w] = torch_img # pad image
             torch_img = _img
 
-        torch_output = self.tiled_inference(torch_img, model).squeeze(0)
+        with torch.no_grad():
+            torch_output = tiled_upscale_2(
+                torch_img,
+                model,
+                tile_size=opts.SCUNET_tile,
+                tile_overlap=opts.SCUNET_tile_overlap,
+                scale=1,
+                device=devices.get_device_for('scunet'),
+                desc="ScuNET tiles",
+            ).squeeze(0)
         torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
         np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
         del torch_img, torch_output
diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index 95c7ec64..8a555c79 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -4,11 +4,11 @@ import sys
 import numpy as np
 import torch
 from PIL import Image
-from tqdm import tqdm
 
 from modules import modelloader, devices, script_callbacks, shared
-from modules.shared import opts, state
+from modules.shared import opts
 from modules.upscaler import Upscaler, UpscalerData
+from modules.upscaler_utils import tiled_upscale_2
 
 SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
 
@@ -110,14 +110,14 @@ def upscale(
         w_pad = (w_old // window_size + 1) * window_size - w_old
         img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
         img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
-        output = inference(
+        output = tiled_upscale_2(
             img,
             model,
-            tile=tile,
+            tile_size=tile,
             tile_overlap=tile_overlap,
-            window_size=window_size,
             scale=scale,
             device=device,
+            desc="SwinIR tiles",
         )
         output = output[..., : h_old * scale, : w_old * scale]
         output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
@@ -129,53 +129,6 @@ def upscale(
         return Image.fromarray(output, "RGB")
 
 
-def inference(
-    img,
-    model,
-    *,
-    tile: int,
-    tile_overlap: int,
-    window_size: int,
-    scale: int,
-    device,
-):
-    # test the image tile by tile
-    b, c, h, w = img.size()
-    tile = min(tile, h, w)
-    assert tile % window_size == 0, "tile size should be a multiple of window_size"
-    sf = scale
-
-    stride = tile - tile_overlap
-    h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
-    w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
-    E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device).type_as(img)
-    W = torch.zeros_like(E, dtype=devices.dtype, device=device)
-
-    with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
-        for h_idx in h_idx_list:
-            if state.interrupted or state.skipped:
-                break
-
-            for w_idx in w_idx_list:
-                if state.interrupted or state.skipped:
-                    break
-
-                in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
-                out_patch = model(in_patch)
-                out_patch_mask = torch.ones_like(out_patch)
-
-                E[
-                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                ].add_(out_patch)
-                W[
-                ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
-                ].add_(out_patch_mask)
-                pbar.update(1)
-    output = E.div_(W)
-
-    return output
-
-
 def on_ui_settings():
     import gradio as gr
 
diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 174c9bc3..8e413854 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -6,7 +6,7 @@ import torch
 import tqdm
 from PIL import Image
 
-from modules import images
+from modules import images, shared
 
 logger = logging.getLogger(__name__)
 
@@ -68,3 +68,73 @@ def upscale_with_model(
         overlap=grid.overlap * scale_factor,
     )
     return images.combine_grid(newgrid)
+
+
+def tiled_upscale_2(
+    img,
+    model,
+    *,
+    tile_size: int,
+    tile_overlap: int,
+    scale: int,
+    device,
+    desc="Tiled upscale",
+):
+    # Alternative implementation of `upscale_with_model` originally used by
+    # SwinIR and ScuNET.  It differs from `upscale_with_model` in that tiling and
+    # weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
+    # Pillow space without weighting.
+    b, c, h, w = img.size()
+    tile_size = min(tile_size, h, w)
+
+    if tile_size <= 0:
+        logger.debug("Upscaling %s without tiling", img.shape)
+        return model(img)
+
+    stride = tile_size - tile_overlap
+    h_idx_list = list(range(0, h - tile_size, stride)) + [h - tile_size]
+    w_idx_list = list(range(0, w - tile_size, stride)) + [w - tile_size]
+    result = torch.zeros(
+        b,
+        c,
+        h * scale,
+        w * scale,
+        device=device,
+    ).type_as(img)
+    weights = torch.zeros_like(result)
+    logger.debug("Upscaling %s to %s with tiles", img.shape, result.shape)
+    with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc) as pbar:
+        for h_idx in h_idx_list:
+            if shared.state.interrupted or shared.state.skipped:
+                break
+
+            for w_idx in w_idx_list:
+                if shared.state.interrupted or shared.state.skipped:
+                    break
+
+                in_patch = img[
+                    ...,
+                    h_idx : h_idx + tile_size,
+                    w_idx : w_idx + tile_size,
+                ]
+                out_patch = model(in_patch)
+
+                result[
+                    ...,
+                    h_idx * scale : (h_idx + tile_size) * scale,
+                    w_idx * scale : (w_idx + tile_size) * scale,
+                ].add_(out_patch)
+
+                out_patch_mask = torch.ones_like(out_patch)
+
+                weights[
+                    ...,
+                    h_idx * scale : (h_idx + tile_size) * scale,
+                    w_idx * scale : (w_idx + tile_size) * scale,
+                ].add_(out_patch_mask)
+
+                pbar.update(1)
+
+    output = result.div_(weights)
+
+    return output
-- 
cgit v1.2.3


From d4945f4422e5a0bf31a6dbe4c1aeedd78c09eacb Mon Sep 17 00:00:00 2001
From: Learwin <6223515+Learwin@users.noreply.github.com>
Date: Sun, 31 Dec 2023 13:22:30 +0100
Subject: Removed weight slider for negative prompts

---
 extensions-builtin/Lora/ui_edit_user_metadata.py  | 7 +------
 extensions-builtin/Lora/ui_extra_networks_lora.py | 6 +-----
 2 files changed, 2 insertions(+), 11 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/ui_edit_user_metadata.py b/extensions-builtin/Lora/ui_edit_user_metadata.py
index f7859b21..3160aecf 100644
--- a/extensions-builtin/Lora/ui_edit_user_metadata.py
+++ b/extensions-builtin/Lora/ui_edit_user_metadata.py
@@ -54,14 +54,13 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
         self.slider_preferred_weight = None
         self.edit_notes = None
 
-    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, negative_weight, notes):
+    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes):
         user_metadata = self.get_user_metadata(name)
         user_metadata["description"] = desc
         user_metadata["sd version"] = sd_version
         user_metadata["activation text"] = activation_text
         user_metadata["preferred weight"] = preferred_weight
         user_metadata["negative text"] = negative_text
-        user_metadata["negative weight"] = negative_weight
         user_metadata["notes"] = notes
 
         self.write_user_metadata(name, user_metadata)
@@ -130,7 +129,6 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             user_metadata.get('activation text', ''),
             float(user_metadata.get('preferred weight', 0.0)),
             user_metadata.get('negative text', ''),
-            float(user_metadata.get('negative weight', 0.0)),
             gr.update(visible=True if tags else False),
             gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
         ]
@@ -167,7 +165,6 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
         self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
         self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
         self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts")
-        self.slider_negative_weight = gr.Slider(label='Preferred negative weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
         with gr.Row() as row_random_prompt:
             with gr.Column(scale=8):
                 random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
@@ -204,7 +201,6 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             self.edit_activation_text,
             self.slider_preferred_weight,
             self.edit_negative_text,
-            self.slider_negative_weight,
             row_random_prompt,
             random_prompt,
         ]
@@ -219,7 +215,6 @@ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor)
             self.edit_activation_text,
             self.slider_preferred_weight,
             self.edit_negative_text,
-            self.slider_negative_weight,
             self.edit_notes,
         ]
 
diff --git a/extensions-builtin/Lora/ui_extra_networks_lora.py b/extensions-builtin/Lora/ui_extra_networks_lora.py
index 9a6624e3..e714fac4 100644
--- a/extensions-builtin/Lora/ui_extra_networks_lora.py
+++ b/extensions-builtin/Lora/ui_extra_networks_lora.py
@@ -46,13 +46,9 @@ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
             item["prompt"] += " + " + quote_js(" " + activation_text)
 
         negative_prompt = item["user_metadata"].get("negative text")
-        preferred_negative_weight = item["user_metadata"].get("negative weight")
         item["negative_prompt"] = quote_js("")
         if negative_prompt:
-            neg_prompt = negative_prompt
-            if (preferred_negative_weight > 0):
-                neg_prompt = '(' + negative_prompt + ':' + str(preferred_negative_weight) + ')'
-            item["negative_prompt"] = quote_js(neg_prompt)
+            item["negative_prompt"] = quote_js('(' + negative_prompt + ':1)')
 
         sd_version = item["user_metadata"].get("sd version")
         if sd_version in network.SdVersion.__members__:
-- 
cgit v1.2.3


From d859cec696a953dbfd6f69f7735e68661748d579 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 1 Jan 2024 13:53:12 +0300
Subject: infotext.py: rename usages in the codebase

---
 .../extra-options-section/scripts/extra_options_section.py     |  4 ++--
 modules/api/api.py                                             | 10 +++++-----
 modules/img2img.py                                             |  2 +-
 modules/postprocessing.py                                      |  4 ++--
 modules/processing.py                                          |  4 ++--
 modules/processing_scripts/refiner.py                          |  2 +-
 modules/processing_scripts/seed.py                             |  2 +-
 modules/shared_items.py                                        |  4 ++--
 modules/txt2img.py                                             |  2 +-
 modules/ui.py                                                  |  4 ++--
 modules/ui_common.py                                           |  4 ++--
 modules/ui_extra_networks.py                                   |  2 +-
 modules/ui_extra_networks_user_metadata.py                     |  4 ++--
 modules/ui_postprocessing.py                                   |  2 +-
 14 files changed, 25 insertions(+), 25 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
index ac2c3de4..8aa901fd 100644
--- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py
+++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -1,7 +1,7 @@
 import math
 
 import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings, generation_parameters_copypaste
+from modules import scripts, shared, ui_components, ui_settings, infotext
 from modules.ui_components import FormColumn
 
 
@@ -25,7 +25,7 @@ class ExtraOptionsSection(scripts.Script):
         extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
         elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img")
 
-        mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
+        mapping = {k: v for v, k in infotext.infotext_to_setting_name_mapping}
 
         with gr.Blocks() as interface:
             with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):
diff --git a/modules/api/api.py b/modules/api/api.py
index 843c59b0..0e2807de 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -17,7 +17,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, generation_parameters_copypaste, sd_models
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext, sd_models
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -369,9 +369,9 @@ class Api:
         if not request.infotext:
             return {}
 
-        possible_fields = generation_parameters_copypaste.paste_fields[tabname]["fields"]
+        possible_fields = infotext.paste_fields[tabname]["fields"]
         set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True)  # pydantic v1/v2 have differenrt names for this
-        params = generation_parameters_copypaste.parse_generation_parameters(request.infotext)
+        params = infotext.parse_generation_parameters(request.infotext)
 
         def get_field_value(field, params):
             value = field.function(params) if field.function else params.get(field.label)
@@ -408,7 +408,7 @@ class Api:
         if request.override_settings is None:
             request.override_settings = {}
 
-        overriden_settings = generation_parameters_copypaste.get_override_settings(params)
+        overriden_settings = infotext.get_override_settings(params)
         for _, setting_name, value in overriden_settings:
             if setting_name not in request.override_settings:
                 request.override_settings[setting_name] = value
@@ -584,7 +584,7 @@ class Api:
         if geninfo is None:
             geninfo = ""
 
-        params = generation_parameters_copypaste.parse_generation_parameters(geninfo)
+        params = infotext.parse_generation_parameters(geninfo)
         script_callbacks.infotext_pasted_callback(geninfo, params)
 
         return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
diff --git a/modules/img2img.py b/modules/img2img.py
index c583290a..75b3d346 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -7,7 +7,7 @@ from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageErr
 import gradio as gr
 
 from modules import images as imgutil
-from modules.generation_parameters_copypaste import create_override_settings_dict, parse_generation_parameters
+from modules.infotext import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 from modules.sd_models import get_closet_checkpoint_match
diff --git a/modules/postprocessing.py b/modules/postprocessing.py
index 0c59fad4..f776f7b6 100644
--- a/modules/postprocessing.py
+++ b/modules/postprocessing.py
@@ -2,7 +2,7 @@ import os
 
 from PIL import Image
 
-from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, generation_parameters_copypaste
+from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext
 from modules.shared import opts
 
 
@@ -86,7 +86,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
                 basename = ''
                 forced_filename = None
 
-            infotext = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in pp.info.items() if v is not None])
+            infotext = ", ".join([k if k == v else f'{k}: {infotext.quote(v)}' for k, v in pp.info.items() if v is not None])
 
             if opts.enable_pnginfo:
                 pp.image.info = existing_pnginfo
diff --git a/modules/processing.py b/modules/processing.py
index 7789f9a4..b30df60d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -16,7 +16,7 @@ from skimage import exposure
 from typing import Any
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
 from modules.rng import slerp # noqa: F401
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@@ -733,7 +733,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "User": p.user if opts.add_user_name_to_info else None,
     }
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
+    generation_params_text = ", ".join([k if k == v else f'{k}: {infotext.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
     negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
diff --git a/modules/processing_scripts/refiner.py b/modules/processing_scripts/refiner.py
index cefad32b..e9941413 100644
--- a/modules/processing_scripts/refiner.py
+++ b/modules/processing_scripts/refiner.py
@@ -1,7 +1,7 @@
 import gradio as gr
 
 from modules import scripts, sd_models
-from modules.generation_parameters_copypaste import PasteField
+from modules.infotext import PasteField
 from modules.ui_common import create_refresh_button
 from modules.ui_components import InputAccordion
 
diff --git a/modules/processing_scripts/seed.py b/modules/processing_scripts/seed.py
index a3e16a12..60293278 100644
--- a/modules/processing_scripts/seed.py
+++ b/modules/processing_scripts/seed.py
@@ -3,7 +3,7 @@ import json
 import gradio as gr
 
 from modules import scripts, ui, errors
-from modules.generation_parameters_copypaste import PasteField
+from modules.infotext import PasteField
 from modules.shared import cmd_opts
 from modules.ui_components import ToolButton
 
diff --git a/modules/shared_items.py b/modules/shared_items.py
index 991971ad..e1392472 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -67,14 +67,14 @@ def reload_hypernetworks():
 
 
 def get_infotext_names():
-    from modules import generation_parameters_copypaste, shared
+    from modules import infotext, shared
     res = {}
 
     for info in shared.opts.data_labels.values():
         if info.infotext:
             res[info.infotext] = 1
 
-    for tab_data in generation_parameters_copypaste.paste_fields.values():
+    for tab_data in infotext.paste_fields.values():
         for _, name in tab_data.get("fields") or []:
             if isinstance(name, str):
                 res[name] = 1
diff --git a/modules/txt2img.py b/modules/txt2img.py
index e4e18ceb..3a481915 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -2,7 +2,7 @@ from contextlib import closing
 
 import modules.scripts
 from modules import processing
-from modules.generation_parameters_copypaste import create_override_settings_dict
+from modules.infotext import create_override_settings_dict
 from modules.shared import opts
 import modules.shared as shared
 from modules.ui import plaintext_to_html
diff --git a/modules/ui.py b/modules/ui.py
index 9db2407e..6451e14c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -21,14 +21,14 @@ from modules.ui_gradio_extensions import reload_javascript
 
 from modules.shared import opts, cmd_opts
 
-import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.infotext as parameters_copypaste
 import modules.hypernetworks.ui as hypernetworks_ui
 import modules.textual_inversion.ui as textual_inversion_ui
 import modules.textual_inversion.textual_inversion as textual_inversion
 import modules.shared as shared
 from modules import prompt_parser
 from modules.sd_hijack import model_hijack
-from modules.generation_parameters_copypaste import image_from_url_text, PasteField
+from modules.infotext import image_from_url_text, PasteField
 
 create_setting_component = ui_settings.create_setting_component
 
diff --git a/modules/ui_common.py b/modules/ui_common.py
index 032ec4af..fd32676f 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -8,10 +8,10 @@ import gradio as gr
 import subprocess as sp
 
 from modules import call_queue, shared
-from modules.generation_parameters_copypaste import image_from_url_text
+from modules.infotext import image_from_url_text
 import modules.images
 from modules.ui_components import ToolButton
-import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.infotext as parameters_copypaste
 
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index b8c02241..790af135 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -10,7 +10,7 @@ import json
 import html
 from fastapi.exceptions import HTTPException
 
-from modules.generation_parameters_copypaste import image_from_url_text
+from modules.infotext import image_from_url_text
 from modules.ui_components import ToolButton
 
 extra_pages = []
diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py
index 36a807fc..87aeb6f3 100644
--- a/modules/ui_extra_networks_user_metadata.py
+++ b/modules/ui_extra_networks_user_metadata.py
@@ -5,7 +5,7 @@ import os.path
 
 import gradio as gr
 
-from modules import generation_parameters_copypaste, images, sysinfo, errors, ui_extra_networks
+from modules import infotext, images, sysinfo, errors, ui_extra_networks
 
 
 class UserMetadataEditor:
@@ -181,7 +181,7 @@ class UserMetadataEditor:
         index = len(gallery) - 1 if index >= len(gallery) else index
 
         img_info = gallery[index if index >= 0 else 0]
-        image = generation_parameters_copypaste.image_from_url_text(img_info)
+        image = infotext.image_from_url_text(img_info)
         geninfo, items = images.read_info_from_image(image)
 
         images.save_image_with_geninfo(image, geninfo, item["local_preview"])
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py
index 13d888e4..b74a1532 100644
--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -1,6 +1,6 @@
 import gradio as gr
 from modules import scripts, shared, ui_common, postprocessing, call_queue, ui_toprow
-import modules.generation_parameters_copypaste as parameters_copypaste
+import modules.infotext as parameters_copypaste
 
 
 def create_ui():
-- 
cgit v1.2.3


From 5d7d1823afab0a051a3fbbdb3213bae8051350b7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC1111 <16777216c@gmail.com>
Date: Mon, 1 Jan 2024 17:25:30 +0300
Subject: rename infotext.py again, this time to infotext_utils.py; I didn't
 realize infotext would be used for variable names in multiple places, which
 makes it awkward to import the module; also fix the bug I caused by this
 rename that breaks tests

---
 .../scripts/extra_options_section.py               |   4 +-
 modules/api/api.py                                 |  10 +-
 modules/img2img.py                                 |   2 +-
 modules/infotext.py                                | 502 ---------------------
 modules/infotext_utils.py                          | 502 +++++++++++++++++++++
 modules/postprocessing.py                          |   4 +-
 modules/processing.py                              |   4 +-
 modules/processing_scripts/refiner.py              |   2 +-
 modules/processing_scripts/seed.py                 |   2 +-
 modules/shared_items.py                            |   4 +-
 modules/txt2img.py                                 |   2 +-
 modules/ui.py                                      |   4 +-
 modules/ui_common.py                               |   4 +-
 modules/ui_extra_networks.py                       |   2 +-
 modules/ui_extra_networks_user_metadata.py         |   4 +-
 modules/ui_postprocessing.py                       |   2 +-
 16 files changed, 527 insertions(+), 527 deletions(-)
 delete mode 100644 modules/infotext.py
 create mode 100644 modules/infotext_utils.py

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/extra-options-section/scripts/extra_options_section.py b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
index 8aa901fd..4c10d9c7 100644
--- a/extensions-builtin/extra-options-section/scripts/extra_options_section.py
+++ b/extensions-builtin/extra-options-section/scripts/extra_options_section.py
@@ -1,7 +1,7 @@
 import math
 
 import gradio as gr
-from modules import scripts, shared, ui_components, ui_settings, infotext
+from modules import scripts, shared, ui_components, ui_settings, infotext_utils
 from modules.ui_components import FormColumn
 
 
@@ -25,7 +25,7 @@ class ExtraOptionsSection(scripts.Script):
         extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
         elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img")
 
-        mapping = {k: v for v, k in infotext.infotext_to_setting_name_mapping}
+        mapping = {k: v for v, k in infotext_utils.infotext_to_setting_name_mapping}
 
         with gr.Blocks() as interface:
             with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):
diff --git a/modules/api/api.py b/modules/api/api.py
index 0e2807de..9d1292e9 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -17,7 +17,7 @@ from fastapi.encoders import jsonable_encoder
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext, sd_models
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing, errors, restart, shared_items, script_callbacks, infotext_utils, sd_models
 from modules.api import models
 from modules.shared import opts
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
@@ -369,9 +369,9 @@ class Api:
         if not request.infotext:
             return {}
 
-        possible_fields = infotext.paste_fields[tabname]["fields"]
+        possible_fields = infotext_utils.paste_fields[tabname]["fields"]
         set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True)  # pydantic v1/v2 have differenrt names for this
-        params = infotext.parse_generation_parameters(request.infotext)
+        params = infotext_utils.parse_generation_parameters(request.infotext)
 
         def get_field_value(field, params):
             value = field.function(params) if field.function else params.get(field.label)
@@ -408,7 +408,7 @@ class Api:
         if request.override_settings is None:
             request.override_settings = {}
 
-        overriden_settings = infotext.get_override_settings(params)
+        overriden_settings = infotext_utils.get_override_settings(params)
         for _, setting_name, value in overriden_settings:
             if setting_name not in request.override_settings:
                 request.override_settings[setting_name] = value
@@ -584,7 +584,7 @@ class Api:
         if geninfo is None:
             geninfo = ""
 
-        params = infotext.parse_generation_parameters(geninfo)
+        params = infotext_utils.parse_generation_parameters(geninfo)
         script_callbacks.infotext_pasted_callback(geninfo, params)
 
         return models.PNGInfoResponse(info=geninfo, items=items, parameters=params)
diff --git a/modules/img2img.py b/modules/img2img.py
index e7e8e251..04de8e62 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -7,7 +7,7 @@ from PIL import Image, ImageOps, ImageFilter, ImageEnhance, UnidentifiedImageErr
 import gradio as gr
 
 from modules import images as imgutil
-from modules.infotext import create_override_settings_dict, parse_generation_parameters
+from modules.infotext_utils import create_override_settings_dict, parse_generation_parameters
 from modules.processing import Processed, StableDiffusionProcessingImg2Img, process_images
 from modules.shared import opts, state
 from modules.sd_models import get_closet_checkpoint_match
diff --git a/modules/infotext.py b/modules/infotext.py
deleted file mode 100644
index 26e9b949..00000000
--- a/modules/infotext.py
+++ /dev/null
@@ -1,502 +0,0 @@
-from __future__ import annotations
-import base64
-import io
-import json
-import os
-import re
-import sys
-
-import gradio as gr
-from modules.paths import data_path
-from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions
-from PIL import Image
-
-sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__]  # alias for old name
-
-re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
-re_param = re.compile(re_param_code)
-re_imagesize = re.compile(r"^(\d+)x(\d+)$")
-re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
-type_of_gr_update = type(gr.update())
-
-
-class ParamBinding:
-    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
-        self.paste_button = paste_button
-        self.tabname = tabname
-        self.source_text_component = source_text_component
-        self.source_image_component = source_image_component
-        self.source_tabname = source_tabname
-        self.override_settings_component = override_settings_component
-        self.paste_field_names = paste_field_names or []
-
-
-class PasteField(tuple):
-    def __new__(cls, component, target, *, api=None):
-        return super().__new__(cls, (component, target))
-
-    def __init__(self, component, target, *, api=None):
-        super().__init__()
-
-        self.api = api
-        self.component = component
-        self.label = target if isinstance(target, str) else None
-        self.function = target if callable(target) else None
-
-
-paste_fields: dict[str, dict] = {}
-registered_param_bindings: list[ParamBinding] = []
-
-
-def reset():
-    paste_fields.clear()
-    registered_param_bindings.clear()
-
-
-def quote(text):
-    if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
-        return text
-
-    return json.dumps(text, ensure_ascii=False)
-
-
-def unquote(text):
-    if len(text) == 0 or text[0] != '"' or text[-1] != '"':
-        return text
-
-    try:
-        return json.loads(text)
-    except Exception:
-        return text
-
-
-def image_from_url_text(filedata):
-    if filedata is None:
-        return None
-
-    if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False):
-        filedata = filedata[0]
-
-    if type(filedata) == dict and filedata.get("is_file", False):
-        filename = filedata["name"]
-        is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
-        assert is_in_right_dir, 'trying to open image file outside of allowed directories'
-
-        filename = filename.rsplit('?', 1)[0]
-        return Image.open(filename)
-
-    if type(filedata) == list:
-        if len(filedata) == 0:
-            return None
-
-        filedata = filedata[0]
-
-    if filedata.startswith("data:image/png;base64,"):
-        filedata = filedata[len("data:image/png;base64,"):]
-
-    filedata = base64.decodebytes(filedata.encode('utf-8'))
-    image = Image.open(io.BytesIO(filedata))
-    return image
-
-
-def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
-
-    if fields:
-        for i in range(len(fields)):
-            if not isinstance(fields[i], PasteField):
-                fields[i] = PasteField(*fields[i])
-
-    paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
-
-    # backwards compatibility for existing extensions
-    import modules.ui
-    if tabname == 'txt2img':
-        modules.ui.txt2img_paste_fields = fields
-    elif tabname == 'img2img':
-        modules.ui.img2img_paste_fields = fields
-
-
-def create_buttons(tabs_list):
-    buttons = {}
-    for tab in tabs_list:
-        buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
-    return buttons
-
-
-def bind_buttons(buttons, send_image, send_generate_info):
-    """old function for backwards compatibility; do not use this, use register_paste_params_button"""
-    for tabname, button in buttons.items():
-        source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
-        source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
-
-        register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname))
-
-
-def register_paste_params_button(binding: ParamBinding):
-    registered_param_bindings.append(binding)
-
-
-def connect_paste_params_buttons():
-    for binding in registered_param_bindings:
-        destination_image_component = paste_fields[binding.tabname]["init_img"]
-        fields = paste_fields[binding.tabname]["fields"]
-        override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
-
-        destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
-        destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
-
-        if binding.source_image_component and destination_image_component:
-            if isinstance(binding.source_image_component, gr.Gallery):
-                func = send_image_and_dimensions if destination_width_component else image_from_url_text
-                jsfunc = "extract_image_from_gallery"
-            else:
-                func = send_image_and_dimensions if destination_width_component else lambda x: x
-                jsfunc = None
-
-            binding.paste_button.click(
-                fn=func,
-                _js=jsfunc,
-                inputs=[binding.source_image_component],
-                outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
-                show_progress=False,
-            )
-
-        if binding.source_text_component is not None and fields is not None:
-            connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
-
-        if binding.source_tabname is not None and fields is not None:
-            paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) + binding.paste_field_names
-            binding.paste_button.click(
-                fn=lambda *x: x,
-                inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
-                outputs=[field for field, name in fields if name in paste_field_names],
-                show_progress=False,
-            )
-
-        binding.paste_button.click(
-            fn=None,
-            _js=f"switch_to_{binding.tabname}",
-            inputs=None,
-            outputs=None,
-            show_progress=False,
-        )
-
-
-def send_image_and_dimensions(x):
-    if isinstance(x, Image.Image):
-        img = x
-    else:
-        img = image_from_url_text(x)
-
-    if shared.opts.send_size and isinstance(img, Image.Image):
-        w = img.width
-        h = img.height
-    else:
-        w = gr.update()
-        h = gr.update()
-
-    return img, w, h
-
-
-def restore_old_hires_fix_params(res):
-    """for infotexts that specify old First pass size parameter, convert it into
-    width, height, and hr scale"""
-
-    firstpass_width = res.get('First pass size-1', None)
-    firstpass_height = res.get('First pass size-2', None)
-
-    if shared.opts.use_old_hires_fix_width_height:
-        hires_width = int(res.get("Hires resize-1", 0))
-        hires_height = int(res.get("Hires resize-2", 0))
-
-        if hires_width and hires_height:
-            res['Size-1'] = hires_width
-            res['Size-2'] = hires_height
-            return
-
-    if firstpass_width is None or firstpass_height is None:
-        return
-
-    firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
-    width = int(res.get("Size-1", 512))
-    height = int(res.get("Size-2", 512))
-
-    if firstpass_width == 0 or firstpass_height == 0:
-        firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)
-
-    res['Size-1'] = firstpass_width
-    res['Size-2'] = firstpass_height
-    res['Hires resize-1'] = width
-    res['Hires resize-2'] = height
-
-
-def parse_generation_parameters(x: str):
-    """parses generation parameters string, the one you see in text field under the picture in UI:
-```
-girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate
-Negative prompt: ugly, fat, obese, chubby, (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing
-Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hash: 45dee52b
-```
-
-    returns a dict with field values
-    """
-
-    res = {}
-
-    prompt = ""
-    negative_prompt = ""
-
-    done_with_prompt = False
-
-    *lines, lastline = x.strip().split("\n")
-    if len(re_param.findall(lastline)) < 3:
-        lines.append(lastline)
-        lastline = ''
-
-    for line in lines:
-        line = line.strip()
-        if line.startswith("Negative prompt:"):
-            done_with_prompt = True
-            line = line[16:].strip()
-        if done_with_prompt:
-            negative_prompt += ("" if negative_prompt == "" else "\n") + line
-        else:
-            prompt += ("" if prompt == "" else "\n") + line
-
-    if shared.opts.infotext_styles != "Ignore":
-        found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt)
-
-        if shared.opts.infotext_styles == "Apply":
-            res["Styles array"] = found_styles
-        elif shared.opts.infotext_styles == "Apply if any" and found_styles:
-            res["Styles array"] = found_styles
-
-    res["Prompt"] = prompt
-    res["Negative prompt"] = negative_prompt
-
-    for k, v in re_param.findall(lastline):
-        try:
-            if v[0] == '"' and v[-1] == '"':
-                v = unquote(v)
-
-            m = re_imagesize.match(v)
-            if m is not None:
-                res[f"{k}-1"] = m.group(1)
-                res[f"{k}-2"] = m.group(2)
-            else:
-                res[k] = v
-        except Exception:
-            print(f"Error parsing \"{k}: {v}\"")
-
-    # Missing CLIP skip means it was set to 1 (the default)
-    if "Clip skip" not in res:
-        res["Clip skip"] = "1"
-
-    hypernet = res.get("Hypernet", None)
-    if hypernet is not None:
-        res["Prompt"] += f"""<hypernet:{hypernet}:{res.get("Hypernet strength", "1.0")}>"""
-
-    if "Hires resize-1" not in res:
-        res["Hires resize-1"] = 0
-        res["Hires resize-2"] = 0
-
-    if "Hires sampler" not in res:
-        res["Hires sampler"] = "Use same sampler"
-
-    if "Hires checkpoint" not in res:
-        res["Hires checkpoint"] = "Use same checkpoint"
-
-    if "Hires prompt" not in res:
-        res["Hires prompt"] = ""
-
-    if "Hires negative prompt" not in res:
-        res["Hires negative prompt"] = ""
-
-    restore_old_hires_fix_params(res)
-
-    # Missing RNG means the default was set, which is GPU RNG
-    if "RNG" not in res:
-        res["RNG"] = "GPU"
-
-    if "Schedule type" not in res:
-        res["Schedule type"] = "Automatic"
-
-    if "Schedule max sigma" not in res:
-        res["Schedule max sigma"] = 0
-
-    if "Schedule min sigma" not in res:
-        res["Schedule min sigma"] = 0
-
-    if "Schedule rho" not in res:
-        res["Schedule rho"] = 0
-
-    if "VAE Encoder" not in res:
-        res["VAE Encoder"] = "Full"
-
-    if "VAE Decoder" not in res:
-        res["VAE Decoder"] = "Full"
-
-    if "FP8 weight" not in res:
-        res["FP8 weight"] = "Disable"
-
-    if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
-        res["Cache FP16 weight for LoRA"] = False
-
-    infotext_versions.backcompat(res)
-
-    skip = set(shared.opts.infotext_skip_pasting)
-    res = {k: v for k, v in res.items() if k not in skip}
-
-    return res
-
-
-infotext_to_setting_name_mapping = [
-
-]
-"""Mapping of infotext labels to setting names. Only left for backwards compatibility - use OptionInfo(..., infotext='...') instead.
-Example content:
-
-infotext_to_setting_name_mapping = [
-    ('Conditional mask weight', 'inpainting_mask_weight'),
-    ('Model hash', 'sd_model_checkpoint'),
-    ('ENSD', 'eta_noise_seed_delta'),
-    ('Schedule type', 'k_sched_type'),
-]
-"""
-
-
-def create_override_settings_dict(text_pairs):
-    """creates processing's override_settings parameters from gradio's multiselect
-
-    Example input:
-        ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
-
-    Example output:
-        {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
-    """
-
-    res = {}
-
-    params = {}
-    for pair in text_pairs:
-        k, v = pair.split(":", maxsplit=1)
-
-        params[k] = v.strip()
-
-    mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
-    for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
-        value = params.get(param_name, None)
-
-        if value is None:
-            continue
-
-        res[setting_name] = shared.opts.cast_value(setting_name, value)
-
-    return res
-
-
-def get_override_settings(params, *, skip_fields=None):
-    """Returns a list of settings overrides from the infotext parameters dictionary.
-
-    This function checks the `params` dictionary for any keys that correspond to settings in `shared.opts` and returns
-    a list of tuples containing the parameter name, setting name, and new value cast to correct type.
-
-    It checks for conditions before adding an override:
-    - ignores settings that match the current value
-    - ignores parameter keys present in skip_fields argument.
-
-    Example input:
-        {"Clip skip": "2"}
-
-    Example output:
-        [("Clip skip", "CLIP_stop_at_last_layers", 2)]
-    """
-
-    res = []
-
-    mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
-    for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
-        if param_name in (skip_fields or {}):
-            continue
-
-        v = params.get(param_name, None)
-        if v is None:
-            continue
-
-        if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
-            continue
-
-        v = shared.opts.cast_value(setting_name, v)
-        current_value = getattr(shared.opts, setting_name, None)
-
-        if v == current_value:
-            continue
-
-        res.append((param_name, setting_name, v))
-
-    return res
-
-
-def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
-    def paste_func(prompt):
-        if not prompt and not shared.cmd_opts.hide_ui_dir_config:
-            filename = os.path.join(data_path, "params.txt")
-            if os.path.exists(filename):
-                with open(filename, "r", encoding="utf8") as file:
-                    prompt = file.read()
-
-        params = parse_generation_parameters(prompt)
-        script_callbacks.infotext_pasted_callback(prompt, params)
-        res = []
-
-        for output, key in paste_fields:
-            if callable(key):
-                v = key(params)
-            else:
-                v = params.get(key, None)
-
-            if v is None:
-                res.append(gr.update())
-            elif isinstance(v, type_of_gr_update):
-                res.append(v)
-            else:
-                try:
-                    valtype = type(output.value)
-
-                    if valtype == bool and v == "False":
-                        val = False
-                    else:
-                        val = valtype(v)
-
-                    res.append(gr.update(value=val))
-                except Exception:
-                    res.append(gr.update())
-
-        return res
-
-    if override_settings_component is not None:
-        already_handled_fields = {key: 1 for _, key in paste_fields}
-
-        def paste_settings(params):
-            vals = get_override_settings(params, skip_fields=already_handled_fields)
-
-            vals_pairs = [f"{infotext_text}: {value}" for infotext_text, setting_name, value in vals]
-
-            return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
-
-        paste_fields = paste_fields + [(override_settings_component, paste_settings)]
-
-    button.click(
-        fn=paste_func,
-        inputs=[input_comp],
-        outputs=[x[0] for x in paste_fields],
-        show_progress=False,
-    )
-    button.click(
-        fn=None,
-        _js=f"recalculate_prompts_{tabname}",
-        inputs=[],
-        outputs=[],
-        show_progress=False,
-    )
-
diff --git a/modules/infotext_utils.py b/modules/infotext_utils.py
new file mode 100644
index 00000000..26e9b949
--- /dev/null
+++ b/modules/infotext_utils.py
@@ -0,0 +1,502 @@
+from __future__ import annotations
+import base64
+import io
+import json
+import os
+import re
+import sys
+
+import gradio as gr
+from modules.paths import data_path
+from modules import shared, ui_tempdir, script_callbacks, processing, infotext_versions
+from PIL import Image
+
+sys.modules['modules.generation_parameters_copypaste'] = sys.modules[__name__]  # alias for old name
+
+re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)'
+re_param = re.compile(re_param_code)
+re_imagesize = re.compile(r"^(\d+)x(\d+)$")
+re_hypernet_hash = re.compile("\(([0-9a-f]+)\)$")
+type_of_gr_update = type(gr.update())
+
+
+class ParamBinding:
+    def __init__(self, paste_button, tabname, source_text_component=None, source_image_component=None, source_tabname=None, override_settings_component=None, paste_field_names=None):
+        self.paste_button = paste_button
+        self.tabname = tabname
+        self.source_text_component = source_text_component
+        self.source_image_component = source_image_component
+        self.source_tabname = source_tabname
+        self.override_settings_component = override_settings_component
+        self.paste_field_names = paste_field_names or []
+
+
+class PasteField(tuple):
+    def __new__(cls, component, target, *, api=None):
+        return super().__new__(cls, (component, target))
+
+    def __init__(self, component, target, *, api=None):
+        super().__init__()
+
+        self.api = api
+        self.component = component
+        self.label = target if isinstance(target, str) else None
+        self.function = target if callable(target) else None
+
+
+paste_fields: dict[str, dict] = {}
+registered_param_bindings: list[ParamBinding] = []
+
+
+def reset():
+    paste_fields.clear()
+    registered_param_bindings.clear()
+
+
+def quote(text):
+    if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text):
+        return text
+
+    return json.dumps(text, ensure_ascii=False)
+
+
+def unquote(text):
+    if len(text) == 0 or text[0] != '"' or text[-1] != '"':
+        return text
+
+    try:
+        return json.loads(text)
+    except Exception:
+        return text
+
+
+def image_from_url_text(filedata):
+    if filedata is None:
+        return None
+
+    if type(filedata) == list and filedata and type(filedata[0]) == dict and filedata[0].get("is_file", False):
+        filedata = filedata[0]
+
+    if type(filedata) == dict and filedata.get("is_file", False):
+        filename = filedata["name"]
+        is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
+        assert is_in_right_dir, 'trying to open image file outside of allowed directories'
+
+        filename = filename.rsplit('?', 1)[0]
+        return Image.open(filename)
+
+    if type(filedata) == list:
+        if len(filedata) == 0:
+            return None
+
+        filedata = filedata[0]
+
+    if filedata.startswith("data:image/png;base64,"):
+        filedata = filedata[len("data:image/png;base64,"):]
+
+    filedata = base64.decodebytes(filedata.encode('utf-8'))
+    image = Image.open(io.BytesIO(filedata))
+    return image
+
+
+def add_paste_fields(tabname, init_img, fields, override_settings_component=None):
+
+    if fields:
+        for i in range(len(fields)):
+            if not isinstance(fields[i], PasteField):
+                fields[i] = PasteField(*fields[i])
+
+    paste_fields[tabname] = {"init_img": init_img, "fields": fields, "override_settings_component": override_settings_component}
+
+    # backwards compatibility for existing extensions
+    import modules.ui
+    if tabname == 'txt2img':
+        modules.ui.txt2img_paste_fields = fields
+    elif tabname == 'img2img':
+        modules.ui.img2img_paste_fields = fields
+
+
+def create_buttons(tabs_list):
+    buttons = {}
+    for tab in tabs_list:
+        buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
+    return buttons
+
+
+def bind_buttons(buttons, send_image, send_generate_info):
+    """old function for backwards compatibility; do not use this, use register_paste_params_button"""
+    for tabname, button in buttons.items():
+        source_text_component = send_generate_info if isinstance(send_generate_info, gr.components.Component) else None
+        source_tabname = send_generate_info if isinstance(send_generate_info, str) else None
+
+        register_paste_params_button(ParamBinding(paste_button=button, tabname=tabname, source_text_component=source_text_component, source_image_component=send_image, source_tabname=source_tabname))
+
+
+def register_paste_params_button(binding: ParamBinding):
+    registered_param_bindings.append(binding)
+
+
+def connect_paste_params_buttons():
+    for binding in registered_param_bindings:
+        destination_image_component = paste_fields[binding.tabname]["init_img"]
+        fields = paste_fields[binding.tabname]["fields"]
+        override_settings_component = binding.override_settings_component or paste_fields[binding.tabname]["override_settings_component"]
+
+        destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+        destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+        if binding.source_image_component and destination_image_component:
+            if isinstance(binding.source_image_component, gr.Gallery):
+                func = send_image_and_dimensions if destination_width_component else image_from_url_text
+                jsfunc = "extract_image_from_gallery"
+            else:
+                func = send_image_and_dimensions if destination_width_component else lambda x: x
+                jsfunc = None
+
+            binding.paste_button.click(
+                fn=func,
+                _js=jsfunc,
+                inputs=[binding.source_image_component],
+                outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+                show_progress=False,
+            )
+
+        if binding.source_text_component is not None and fields is not None:
+            connect_paste(binding.paste_button, fields, binding.source_text_component, override_settings_component, binding.tabname)
+
+        if binding.source_tabname is not None and fields is not None:
+            paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) + binding.paste_field_names
+            binding.paste_button.click(
+                fn=lambda *x: x,
+                inputs=[field for field, name in paste_fields[binding.source_tabname]["fields"] if name in paste_field_names],
+                outputs=[field for field, name in fields if name in paste_field_names],
+                show_progress=False,
+            )
+
+        binding.paste_button.click(
+            fn=None,
+            _js=f"switch_to_{binding.tabname}",
+            inputs=None,
+            outputs=None,
+            show_progress=False,
+        )
+
+
+def send_image_and_dimensions(x):
+    if isinstance(x, Image.Image):
+        img = x
+    else:
+        img = image_from_url_text(x)
+
+    if shared.opts.send_size and isinstance(img, Image.Image):
+        w = img.width
+        h = img.height
+    else:
+        w = gr.update()
+        h = gr.update()
+
+    return img, w, h
+
+
+def restore_old_hires_fix_params(res):
+    """for infotexts that specify old First pass size parameter, convert it into
+    width, height, and hr scale"""
+
+    firstpass_width = res.get('First pass size-1', None)
+    firstpass_height = res.get('First pass size-2', None)
+
+    if shared.opts.use_old_hires_fix_width_height:
+        hires_width = int(res.get("Hires resize-1", 0))
+        hires_height = int(res.get("Hires resize-2", 0))
+
+        if hires_width and hires_height:
+            res['Size-1'] = hires_width
+            res['Size-2'] = hires_height
+            return
+
+    if firstpass_width is None or firstpass_height is None:
+        return
+
+    firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
+    width = int(res.get("Size-1", 512))
+    height = int(res.get("Size-2", 512))
+
+    if firstpass_width == 0 or firstpass_height == 0:
+        firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)
+
+    res['Size-1'] = firstpass_width
+    res['Size-2'] = firstpass_height
+    res['Hires resize-1'] = width
+    res['Hires resize-2'] = height
+
+
+def parse_generation_parameters(x: str):
+    """parses generation parameters string, the one you see in text field under the picture in UI:
+```
+girl with an artist's beret, determined, blue eyes, desert scene, computer monitors, heavy makeup, by Alphonse Mucha and Charlie Bowater, ((eyeshadow)), (coquettish), detailed, intricate
+Negative prompt: ugly, fat, obese, chubby, (((deformed))), [blurry], bad anatomy, disfigured, poorly drawn face, mutation, mutated, (extra_limb), (ugly), (poorly drawn hands), messy drawing
+Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model hash: 45dee52b
+```
+
+    returns a dict with field values
+    """
+
+    res = {}
+
+    prompt = ""
+    negative_prompt = ""
+
+    done_with_prompt = False
+
+    *lines, lastline = x.strip().split("\n")
+    if len(re_param.findall(lastline)) < 3:
+        lines.append(lastline)
+        lastline = ''
+
+    for line in lines:
+        line = line.strip()
+        if line.startswith("Negative prompt:"):
+            done_with_prompt = True
+            line = line[16:].strip()
+        if done_with_prompt:
+            negative_prompt += ("" if negative_prompt == "" else "\n") + line
+        else:
+            prompt += ("" if prompt == "" else "\n") + line
+
+    if shared.opts.infotext_styles != "Ignore":
+        found_styles, prompt, negative_prompt = shared.prompt_styles.extract_styles_from_prompt(prompt, negative_prompt)
+
+        if shared.opts.infotext_styles == "Apply":
+            res["Styles array"] = found_styles
+        elif shared.opts.infotext_styles == "Apply if any" and found_styles:
+            res["Styles array"] = found_styles
+
+    res["Prompt"] = prompt
+    res["Negative prompt"] = negative_prompt
+
+    for k, v in re_param.findall(lastline):
+        try:
+            if v[0] == '"' and v[-1] == '"':
+                v = unquote(v)
+
+            m = re_imagesize.match(v)
+            if m is not None:
+                res[f"{k}-1"] = m.group(1)
+                res[f"{k}-2"] = m.group(2)
+            else:
+                res[k] = v
+        except Exception:
+            print(f"Error parsing \"{k}: {v}\"")
+
+    # Missing CLIP skip means it was set to 1 (the default)
+    if "Clip skip" not in res:
+        res["Clip skip"] = "1"
+
+    hypernet = res.get("Hypernet", None)
+    if hypernet is not None:
+        res["Prompt"] += f"""<hypernet:{hypernet}:{res.get("Hypernet strength", "1.0")}>"""
+
+    if "Hires resize-1" not in res:
+        res["Hires resize-1"] = 0
+        res["Hires resize-2"] = 0
+
+    if "Hires sampler" not in res:
+        res["Hires sampler"] = "Use same sampler"
+
+    if "Hires checkpoint" not in res:
+        res["Hires checkpoint"] = "Use same checkpoint"
+
+    if "Hires prompt" not in res:
+        res["Hires prompt"] = ""
+
+    if "Hires negative prompt" not in res:
+        res["Hires negative prompt"] = ""
+
+    restore_old_hires_fix_params(res)
+
+    # Missing RNG means the default was set, which is GPU RNG
+    if "RNG" not in res:
+        res["RNG"] = "GPU"
+
+    if "Schedule type" not in res:
+        res["Schedule type"] = "Automatic"
+
+    if "Schedule max sigma" not in res:
+        res["Schedule max sigma"] = 0
+
+    if "Schedule min sigma" not in res:
+        res["Schedule min sigma"] = 0
+
+    if "Schedule rho" not in res:
+        res["Schedule rho"] = 0
+
+    if "VAE Encoder" not in res:
+        res["VAE Encoder"] = "Full"
+
+    if "VAE Decoder" not in res:
+        res["VAE Decoder"] = "Full"
+
+    if "FP8 weight" not in res:
+        res["FP8 weight"] = "Disable"
+
+    if "Cache FP16 weight for LoRA" not in res and res["FP8 weight"] != "Disable":
+        res["Cache FP16 weight for LoRA"] = False
+
+    infotext_versions.backcompat(res)
+
+    skip = set(shared.opts.infotext_skip_pasting)
+    res = {k: v for k, v in res.items() if k not in skip}
+
+    return res
+
+
+infotext_to_setting_name_mapping = [
+
+]
+"""Mapping of infotext labels to setting names. Only left for backwards compatibility - use OptionInfo(..., infotext='...') instead.
+Example content:
+
+infotext_to_setting_name_mapping = [
+    ('Conditional mask weight', 'inpainting_mask_weight'),
+    ('Model hash', 'sd_model_checkpoint'),
+    ('ENSD', 'eta_noise_seed_delta'),
+    ('Schedule type', 'k_sched_type'),
+]
+"""
+
+
+def create_override_settings_dict(text_pairs):
+    """creates processing's override_settings parameters from gradio's multiselect
+
+    Example input:
+        ['Clip skip: 2', 'Model hash: e6e99610c4', 'ENSD: 31337']
+
+    Example output:
+        {'CLIP_stop_at_last_layers': 2, 'sd_model_checkpoint': 'e6e99610c4', 'eta_noise_seed_delta': 31337}
+    """
+
+    res = {}
+
+    params = {}
+    for pair in text_pairs:
+        k, v = pair.split(":", maxsplit=1)
+
+        params[k] = v.strip()
+
+    mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+    for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
+        value = params.get(param_name, None)
+
+        if value is None:
+            continue
+
+        res[setting_name] = shared.opts.cast_value(setting_name, value)
+
+    return res
+
+
+def get_override_settings(params, *, skip_fields=None):
+    """Returns a list of settings overrides from the infotext parameters dictionary.
+
+    This function checks the `params` dictionary for any keys that correspond to settings in `shared.opts` and returns
+    a list of tuples containing the parameter name, setting name, and new value cast to correct type.
+
+    It checks for conditions before adding an override:
+    - ignores settings that match the current value
+    - ignores parameter keys present in skip_fields argument.
+
+    Example input:
+        {"Clip skip": "2"}
+
+    Example output:
+        [("Clip skip", "CLIP_stop_at_last_layers", 2)]
+    """
+
+    res = []
+
+    mapping = [(info.infotext, k) for k, info in shared.opts.data_labels.items() if info.infotext]
+    for param_name, setting_name in mapping + infotext_to_setting_name_mapping:
+        if param_name in (skip_fields or {}):
+            continue
+
+        v = params.get(param_name, None)
+        if v is None:
+            continue
+
+        if setting_name == "sd_model_checkpoint" and shared.opts.disable_weights_auto_swap:
+            continue
+
+        v = shared.opts.cast_value(setting_name, v)
+        current_value = getattr(shared.opts, setting_name, None)
+
+        if v == current_value:
+            continue
+
+        res.append((param_name, setting_name, v))
+
+    return res
+
+
+def connect_paste(button, paste_fields, input_comp, override_settings_component, tabname):
+    def paste_func(prompt):
+        if not prompt and not shared.cmd_opts.hide_ui_dir_config:
+            filename = os.path.join(data_path, "params.txt")
+            if os.path.exists(filename):
+                with open(filename, "r", encoding="utf8") as file:
+                    prompt = file.read()
+
+        params = parse_generation_parameters(prompt)
+        script_callbacks.infotext_pasted_callback(prompt, params)
+        res = []
+
+        for output, key in paste_fields:
+            if callable(key):
+                v = key(params)
+            else:
+                v = params.get(key, None)
+
+            if v is None:
+                res.append(gr.update())
+            elif isinstance(v, type_of_gr_update):
+                res.append(v)
+            else:
+                try:
+                    valtype = type(output.value)
+
+                    if valtype == bool and v == "False":
+                        val = False
+                    else:
+                        val = valtype(v)
+
+                    res.append(gr.update(value=val))
+                except Exception:
+                    res.append(gr.update())
+
+        return res
+
+    if override_settings_component is not None:
+        already_handled_fields = {key: 1 for _, key in paste_fields}
+
+        def paste_settings(params):
+            vals = get_override_settings(params, skip_fields=already_handled_fields)
+
+            vals_pairs = [f"{infotext_text}: {value}" for infotext_text, setting_name, value in vals]
+
+            return gr.Dropdown.update(value=vals_pairs, choices=vals_pairs, visible=bool(vals_pairs))
+
+        paste_fields = paste_fields + [(override_settings_component, paste_settings)]
+
+    button.click(
+        fn=paste_func,
+        inputs=[input_comp],
+        outputs=[x[0] for x in paste_fields],
+        show_progress=False,
+    )
+    button.click(
+        fn=None,
+        _js=f"recalculate_prompts_{tabname}",
+        inputs=[],
+        outputs=[],
+        show_progress=False,
+    )
+
diff --git a/modules/postprocessing.py b/modules/postprocessing.py
index facea899..7850328f 100644
--- a/modules/postprocessing.py
+++ b/modules/postprocessing.py
@@ -2,7 +2,7 @@ import os
 
 from PIL import Image
 
-from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common
+from modules import shared, images, devices, scripts, scripts_postprocessing, ui_common, infotext_utils
 from modules.shared import opts
 
 
@@ -86,7 +86,7 @@ def run_postprocessing(extras_mode, image, image_folder, input_dir, output_dir,
                 basename = ''
                 forced_filename = None
 
-            infotext = ", ".join([k if k == v else f'{k}: {infotext.quote(v)}' for k, v in pp.info.items() if v is not None])
+            infotext = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in pp.info.items() if v is not None])
 
             if opts.enable_pnginfo:
                 pp.image.info = existing_pnginfo
diff --git a/modules/processing.py b/modules/processing.py
index f55b85ed..213a2879 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -16,7 +16,7 @@ from skimage import exposure
 from typing import Any
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, infotext_utils, extra_networks, sd_vae_approx, scripts, sd_samplers_common, sd_unet, errors, rng
 from modules.rng import slerp # noqa: F401
 from modules.sd_hijack import model_hijack
 from modules.sd_samplers_common import images_tensor_to_samples, decode_first_stage, approximation_indexes
@@ -746,7 +746,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iter
         "User": p.user if opts.add_user_name_to_info else None,
     }
 
-    generation_params_text = ", ".join([k if k == v else f'{k}: {infotext.quote(v)}' for k, v in generation_params.items() if v is not None])
+    generation_params_text = ", ".join([k if k == v else f'{k}: {infotext_utils.quote(v)}' for k, v in generation_params.items() if v is not None])
 
     prompt_text = p.main_prompt if use_main_prompt else all_prompts[index]
     negative_prompt_text = f"\nNegative prompt: {p.main_negative_prompt if use_main_prompt else all_negative_prompts[index]}" if all_negative_prompts[index] else ""
diff --git a/modules/processing_scripts/refiner.py b/modules/processing_scripts/refiner.py
index e9941413..ba33d8a4 100644
--- a/modules/processing_scripts/refiner.py
+++ b/modules/processing_scripts/refiner.py
@@ -1,7 +1,7 @@
 import gradio as gr
 
 from modules import scripts, sd_models
-from modules.infotext import PasteField
+from modules.infotext_utils import PasteField
 from modules.ui_common import create_refresh_button
 from modules.ui_components import InputAccordion
 
diff --git a/modules/processing_scripts/seed.py b/modules/processing_scripts/seed.py
index 60293278..2d3cbb97 100644
--- a/modules/processing_scripts/seed.py
+++ b/modules/processing_scripts/seed.py
@@ -3,7 +3,7 @@ import json
 import gradio as gr
 
 from modules import scripts, ui, errors
-from modules.infotext import PasteField
+from modules.infotext_utils import PasteField
 from modules.shared import cmd_opts
 from modules.ui_components import ToolButton
 
diff --git a/modules/shared_items.py b/modules/shared_items.py
index e1392472..13fb2814 100644
--- a/modules/shared_items.py
+++ b/modules/shared_items.py
@@ -67,14 +67,14 @@ def reload_hypernetworks():
 
 
 def get_infotext_names():
-    from modules import infotext, shared
+    from modules import infotext_utils, shared
     res = {}
 
     for info in shared.opts.data_labels.values():
         if info.infotext:
             res[info.infotext] = 1
 
-    for tab_data in infotext.paste_fields.values():
+    for tab_data in infotext_utils.paste_fields.values():
         for _, name in tab_data.get("fields") or []:
             if isinstance(name, str):
                 res[name] = 1
diff --git a/modules/txt2img.py b/modules/txt2img.py
index 3a481915..49660e89 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -2,7 +2,7 @@ from contextlib import closing
 
 import modules.scripts
 from modules import processing
-from modules.infotext import create_override_settings_dict
+from modules.infotext_utils import create_override_settings_dict
 from modules.shared import opts
 import modules.shared as shared
 from modules.ui import plaintext_to_html
diff --git a/modules/ui.py b/modules/ui.py
index 378529c7..52b15646 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -21,14 +21,14 @@ from modules.ui_gradio_extensions import reload_javascript
 
 from modules.shared import opts, cmd_opts
 
-import modules.infotext as parameters_copypaste
+import modules.infotext_utils as parameters_copypaste
 import modules.hypernetworks.ui as hypernetworks_ui
 import modules.textual_inversion.ui as textual_inversion_ui
 import modules.textual_inversion.textual_inversion as textual_inversion
 import modules.shared as shared
 from modules import prompt_parser
 from modules.sd_hijack import model_hijack
-from modules.infotext import image_from_url_text, PasteField
+from modules.infotext_utils import image_from_url_text, PasteField
 
 create_setting_component = ui_settings.create_setting_component
 
diff --git a/modules/ui_common.py b/modules/ui_common.py
index fd32676f..f48ad426 100644
--- a/modules/ui_common.py
+++ b/modules/ui_common.py
@@ -8,10 +8,10 @@ import gradio as gr
 import subprocess as sp
 
 from modules import call_queue, shared
-from modules.infotext import image_from_url_text
+from modules.infotext_utils import image_from_url_text
 import modules.images
 from modules.ui_components import ToolButton
-import modules.infotext as parameters_copypaste
+import modules.infotext_utils as parameters_copypaste
 
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
diff --git a/modules/ui_extra_networks.py b/modules/ui_extra_networks.py
index 790af135..beea1316 100644
--- a/modules/ui_extra_networks.py
+++ b/modules/ui_extra_networks.py
@@ -10,7 +10,7 @@ import json
 import html
 from fastapi.exceptions import HTTPException
 
-from modules.infotext import image_from_url_text
+from modules.infotext_utils import image_from_url_text
 from modules.ui_components import ToolButton
 
 extra_pages = []
diff --git a/modules/ui_extra_networks_user_metadata.py b/modules/ui_extra_networks_user_metadata.py
index 87aeb6f3..989a649b 100644
--- a/modules/ui_extra_networks_user_metadata.py
+++ b/modules/ui_extra_networks_user_metadata.py
@@ -5,7 +5,7 @@ import os.path
 
 import gradio as gr
 
-from modules import infotext, images, sysinfo, errors, ui_extra_networks
+from modules import infotext_utils, images, sysinfo, errors, ui_extra_networks
 
 
 class UserMetadataEditor:
@@ -181,7 +181,7 @@ class UserMetadataEditor:
         index = len(gallery) - 1 if index >= len(gallery) else index
 
         img_info = gallery[index if index >= 0 else 0]
-        image = infotext.image_from_url_text(img_info)
+        image = infotext_utils.image_from_url_text(img_info)
         geninfo, items = images.read_info_from_image(image)
 
         images.save_image_with_geninfo(image, geninfo, item["local_preview"])
diff --git a/modules/ui_postprocessing.py b/modules/ui_postprocessing.py
index b74a1532..1edb68c5 100644
--- a/modules/ui_postprocessing.py
+++ b/modules/ui_postprocessing.py
@@ -1,6 +1,6 @@
 import gradio as gr
 from modules import scripts, shared, ui_common, postprocessing, call_queue, ui_toprow
-import modules.infotext as parameters_copypaste
+import modules.infotext_utils as parameters_copypaste
 
 
 def create_ui():
-- 
cgit v1.2.3


From cf14a6a7aaf8ccb40552990785d5c9e400d93610 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Sun, 31 Dec 2023 16:11:18 +0200
Subject: Refactor upscale_2 helper out of ScuNET/SwinIR; make sure devices are
 right

---
 extensions-builtin/ScuNET/scripts/scunet_model.py | 48 +++---------
 extensions-builtin/SwinIR/scripts/swinir_model.py | 62 ++--------------
 modules/upscaler_utils.py                         | 89 ++++++++++++++++++-----
 3 files changed, 87 insertions(+), 112 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/ScuNET/scripts/scunet_model.py b/extensions-builtin/ScuNET/scripts/scunet_model.py
index f799cb76..fe5e5a19 100644
--- a/extensions-builtin/ScuNET/scripts/scunet_model.py
+++ b/extensions-builtin/ScuNET/scripts/scunet_model.py
@@ -1,13 +1,9 @@
 import sys
 
 import PIL.Image
-import numpy as np
-import torch
 
 import modules.upscaler
-from modules import devices, modelloader, script_callbacks, errors
-from modules.shared import opts
-from modules.upscaler_utils import tiled_upscale_2
+from modules import devices, errors, modelloader, script_callbacks, shared, upscaler_utils
 
 
 class UpscalerScuNET(modules.upscaler.Upscaler):
@@ -40,46 +36,23 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         self.scalers = scalers
 
     def do_upscale(self, img: PIL.Image.Image, selected_file):
-
         devices.torch_gc()
-
         try:
             model = self.load_model(selected_file)
         except Exception as e:
             print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
             return img
 
-        device = devices.get_device_for('scunet')
-        tile = opts.SCUNET_tile
-        h, w = img.height, img.width
-        np_img = np.array(img)
-        np_img = np_img[:, :, ::-1]  # RGB to BGR
-        np_img = np_img.transpose((2, 0, 1)) / 255  # HWC to CHW
-        torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device)  # type: ignore
-
-        if tile > h or tile > w:
-            _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
-            _img[:, :, :h, :w] = torch_img # pad image
-            torch_img = _img
-
-        with torch.no_grad():
-            torch_output = tiled_upscale_2(
-                torch_img,
-                model,
-                tile_size=opts.SCUNET_tile,
-                tile_overlap=opts.SCUNET_tile_overlap,
-                scale=1,
-                device=devices.get_device_for('scunet'),
-                desc="ScuNET tiles",
-            ).squeeze(0)
-        torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
-        np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
-        del torch_img, torch_output
+        img = upscaler_utils.upscale_2(
+            img,
+            model,
+            tile_size=shared.opts.SCUNET_tile,
+            tile_overlap=shared.opts.SCUNET_tile_overlap,
+            scale=1,  # ScuNET is a denoising model, not an upscaler
+            desc='ScuNET',
+        )
         devices.torch_gc()
-
-        output = np_output.transpose((1, 2, 0))  # CHW to HWC
-        output = output[:, :, ::-1]  # BGR to RGB
-        return PIL.Image.fromarray((output * 255).astype(np.uint8))
+        return img
 
     def load_model(self, path: str):
         device = devices.get_device_for('scunet')
@@ -93,7 +66,6 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
 
 def on_ui_settings():
     import gradio as gr
-    from modules import shared
 
     shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
     shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index 8a555c79..bc427fea 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -1,14 +1,10 @@
 import logging
 import sys
 
-import numpy as np
-import torch
 from PIL import Image
 
-from modules import modelloader, devices, script_callbacks, shared
-from modules.shared import opts
+from modules import devices, modelloader, script_callbacks, shared, upscaler_utils
 from modules.upscaler import Upscaler, UpscalerData
-from modules.upscaler_utils import tiled_upscale_2
 
 SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
 
@@ -36,9 +32,7 @@ class UpscalerSwinIR(Upscaler):
         self.scalers = scalers
 
     def do_upscale(self, img: Image.Image, model_file: str) -> Image.Image:
-        current_config = (model_file, opts.SWIN_tile)
-
-        device = self._get_device()
+        current_config = (model_file, shared.opts.SWIN_tile)
 
         if self._cached_model_config == current_config:
             model = self._cached_model
@@ -51,12 +45,13 @@ class UpscalerSwinIR(Upscaler):
             self._cached_model = model
             self._cached_model_config = current_config
 
-        img = upscale(
+        img = upscaler_utils.upscale_2(
             img,
             model,
-            tile=opts.SWIN_tile,
-            tile_overlap=opts.SWIN_tile_overlap,
-            device=device,
+            tile_size=shared.opts.SWIN_tile,
+            tile_overlap=shared.opts.SWIN_tile_overlap,
+            scale=4,  # TODO: This was hard-coded before too...
+            desc="SwinIR",
         )
         devices.torch_gc()
         return img
@@ -77,7 +72,7 @@ class UpscalerSwinIR(Upscaler):
             dtype=devices.dtype,
             expected_architecture="SwinIR",
         )
-        if getattr(opts, 'SWIN_torch_compile', False):
+        if getattr(shared.opts, 'SWIN_torch_compile', False):
             try:
                 model_descriptor.model.compile()
             except Exception:
@@ -88,47 +83,6 @@ class UpscalerSwinIR(Upscaler):
         return devices.get_device_for('swinir')
 
 
-def upscale(
-    img,
-    model,
-    *,
-    tile: int,
-    tile_overlap: int,
-    window_size=8,
-    scale=4,
-    device,
-):
-
-    img = np.array(img)
-    img = img[:, :, ::-1]
-    img = np.moveaxis(img, 2, 0) / 255
-    img = torch.from_numpy(img).float()
-    img = img.unsqueeze(0).to(device, dtype=devices.dtype)
-    with torch.no_grad(), devices.autocast():
-        _, _, h_old, w_old = img.size()
-        h_pad = (h_old // window_size + 1) * window_size - h_old
-        w_pad = (w_old // window_size + 1) * window_size - w_old
-        img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
-        img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
-        output = tiled_upscale_2(
-            img,
-            model,
-            tile_size=tile,
-            tile_overlap=tile_overlap,
-            scale=scale,
-            device=device,
-            desc="SwinIR tiles",
-        )
-        output = output[..., : h_old * scale, : w_old * scale]
-        output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
-        if output.ndim == 3:
-            output = np.transpose(
-                output[[2, 1, 0], :, :], (1, 2, 0)
-            )  # CHW-RGB to HCW-BGR
-        output = (output * 255.0).round().astype(np.uint8)  # float32 to uint8
-        return Image.fromarray(output, "RGB")
-
-
 def on_ui_settings():
     import gradio as gr
 
diff --git a/modules/upscaler_utils.py b/modules/upscaler_utils.py
index 9379f512..e4c63f09 100644
--- a/modules/upscaler_utils.py
+++ b/modules/upscaler_utils.py
@@ -11,23 +11,40 @@ from modules import images, shared, torch_utils
 logger = logging.getLogger(__name__)
 
 
-def upscale_without_tiling(model, img: Image.Image):
-    img = np.array(img)
-    img = img[:, :, ::-1]
-    img = np.ascontiguousarray(np.transpose(img, (2, 0, 1))) / 255
-    img = torch.from_numpy(img).float()
-
+def pil_image_to_torch_bgr(img: Image.Image) -> torch.Tensor:
+    img = np.array(img.convert("RGB"))
+    img = img[:, :, ::-1]  # flip RGB to BGR
+    img = np.transpose(img, (2, 0, 1))  # HWC to CHW
+    img = np.ascontiguousarray(img) / 255  # Rescale to [0, 1]
+    return torch.from_numpy(img)
+
+
+def torch_bgr_to_pil_image(tensor: torch.Tensor) -> Image.Image:
+    if tensor.ndim == 4:
+        # If we're given a tensor with a batch dimension, squeeze it out
+        # (but only if it's a batch of size 1).
+        if tensor.shape[0] != 1:
+            raise ValueError(f"{tensor.shape} does not describe a BCHW tensor")
+        tensor = tensor.squeeze(0)
+    assert tensor.ndim == 3, f"{tensor.shape} does not describe a CHW tensor"
+    # TODO: is `tensor.float().cpu()...numpy()` the most efficient idiom?
+    arr = tensor.float().cpu().clamp_(0, 1).numpy()  # clamp
+    arr = 255.0 * np.moveaxis(arr, 0, 2)  # CHW to HWC, rescale
+    arr = arr.astype(np.uint8)
+    arr = arr[:, :, ::-1]  # flip BGR to RGB
+    return Image.fromarray(arr, "RGB")
+
+
+def upscale_pil_patch(model, img: Image.Image) -> Image.Image:
+    """
+    Upscale a given PIL image using the given model.
+    """
     param = torch_utils.get_param(model)
-    img = img.unsqueeze(0).to(device=param.device, dtype=param.dtype)
 
     with torch.no_grad():
-        output = model(img)
-
-    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
-    output = 255. * np.moveaxis(output, 0, 2)
-    output = output.astype(np.uint8)
-    output = output[:, :, ::-1]
-    return Image.fromarray(output, 'RGB')
+        tensor = pil_image_to_torch_bgr(img).unsqueeze(0)  # add batch dimension
+        tensor = tensor.to(device=param.device, dtype=param.dtype)
+        return torch_bgr_to_pil_image(model(tensor))
 
 
 def upscale_with_model(
@@ -40,7 +57,7 @@ def upscale_with_model(
 ) -> Image.Image:
     if tile_size <= 0:
         logger.debug("Upscaling %s without tiling", img)
-        output = upscale_without_tiling(model, img)
+        output = upscale_pil_patch(model, img)
         logger.debug("=> %s", output)
         return output
 
@@ -52,7 +69,7 @@ def upscale_with_model(
             newrow = []
             for x, w, tile in row:
                 logger.debug("Tile (%d, %d) %s...", x, y, tile)
-                output = upscale_without_tiling(model, tile)
+                output = upscale_pil_patch(model, tile)
                 scale_factor = output.width // tile.width
                 logger.debug("=> %s (scale factor %s)", output, scale_factor)
                 newrow.append([x * scale_factor, w * scale_factor, output])
@@ -71,19 +88,22 @@ def upscale_with_model(
 
 
 def tiled_upscale_2(
-    img,
+    img: torch.Tensor,
     model,
     *,
     tile_size: int,
     tile_overlap: int,
     scale: int,
-    device,
     desc="Tiled upscale",
 ):
     # Alternative implementation of `upscale_with_model` originally used by
     # SwinIR and ScuNET.  It differs from `upscale_with_model` in that tiling and
     # weighting is done in PyTorch space, as opposed to `images.Grid` doing it in
     # Pillow space without weighting.
+
+    # Grab the device the model is on, and use it.
+    device = torch_utils.get_param(model).device
+
     b, c, h, w = img.size()
     tile_size = min(tile_size, h, w)
 
@@ -100,7 +120,8 @@ def tiled_upscale_2(
         h * scale,
         w * scale,
         device=device,
-    ).type_as(img)
+        dtype=img.dtype,
+    )
     weights = torch.zeros_like(result)
     logger.debug("Upscaling %s to %s with tiles", img.shape, result.shape)
     with tqdm.tqdm(total=len(h_idx_list) * len(w_idx_list), desc=desc, disable=not shared.opts.enable_upscale_progressbar) as pbar:
@@ -112,11 +133,13 @@ def tiled_upscale_2(
                 if shared.state.interrupted or shared.state.skipped:
                     break
 
+                # Only move this patch to the device if it's not already there.
                 in_patch = img[
                     ...,
                     h_idx : h_idx + tile_size,
                     w_idx : w_idx + tile_size,
-                ]
+                ].to(device=device)
+
                 out_patch = model(in_patch)
 
                 result[
@@ -138,3 +161,29 @@ def tiled_upscale_2(
     output = result.div_(weights)
 
     return output
+
+
+def upscale_2(
+    img: Image.Image,
+    model,
+    *,
+    tile_size: int,
+    tile_overlap: int,
+    scale: int,
+    desc: str,
+):
+    """
+    Convenience wrapper around `tiled_upscale_2` that handles PIL images.
+    """
+    tensor = pil_image_to_torch_bgr(img).float().unsqueeze(0)  # add batch dimension
+
+    with torch.no_grad():
+        output = tiled_upscale_2(
+            tensor,
+            model,
+            tile_size=tile_size,
+            tile_overlap=tile_overlap,
+            scale=scale,
+            desc=desc,
+        )
+    return torch_bgr_to_pil_image(output)
-- 
cgit v1.2.3


From dfdc51246c678b585e1bdfdb7d2f202b0ca0e362 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Wed, 3 Jan 2024 22:38:13 +0200
Subject: SwinIR: use prefer_half

---
 extensions-builtin/SwinIR/scripts/swinir_model.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index bc427fea..6a8e21b0 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -1,6 +1,7 @@
 import logging
 import sys
 
+import torch
 from PIL import Image
 
 from modules import devices, modelloader, script_callbacks, shared, upscaler_utils
@@ -69,7 +70,7 @@ class UpscalerSwinIR(Upscaler):
         model_descriptor = modelloader.load_spandrel_model(
             filename,
             device=self._get_device(),
-            dtype=devices.dtype,
+            prefer_half=(devices.dtype == torch.float16),
             expected_architecture="SwinIR",
         )
         if getattr(shared.opts, 'SWIN_torch_compile', False):
-- 
cgit v1.2.3


From 3d31d5c27beb433fa37b30f135ec06a278a87630 Mon Sep 17 00:00:00 2001
From: Aarni Koskela <akx@iki.fi>
Date: Wed, 3 Jan 2024 22:38:49 +0200
Subject: SwinIR: pass model.scale

---
 extensions-builtin/SwinIR/scripts/swinir_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/SwinIR/scripts/swinir_model.py b/extensions-builtin/SwinIR/scripts/swinir_model.py
index 6a8e21b0..16bf9b79 100644
--- a/extensions-builtin/SwinIR/scripts/swinir_model.py
+++ b/extensions-builtin/SwinIR/scripts/swinir_model.py
@@ -51,7 +51,7 @@ class UpscalerSwinIR(Upscaler):
             model,
             tile_size=shared.opts.SWIN_tile,
             tile_overlap=shared.opts.SWIN_tile_overlap,
-            scale=4,  # TODO: This was hard-coded before too...
+            scale=model.scale,
             desc="SwinIR",
         )
         devices.torch_gc()
-- 
cgit v1.2.3


From f8f38c7c28e48f9f79225c969e3e82b1adcfb910 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Fri, 5 Jan 2024 16:31:48 +0800
Subject: Fix dtype casting for OFT module

---
 extensions-builtin/Lora/network_oft.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index fa647020..342fcd0d 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -56,7 +56,7 @@ class NetworkModuleOFT(network.NetworkModule):
             self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
 
     def calc_updown(self, orig_weight):
-        oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        oft_blocks = self.oft_blocks.to(orig_weight.device)
         eye = torch.eye(self.block_size, device=self.oft_blocks.device)
 
         if self.is_kohya:
@@ -66,7 +66,7 @@ class NetworkModuleOFT(network.NetworkModule):
             block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
             oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
 
-        R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
+        R = oft_blocks.to(orig_weight.device)
 
         # This errors out for MultiheadAttention, might need to be handled up-stream
         merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
@@ -77,6 +77,6 @@ class NetworkModuleOFT(network.NetworkModule):
         )
         merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
 
-        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
+        updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
         output_shape = orig_weight.shape
         return self.finalize_updown(updown, orig_weight, output_shape)
-- 
cgit v1.2.3


From 18ca987c92f52690daec43a6c67363c341bb6008 Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Fri, 5 Jan 2024 16:32:19 +0800
Subject: Add general forward method for all modules.

---
 extensions-builtin/Lora/network.py  | 34 +++++++++++++++++++++++++++++++++-
 extensions-builtin/Lora/networks.py | 12 ++++++------
 2 files changed, 39 insertions(+), 7 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py
index a62e5eff..f9b571b5 100644
--- a/extensions-builtin/Lora/network.py
+++ b/extensions-builtin/Lora/network.py
@@ -3,6 +3,10 @@ import os
 from collections import namedtuple
 import enum
 
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
 from modules import sd_models, cache, errors, hashes, shared
 
 NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
@@ -115,6 +119,29 @@ class NetworkModule:
         if hasattr(self.sd_module, 'weight'):
             self.shape = self.sd_module.weight.shape
 
+        self.ops = None
+        self.extra_kwargs = {}
+        if isinstance(self.sd_module, nn.Conv2d):
+            self.ops = F.conv2d
+            self.extra_kwargs = {
+                'stride': self.sd_module.stride, 
+                'padding': self.sd_module.padding
+            }
+        elif isinstance(self.sd_module, nn.Linear):
+            self.ops = F.linear
+        elif isinstance(self.sd_module, nn.LayerNorm):
+            self.ops = F.layer_norm
+            self.extra_kwargs = {
+                'normalized_shape': self.sd_module.normalized_shape,
+                'eps': self.sd_module.eps
+            }
+        elif isinstance(self.sd_module, nn.GroupNorm):
+            self.ops = F.group_norm
+            self.extra_kwargs = {
+                'num_groups': self.sd_module.num_groups,
+                'eps': self.sd_module.eps
+            }
+
         self.dim = None
         self.bias = weights.w.get("bias")
         self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
@@ -155,5 +182,10 @@ class NetworkModule:
         raise NotImplementedError()
 
     def forward(self, x, y):
-        raise NotImplementedError()
+        """A general forward implementation for all modules"""
+        if self.ops is None:
+            raise NotImplementedError()
+        else:
+            updown, ex_bias = self.calc_updown(self.sd_module.weight)
+            return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs)
 
diff --git a/extensions-builtin/Lora/networks.py b/extensions-builtin/Lora/networks.py
index 72ebd624..32e10b62 100644
--- a/extensions-builtin/Lora/networks.py
+++ b/extensions-builtin/Lora/networks.py
@@ -458,23 +458,23 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
         self.network_current_names = wanted_names
 
 
-def network_forward(module, input, original_forward):
+def network_forward(org_module, input, original_forward):
     """
     Old way of applying Lora by executing operations during layer's forward.
     Stacking many loras this way results in big performance degradation.
     """
 
     if len(loaded_networks) == 0:
-        return original_forward(module, input)
+        return original_forward(org_module, input)
 
     input = devices.cond_cast_unet(input)
 
-    network_restore_weights_from_backup(module)
-    network_reset_cached_weight(module)
+    network_restore_weights_from_backup(org_module)
+    network_reset_cached_weight(org_module)
 
-    y = original_forward(module, input)
+    y = original_forward(org_module, input)
 
-    network_layer_name = getattr(module, 'network_layer_name', None)
+    network_layer_name = getattr(org_module, 'network_layer_name', None)
     for lora in loaded_networks:
         module = lora.modules.get(network_layer_name, None)
         if module is None:
-- 
cgit v1.2.3


From 44744d6005da5e424267698ee3279caa597dfebc Mon Sep 17 00:00:00 2001
From: Kohaku-Blueleaf <59680068+KohakuBlueleaf@users.noreply.github.com>
Date: Fri, 5 Jan 2024 16:38:05 +0800
Subject: linting

---
 extensions-builtin/Lora/network.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

(limited to 'extensions-builtin')

diff --git a/extensions-builtin/Lora/network.py b/extensions-builtin/Lora/network.py
index f9b571b5..b8fd9194 100644
--- a/extensions-builtin/Lora/network.py
+++ b/extensions-builtin/Lora/network.py
@@ -3,7 +3,6 @@ import os
 from collections import namedtuple
 import enum
 
-import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
@@ -124,7 +123,7 @@ class NetworkModule:
         if isinstance(self.sd_module, nn.Conv2d):
             self.ops = F.conv2d
             self.extra_kwargs = {
-                'stride': self.sd_module.stride, 
+                'stride': self.sd_module.stride,
                 'padding': self.sd_module.padding
             }
         elif isinstance(self.sd_module, nn.Linear):
-- 
cgit v1.2.3