added highres fix feature

5 files changed, 121 insertions, 38 deletions

diff --git a/javascript/hints.js b/javascript/hints.js
index 20a71dbd..65a67d50 100644
--- a/javascript/hints.js
+++ b/javascript/hints.js
@@ -72,6 +72,10 @@ titles = {
     "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
 
     "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
+
+    "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+    "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
+
 }
 
 
diff --git a/modules/processing.py b/modules/processing.py
index a0f0e575..e146524d 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -74,11 +74,12 @@ class StableDiffusionProcessing:
         self.overlay_images = overlay_images

         self.paste_to = None

         self.color_corrections = None

+        self.denoising_strength: float = 0

 

-    def init(self, seed):

+    def init(self, all_prompts, all_seeds, all_subseeds):

         pass

 

-    def sample(self, x, conditioning, unconditional_conditioning):

+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

         raise NotImplementedError()

 

 

@@ -303,7 +304,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext

     ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)

     with torch.no_grad(), precision_scope("cuda"), ema_scope():

-        p.init(seed=all_seeds[0])

+        p.init(all_prompts, all_seeds, all_subseeds)

 

         if state.job_count == -1:

             state.job_count = p.n_iter

@@ -328,13 +329,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 for comment in model_hijack.comments:

                     comments[comment] = 1

 

-            # we manually generate all input noises because each one should have a specific seed

-            x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)

-

             if p.n_iter > 1:

                 shared.state.job = f"Batch {n+1} out of {p.n_iter}"

 

-            samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc)

+            samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)

             if state.interrupted:

 

                 # if we are interruped, sample returns just noise

@@ -406,13 +404,64 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 

 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):

     sampler = None

+    firstphase_width = 0

+    firstphase_height = 0

+    firstphase_width_truncated = 0

+    firstphase_height_truncated = 0

+

+    def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs):

+        super().__init__(**kwargs)

+        self.enable_hr = enable_hr

+        self.scale_latent = scale_latent

+        self.denoising_strength = denoising_strength

+

+    def init(self, all_prompts, all_seeds, all_subseeds):

+        if self.enable_hr:

+            if state.job_count == -1:

+                state.job_count = self.n_iter * 2

+            else:

+                state.job_count = state.job_count * 2

+

+            desired_pixel_count = 512 * 512

+            actual_pixel_count = self.width * self.height

+            scale = math.sqrt(desired_pixel_count / actual_pixel_count)

+

+            self.firstphase_width = math.ceil(scale * self.width / 64) * 64

+            self.firstphase_height = math.ceil(scale * self.height / 64) * 64

+            self.firstphase_width_truncated = int(scale * self.width)

+            self.firstphase_height_truncated = int(scale * self.height)

+

+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

+        self.sampler = samplers[self.sampler_index].constructor(self.sd_model)

+

+        if not self.enable_hr:

+            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

+            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

+            return samples

+

+        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

+

+        truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f

+        truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f

+

+        samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]

+

+        if self.scale_latent:

+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")

+        else:

+            decoded_samples = self.sd_model.decode_first_stage(samples)

+            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")

+            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))

+

+        shared.state.nextjob()

 

-    def init(self, seed):

         self.sampler = samplers[self.sampler_index].constructor(self.sd_model)

+        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)

+

+        return samples

 

-    def sample(self, x, conditioning, unconditional_conditioning):

-        samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning)

-        return samples_ddim

 

 class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):

     sampler = None

@@ -435,7 +484,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         self.mask = None

         self.nmask = None

 

-    def init(self, seed):

+    def init(self, all_prompts, all_seeds, all_subseeds):

         self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)

         crop_region = None

 

@@ -529,12 +578,15 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)

             self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)

 

+            # this needs to be fixed to be done in sample() using actual seeds for batches

             if self.inpainting_fill == 2:

-                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], [seed + x + 1 for x in range(self.init_latent.shape[0])]) * self.nmask

+                self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask

             elif self.inpainting_fill == 3:

                 self.init_latent = self.init_latent * self.mask

 

-    def sample(self, x, conditioning, unconditional_conditioning):

+    def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

+        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)

+

         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)

 

         if self.mask is not None:

diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index af6811a1..1fc9d18c 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -38,9 +38,9 @@ samplers = [
 samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']

 

 

-def setup_img2img_steps(p):

-    if opts.img2img_fix_steps:

-        steps = int(p.steps / min(p.denoising_strength, 0.999))

+def setup_img2img_steps(p, steps=None):

+    if opts.img2img_fix_steps or steps is not None:

+        steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0

         t_enc = p.steps - 1

     else:

         steps = p.steps

@@ -115,8 +115,8 @@ class VanillaStableDiffusionSampler:
         self.step += 1

         return res

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):

-        steps, t_enc = setup_img2img_steps(p)

+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):

+        steps, t_enc = setup_img2img_steps(p, steps)

 

         # existing code fails with cetain step counts, like 9

         try:

@@ -127,16 +127,16 @@ class VanillaStableDiffusionSampler:
         x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)

 

         self.sampler.p_sample_ddim = self.p_sample_ddim_hook

-        self.mask = p.mask

-        self.nmask = p.nmask

-        self.init_latent = p.init_latent

+        self.mask = p.mask if hasattr(p, 'mask') else None

+        self.nmask = p.nmask if hasattr(p, 'nmask') else None

+        self.init_latent = x

         self.step = 0

 

         samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)

 

         return samples

 

-    def sample(self, p, x, conditioning, unconditional_conditioning):

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

         for fieldname in ['p_sample_ddim', 'p_sample_plms']:

             if hasattr(self.sampler, fieldname):

                 setattr(self.sampler, fieldname, self.p_sample_ddim_hook)

@@ -145,11 +145,13 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None

         self.step = 0

 

+        steps = steps or p.steps

+

         # existing code fails with cetin step counts, like 9

         try:

-            samples_ddim, _ = self.sampler.sample(S=p.steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)

+            samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)

         except Exception:

-            samples_ddim, _ = self.sampler.sample(S=p.steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)

+            samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x)

 

         return samples_ddim

 

@@ -186,7 +188,7 @@ class CFGDenoiser(torch.nn.Module):
         return denoised

 

 

-def extended_trange(count, *args, **kwargs):

+def extended_trange(sampler, count, *args, **kwargs):

     state.sampling_steps = count

     state.sampling_step = 0

 

@@ -194,6 +196,9 @@ def extended_trange(count, *args, **kwargs):
         if state.interrupted:

             break

 

+        if sampler.stop_at is not None and x > sampler.stop_at:

+            break

+

         yield x

 

         state.sampling_step += 1

@@ -222,6 +227,7 @@ class KDiffusionSampler:
         self.model_wrap_cfg = CFGDenoiser(self.model_wrap)

         self.sampler_noises = None

         self.sampler_noise_index = 0

+        self.stop_at = None

 

     def callback_state(self, d):

         store_latent(d["denoised"])

@@ -240,8 +246,8 @@ class KDiffusionSampler:
         self.sampler_noise_index += 1

         return res

 

-    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning):

-        steps, t_enc = setup_img2img_steps(p)

+    def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):

+        steps, t_enc = setup_img2img_steps(p, steps)

 

         sigmas = self.model_wrap.get_sigmas(steps)

 

@@ -251,33 +257,36 @@ class KDiffusionSampler:
 

         sigma_sched = sigmas[steps - t_enc - 1:]

 

-        self.model_wrap_cfg.mask = p.mask

-        self.model_wrap_cfg.nmask = p.nmask

-        self.model_wrap_cfg.init_latent = p.init_latent

+        self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None

+        self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None

+        self.model_wrap_cfg.init_latent = x

         self.model_wrap.step = 0

         self.sampler_noise_index = 0

 

         if hasattr(k_diffusion.sampling, 'trange'):

-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)

+            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)

 

         if self.sampler_noises is not None:

             k_diffusion.sampling.torch = TorchHijack(self)

 

         return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)

 

-    def sample(self, p, x, conditioning, unconditional_conditioning):

-        sigmas = self.model_wrap.get_sigmas(p.steps)

+    def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):

+        steps = steps or p.steps

+

+        sigmas = self.model_wrap.get_sigmas(steps)

         x = x * sigmas[0]

 

         self.model_wrap_cfg.step = 0

         self.sampler_noise_index = 0

 

         if hasattr(k_diffusion.sampling, 'trange'):

-            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs)

+            k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)

 

         if self.sampler_noises is not None:

             k_diffusion.sampling.torch = TorchHijack(self)

 

-        samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)

-        return samples_ddim

+        samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state)

+

+        return samples

 

diff --git a/modules/txt2img.py b/modules/txt2img.py
index 30d89849..7c6de2e7 100644
--- a/modules/txt2img.py
+++ b/modules/txt2img.py
@@ -6,7 +6,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html

 

 

-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, *args):

+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args):

     p = StableDiffusionProcessingTxt2Img(

         sd_model=shared.sd_model,

         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,

@@ -28,6 +28,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         height=height,

         restore_faces=restore_faces,

         tiling=tiling,

+        enable_hr=enable_hr,

+        scale_latent=scale_latent,

+        denoising_strength=denoising_strength,

     )

 

     print(f"\ntxt2img: {prompt}", file=shared.progress_print_out)

diff --git a/modules/ui.py b/modules/ui.py
index 01f07954..7422a269 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -327,6 +327,7 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
         outputs=[seed, dummy_component]

     )

 

+

 def create_toprow(is_img2img):

     with gr.Row(elem_id="toprow"):

         with gr.Column(scale=4):

@@ -392,6 +393,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                 with gr.Row():

                     restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)

                     tiling = gr.Checkbox(label='Tiling', value=False)

+                    enable_hr = gr.Checkbox(label='Highres. fix', value=False)

+

+                with gr.Row(visible=False) as hr_options:

+                    scale_latent = gr.Checkbox(label='Scale latent', value=True)

+                    denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)

 

                 with gr.Row():

                     batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)

@@ -451,6 +457,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
                     subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w,

                     height,

                     width,

+                    enable_hr,

+                    scale_latent,

+                    denoising_strength,

                 ] + custom_inputs,

                 outputs=[

                     txt2img_gallery,

@@ -463,6 +472,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo):
             txt2img_prompt.submit(**txt2img_args)

             submit.click(**txt2img_args)

 

+            enable_hr.change(

+                fn=lambda x: gr_show(x),

+                inputs=[enable_hr],

+                outputs=[hr_options],

+            )

+

             interrupt.click(

                 fn=lambda: shared.state.interrupt(),

                 inputs=[],