From 5391c0bf3efe33425341399019f2f39eea4395a9 Mon Sep 17 00:00:00 2001
From: C43H66N12O12S2 <36072735+C43H66N12O12S2@users.noreply.github.com>
Date: Wed, 28 Sep 2022 05:11:03 +0300
Subject: Update processing.py

---
 modules/processing.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 8d043f4d..90e00bf8 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -79,7 +79,7 @@ class StableDiffusionProcessing:
         self.color_corrections = None
         self.denoising_strength: float = 0
         
-        self.ddim_eta = opts.ddim_eta
+        self.eta = opts.eta
         self.ddim_discretize = opts.ddim_discretize
         self.s_churn = opts.s_churn
         self.s_tmin = opts.s_tmin
@@ -124,7 +124,7 @@ class Processed:
         self.extra_generation_params = p.extra_generation_params
         self.index_of_first_image = index_of_first_image
 
-        self.ddim_eta = p.ddim_eta
+        self.eta = p.eta
         self.ddim_discretize = p.ddim_discretize
         self.s_churn = p.s_churn
         self.s_tmin = p.s_tmin
-- 
cgit v1.2.3


From aea5b2510ed4bd9150cea67b6036c837f7df2750 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 28 Sep 2022 17:05:23 +0300
Subject: save parameters for images when using the Save button.

---
 modules/processing.py |  9 +++++++--
 modules/shared.py     |  2 +-
 modules/ui.py         | 16 ++++++++++------
 3 files changed, 18 insertions(+), 9 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/processing.py b/modules/processing.py
index 90e00bf8..e6b84684 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -100,7 +100,7 @@ class StableDiffusionProcessing:
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -139,6 +139,7 @@ class Processed:
         self.all_prompts = all_prompts or [self.prompt]
         self.all_seeds = all_seeds or [self.seed]
         self.all_subseeds = all_subseeds or [self.subseed]
+        self.infotexts = infotexts or [info]
 
     def js(self):
         obj = {
@@ -165,6 +166,7 @@ class Processed:
             "denoising_strength": self.denoising_strength,
             "extra_generation_params": self.extra_generation_params,
             "index_of_first_image": self.index_of_first_image,
+            "infotexts": self.infotexts,
         }
 
         return json.dumps(obj)
@@ -322,6 +324,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     if os.path.exists(cmd_opts.embeddings_dir):
         model_hijack.load_textual_inversion_embeddings(cmd_opts.embeddings_dir, p.sd_model)
 
+    infotexts = []
     output_images = []
     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)
@@ -404,6 +407,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 if opts.samples_save and not p.do_not_save_samples:
                     images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
 
+                infotexts.append(infotext(n, i))
                 output_images.append(image)
 
             state.nextjob()
@@ -416,6 +420,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             grid = images.image_grid(output_images, p.batch_size)
 
             if opts.return_grid:
+                infotexts.insert(0, infotext())
                 output_images.insert(0, grid)
                 index_of_first_image = 1
 
@@ -423,7 +428,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image)
+    return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
diff --git a/modules/shared.py b/modules/shared.py
index 2502fe2d..ae459e14 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -143,6 +143,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
     "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"),
 
     "use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
+    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
 }))
 
 options_templates.update(options_section(('saving-paths', "Paths for saving"), {
@@ -180,7 +181,6 @@ options_templates.update(options_section(('face-restoration', "Face restoration"
     "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
     "code_former_weight": OptionInfo(0.5, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
     "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-    "save_selected_only": OptionInfo(False, "When using 'Save' button, only save a single selected image"),
 }))
 
 options_templates.update(options_section(('system', "System"), {
diff --git a/modules/ui.py b/modules/ui.py
index 7db8edbd..484be762 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -12,7 +12,7 @@ import traceback
 
 import numpy as np
 import torch
-from PIL import Image
+from PIL import Image, PngImagePlugin
 
 import gradio as gr
 import gradio.utils
@@ -97,10 +97,11 @@ def save_files(js_data, images, index):
     filenames = []
 
     data = json.loads(js_data)
-    
-    if index > -1 and opts.save_selected_only and (index > 0 or not opts.return_grid): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
+    if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]):  # ensures we are looking at a specific non-grid picture, and we have save_selected_only
         images = [images[index]]
-        data["seed"] += (index - 1 if opts.return_grid else index)
+        infotexts = [data["infotexts"][index]]
+    else:
+        infotexts = data["infotexts"]
 
     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
         at_start = file.tell() == 0
@@ -116,8 +117,11 @@ def save_files(js_data, images, index):
             if filedata.startswith("data:image/png;base64,"):
                 filedata = filedata[len("data:image/png;base64,"):]
 
-            with open(filepath, "wb") as imgfile:
-                imgfile.write(base64.decodebytes(filedata.encode('utf-8')))
+            pnginfo = PngImagePlugin.PngInfo()
+            pnginfo.add_text('parameters', infotexts[i])
+
+            image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
+            image.save(filepath, quality=opts.jpeg_quality, pnginfo=pnginfo)
 
             filenames.append(filename)
 
-- 
cgit v1.2.3


From d64b451681bdba5453723d3fe0b0681a470d8045 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 28 Sep 2022 18:09:06 +0300
Subject: added support for automatically installing latest k-diffusion added
 eta parameter to parameters output for generated images split eta settings
 into ancestral and ddim (because they have different default values)

---
 launch.py              |  7 +++++
 modules/processing.py  |  9 +++---
 modules/sd_samplers.py | 83 ++++++++++++++++++++++++++------------------------
 modules/shared.py      |  5 +--
 scripts/xy_grid.py     | 12 ++++----
 5 files changed, 65 insertions(+), 51 deletions(-)

(limited to 'modules/processing.py')

diff --git a/launch.py b/launch.py
index 7958c6e9..c3e99afe 100644
--- a/launch.py
+++ b/launch.py
@@ -113,6 +113,13 @@ if not skip_torch_cuda_test:
 if not is_installed("k_diffusion.sampling"):
     run_pip(f"install {k_diffusion_package}", "k-diffusion")
 
+if not check_run_python("import k_diffusion; import inspect; assert 'eta' in inspect.signature(k_diffusion.sampling.sample_euler_ancestral).parameters"):
+    print(f"k-diffusion does not have 'eta' parameter; reinstalling latest version")
+    try:
+        run_pip(f"install --upgrade --force-reinstall {k_diffusion_package}", "k-diffusion")
+    except RuntimeError as e:
+        print(str(e))
+
 if not is_installed("gfpgan"):
     run_pip(f"install {gfpgan_package}", "gfpgan")
 
diff --git a/modules/processing.py b/modules/processing.py
index e6b84684..358a1b11 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -49,7 +49,7 @@ def apply_color_correction(correction, image):
 
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -75,15 +75,15 @@ class StableDiffusionProcessing:
         self.do_not_save_grid: bool = do_not_save_grid
         self.extra_generation_params: dict = extra_generation_params or {}
         self.overlay_images = overlay_images
+        self.eta = eta
         self.paste_to = None
         self.color_corrections = None
         self.denoising_strength: float = 0
-        
-        self.eta = opts.eta
+
         self.ddim_discretize = opts.ddim_discretize
         self.s_churn = opts.s_churn
         self.s_tmin = opts.s_tmin
-        self.s_tmax = float('inf') # not representable as a standard ui option
+        self.s_tmax = float('inf')  # not representable as a standard ui option
         self.s_noise = opts.s_noise
         
         if not seed_enable_extras:
@@ -271,6 +271,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
         "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
         "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
         "Denoising strength": getattr(p, 'denoising_strength', None),
+        "Eta": (None if p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
     }
 
     generation_params.update(p.extra_generation_params)
diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py
index a1183997..3588aae6 100644
--- a/modules/sd_samplers.py
+++ b/modules/sd_samplers.py
@@ -40,10 +40,8 @@ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS']
 
 sampler_extra_params = {
     'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_euler_ancestral': ['eta'],
     'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
     'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-    'sample_dpm_2_ancestral': ['eta'],
 }
 
 def setup_img2img_steps(p, steps=None):
@@ -101,6 +99,8 @@ class VanillaStableDiffusionSampler:
         self.init_latent = None
         self.sampler_noises = None
         self.step = 0
+        self.eta = None
+        self.default_eta = 0.0
 
     def number_of_needed_noises(self, p):
         return 0
@@ -123,20 +123,29 @@ class VanillaStableDiffusionSampler:
         self.step += 1
         return res
 
+    def initialize(self, p):
+        self.eta = p.eta or opts.eta_ddim
+
+        for fieldname in ['p_sample_ddim', 'p_sample_plms']:
+            if hasattr(self.sampler, fieldname):
+                setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
+
+        self.mask = p.mask if hasattr(p, 'mask') else None
+        self.nmask = p.nmask if hasattr(p, 'nmask') else None
+
     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:
-            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps,  ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
         except Exception:
-            self.sampler.make_schedule(ddim_num_steps=steps+1,ddim_eta=p.ddim_eta, ddim_discretize=p.ddim_discretize, verbose=False)
+            self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
 
         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 if hasattr(p, 'mask') else None
-        self.nmask = p.nmask if hasattr(p, 'nmask') else None
+        self.initialize(p)
+
         self.init_latent = x
         self.step = 0
 
@@ -145,11 +154,8 @@ class VanillaStableDiffusionSampler:
         return samples
 
     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)
-        self.mask = None
-        self.nmask = None
+        self.initialize(p)
+
         self.init_latent = None
         self.step = 0
 
@@ -157,9 +163,9 @@ class VanillaStableDiffusionSampler:
 
         # existing code fails with cetin step counts, like 9
         try:
-            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, eta=p.eta)
+            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, eta=self.eta)
         except Exception:
-            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, eta=p.eta)
+            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, eta=self.eta)
 
         return samples_ddim
 
@@ -237,6 +243,8 @@ class KDiffusionSampler:
         self.sampler_noises = None
         self.sampler_noise_index = 0
         self.stop_at = None
+        self.eta = None
+        self.default_eta = 1.0
 
     def callback_state(self, d):
         store_latent(d["denoised"])
@@ -255,22 +263,12 @@ class KDiffusionSampler:
         self.sampler_noise_index += 1
         return res
 
-    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)
-
-        noise = noise * sigmas[steps - t_enc - 1]
-
-        xi = x + noise
-
-        sigma_sched = sigmas[steps - t_enc - 1:]
-
+    def initialize(self, p):
         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
+        self.eta = p.eta or opts.eta_ancestral
 
         if hasattr(k_diffusion.sampling, 'trange'):
             k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
@@ -283,6 +281,25 @@ class KDiffusionSampler:
             if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
                 extra_params_kwargs[param_name] = getattr(p, param_name)
 
+        if 'eta' in inspect.signature(self.func).parameters:
+            extra_params_kwargs['eta'] = self.eta
+
+        return extra_params_kwargs
+
+    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)
+
+        noise = noise * sigmas[steps - t_enc - 1]
+        xi = x + noise
+
+        extra_params_kwargs = self.initialize(p)
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
+
+        self.model_wrap_cfg.init_latent = x
+
         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, **extra_params_kwargs)
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
@@ -291,19 +308,7 @@ class KDiffusionSampler:
         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(self, *args, **kwargs)
-
-        if self.sampler_noises is not None:
-            k_diffusion.sampling.torch = TorchHijack(self)
-
-        extra_params_kwargs = {}
-        for param_name in self.extra_params:
-            if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
-                extra_params_kwargs[param_name] = getattr(p, param_name)
+        extra_params_kwargs = self.initialize(p)
 
         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, **extra_params_kwargs)
 
diff --git a/modules/shared.py b/modules/shared.py
index ae459e14..39cf89bc 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -221,8 +221,9 @@ options_templates.update(options_section(('ui', "User interface"), {
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
-  "eta": OptionInfo(0.0, "DDIM and K Ancestral eta", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform','quad']}),
+  "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+  "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+  "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
   's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
   's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
   's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index 7c01231f..24fa5a0a 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -87,12 +87,12 @@ axis_options = [
     AxisOption("Prompt S/R", str, apply_prompt, format_value),
     AxisOption("Sampler", str, apply_sampler, format_value),
     AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
-    AxisOption("Sigma Churn", float, apply_field("s_churn"),  format_value_add_label),
-    AxisOption("Sigma min",   float, apply_field("s_tmin"),   format_value_add_label),
-    AxisOption("Sigma max",   float, apply_field("s_tmax"),   format_value_add_label),
-    AxisOption("Sigma noise", float, apply_field("s_noise"),  format_value_add_label),
-    AxisOption("DDIM Eta",    float, apply_field("ddim_eta"), format_value_add_label),
-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),#  as it is now all AxisOptionImg2Img items must go after AxisOption ones
+    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
+    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
+    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
+    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
+    AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
+    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
 
-- 
cgit v1.2.3


From 1deac2b6458d3cfe258484ba11e14b75383ed01b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 28 Sep 2022 18:20:30 +0300
Subject: do nor add log messages to saved iamge parameters

---
 modules/img2img.py    | 2 +-
 modules/processing.py | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

(limited to 'modules/processing.py')

diff --git a/modules/img2img.py b/modules/img2img.py
index d80b3e75..03e934e9 100644
--- a/modules/img2img.py
+++ b/modules/img2img.py
@@ -124,4 +124,4 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.samples_log_stdout:
         print(generation_info_js)
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
\ No newline at end of file
+    return processed.images, generation_info_js, plaintext_to_html(processed.info)
diff --git a/modules/processing.py b/modules/processing.py
index 358a1b11..4ecdfcd2 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -280,7 +280,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
 
     negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
 
-    return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() + "".join(["\n\n" + x for x in comments])
+    return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
 
 
 def process_images(p: StableDiffusionProcessing) -> Processed:
@@ -429,7 +429,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
                 images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
 
     devices.torch_gc()
-    return Processed(p, output_images, all_seeds[0], infotext(), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
+    return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
 
 
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
-- 
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