Optimized support force push

webui.py

@@ -11,6 +11,7 @@ parser.add_argument("--n_rows", type=int, default=-1, help="rows in the grid; us
 parser.add_argument("--config", type=str, default="configs/stable-diffusion/v1-inference.yaml", help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default="models/ldm/stable-diffusion-v1/model.ckpt", help="path to checkpoint of model",)
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
+parser.add_argument("--optimized", action='store_true', help="load the model onto the device piecemeal instead of all at once to reduce VRAM usage at the cost of performance")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) # i disagree with where you're putting it but since all guidefags are doing it this way, there you go
 parser.add_argument("--realesrgan-dir", type=str, help="RealESRGAN directory", default=('./src/realesrgan' if os.path.exists('./src/realesrgan') else './RealESRGAN'))
 parser.add_argument("--realesrgan-model", type=str, help="Upscaling model for RealESRGAN", default=('RealESRGAN_x4plus'))
@@ -110,6 +111,14 @@ def load_model_from_config(config, ckpt, verbose=False):
     model.eval()
     return model
 
+def load_sd_from_config(ckpt, verbose=False):
+    print(f"Loading model from {ckpt}")
+    pl_sd = torch.load(ckpt, map_location="cpu")
+    if "global_step" in pl_sd:
+        print(f"Global Step: {pl_sd['global_step']}")
+    sd = pl_sd["state_dict"]
+    return sd
+
 def crash(e, s):
     global model
     global device
@@ -270,11 +279,49 @@ def try_loading_RealESRGAN(model_name: str):
             print(traceback.format_exc(), file=sys.stderr)
 try_loading_RealESRGAN('RealESRGAN_x4plus')
 
-config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
-model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
+if opt.optimized:
+    config = OmegaConf.load("optimizedSD/v1-inference.yaml")
+    sd = load_sd_from_config("models/ldm/stable-diffusion-v1/model.ckpt")
+    li, lo = [], []
+    for key, v_ in sd.items():
+        sp = key.split('.')
+        if(sp[0]) == 'model':
+            if('input_blocks' in sp):
+                li.append(key)
+            elif('middle_block' in sp):
+                li.append(key)
+            elif('time_embed' in sp):
+                li.append(key)
+            else:
+                lo.append(key)
+    for key in li:
+        sd['model1.' + key[6:]] = sd.pop(key)
+    for key in lo:
+        sd['model2.' + key[6:]] = sd.pop(key)
 
-device = torch.device(f"cuda") if torch.cuda.is_available() else torch.device("cpu")
-model = (model if opt.no_half else model.half()).to(device)
+    config.modelUNet.params.small_batch = False
+
+    model = instantiate_from_config(config.modelUNet)
+    _, _ = model.load_state_dict(sd, strict=False)
+    model.cuda()
+    model.eval()
+
+    modelCS = instantiate_from_config(config.modelCondStage)
+    _, _ = modelCS.load_state_dict(sd, strict=False)
+    modelCS.eval()
+        
+    modelFS = instantiate_from_config(config.modelFirstStage)
+    _, _ = modelFS.load_state_dict(sd, strict=False)
+    modelFS.eval()
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = model if opt.no_half else model.half()
+    modelCS = modelCS if opt.no_half else modelCS.half()
+else:
+    config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
+    model = load_model_from_config(config, "models/ldm/stable-diffusion-v1/model.ckpt")
+
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    model = (model if opt.no_half else model.half()).to(device)
 
 def load_embeddings(fp):
     if fp is not None and hasattr(model, "embedding_manager"):
@@ -305,9 +352,9 @@ def seed_to_int(s):
     if type(s) is int:
         return s
     if s is None or s == '':
-        return random.randint(0, 2**32 - 1)
-    n = abs(int(s) if s.isdigit() else random.Random(s).randint(0, 2**32 - 1))
-    while n >= 2**32:
+        return random.randint(0,2**32)
+    n = abs(int(s) if s.isdigit() else hash(s))
+    while n > 2**32:
         n = n >> 32
     return n
 
@@ -425,10 +472,10 @@ def resize_image(resize_mode, im, width, height):
 def check_prompt_length(prompt, comments):
     """this function tests if prompt is too long, and if so, adds a message to comments"""
 
-    tokenizer = model.cond_stage_model.tokenizer
-    max_length = model.cond_stage_model.max_length
+    tokenizer = (model if not opt.optimized else modelCS).cond_stage_model.tokenizer
+    max_length = (model if not opt.optimized else modelCS).cond_stage_model.max_length
 
-    info = model.cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length, return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
+    info = (model if not opt.optimized else modelCS).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length, return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
     ovf = info['overflowing_tokens'][0]
     overflowing_count = ovf.shape[0]
     if overflowing_count == 0:
@@ -445,7 +492,7 @@ def process_images(
         outpath, func_init, func_sample, prompt, seed, sampler_name, skip_grid, skip_save, batch_size,
         n_iter, steps, cfg_scale, width, height, prompt_matrix, use_GFPGAN, use_RealESRGAN, realesrgan_model_name,
         fp, ddim_eta=0.0, do_not_save_grid=False, normalize_prompt_weights=True, init_img=None, init_mask=None,
-        keep_mask=False, mask_blur_strength=3, denoising_strength=0.75, resize_mode=None, uses_loopback=False,
+        keep_mask=False, denoising_strength=0.75, resize_mode=None, uses_loopback=False,
         uses_random_seed_loopback=False, sort_samples=True, write_info_files=True, jpg_sample=False):
     """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
     assert prompt is not None
@@ -463,7 +510,7 @@ def process_images(
 
     sample_path = os.path.join(outpath, "samples")
     os.makedirs(sample_path, exist_ok=True)
-    grid_count = len([_ for x in os.listdir() if x.endswith(('.png', '.jpg'))]) - 1 # start at 0
+    grid_count = len(os.listdir(outpath)) - 1
 
     comments = []
 
@@ -501,7 +548,7 @@ def process_images(
     precision_scope = autocast if opt.precision == "autocast" else nullcontext
     output_images = []
     stats = []
-    with torch.no_grad(), precision_scope("cuda"), model.ema_scope():
+    with torch.no_grad(), precision_scope("cuda"), (model.ema_scope() if not opt.optimized else nullcontext()):
         init_data = func_init()
         tic = time.time()
 
@@ -509,7 +556,9 @@ def process_images(
             prompts = all_prompts[n * batch_size:(n + 1) * batch_size]
             seeds = all_seeds[n * batch_size:(n + 1) * batch_size]
 
-            uc = model.get_learned_conditioning(len(prompts) * [""])
+            if opt.optimized:
+                modelCS.to(device)
+            uc = (model if not opt.optimized else modelCS).get_learned_conditioning(len(prompts) * [""])
             if isinstance(prompts, tuple):
                 prompts = list(prompts)
 
@@ -528,18 +577,26 @@ def process_images(
                         weight = weight / totalPromptWeight
                     #print(f"{subprompts[i]} {weight*100.0}%")
                     # note if alpha negative, it functions same as torch.sub
-                    c = torch.add(c,model.get_learned_conditioning(subprompts[i]), alpha=weight)
+                    c = torch.add(c, (model if not opt.optimized else modelCS).get_learned_conditioning(subprompts[i]), alpha=weight)
             else: # just behave like usual
-                c = model.get_learned_conditioning(prompts)
+                c = (model if not opt.optimized else modelCS).get_learned_conditioning(prompts)
 
             shape = [opt_C, height // opt_f, width // opt_f]
 
+            if opt.optimized:
+                mem = torch.cuda.memory_allocated()/1e6
+                modelCS.to("cpu")
+                while(torch.cuda.memory_allocated()/1e6 >= mem):
+                    time.sleep(1)
+
             # we manually generate all input noises because each one should have a specific seed
             x = create_random_tensors([opt_C, height // opt_f, width // opt_f], seeds=seeds)
             samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
 
+            if opt.optimized:
+                modelFS.to(device)
 
-            x_samples_ddim = model.decode_first_stage(samples_ddim)
+            x_samples_ddim = (model if not opt.optimized else modelFS).decode_first_stage(samples_ddim)
             x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
             for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
@@ -561,7 +618,7 @@ def process_images(
                 image = Image.fromarray(x_sample)
                 if init_mask:
                     #init_mask = init_mask if keep_mask else ImageOps.invert(init_mask)
-                    init_mask = init_mask.filter(ImageFilter.GaussianBlur(mask_blur_strength))
+                    init_mask = init_mask.filter(ImageFilter.GaussianBlur(3))
                     init_mask = init_mask.convert('L')
                     init_img = init_img.convert('RGB')
                     image = image.convert('RGB')
@@ -584,11 +641,11 @@ def process_images(
                     sanitized_prompt = sanitized_prompt[:128] #200 is too long
                     sample_path_i = os.path.join(sample_path, sanitized_prompt)
                     os.makedirs(sample_path_i, exist_ok=True)
-                    base_count = len([_ for x in os.listdir() if x.endswith(('.png', '.jpg'))]) - 1 # start at 0
+                    base_count = len(os.listdir(sample_path_i))
                     filename = f"{base_count:05}-{seeds[i]}"
                 else:
                     sample_path_i = sample_path
-                    base_count = len([_ for x in os.listdir() if x.endswith(('.png', '.jpg'))]) - 1 # start at 0
+                    base_count = len(os.listdir(sample_path_i))
                     sanitized_prompt = sanitized_prompt
                     filename = f"{base_count:05}-{seeds[i]}_{sanitized_prompt}"[:128] #same as before
                 if not skip_save:
@@ -667,6 +724,13 @@ def process_images(
             grid_file = f"grid-{grid_count:05}-{seed}_{prompts[i].replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.jpg"
             grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=100, optimize=True)
             grid_count += 1
+
+        if opt.optimized:
+            mem = torch.cuda.memory_allocated()/1e6
+            modelFS.to("cpu")
+            while(torch.cuda.memory_allocated()/1e6 >= mem):
+                time.sleep(1)
+
         toc = time.time()
 
     mem_max_used, mem_total = mem_mon.read_and_stop()
@@ -815,7 +879,7 @@ class Flagging(gr.FlaggingCallback):
         print("Logged:", filenames[0])
 
 
-def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask_blur_strength: int, ddim_steps: int, sampler_name: str,
+def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, ddim_steps: int, sampler_name: str,
             toggles: List[int], realesrgan_model_name: str, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float,
             seed: int, height: int, width: int, resize_mode: int, fp):
     outpath = opt.outdir_img2img or opt.outdir or "outputs/img2img-samples"
@@ -875,10 +939,19 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
         image = image[None].transpose(0, 3, 1, 2)
         image = torch.from_numpy(image)
 
+        if opt.optimized:
+            modelFS.to(device)
+
         init_image = 2. * image - 1.
         init_image = init_image.to(device)
         init_image = repeat(init_image, '1 ... -> b ...', b=batch_size)
-        init_latent = model.get_first_stage_encoding(model.encode_first_stage(init_image))  # move to latent space
+        init_latent = (model if not opt.optimized else modelFS).get_first_stage_encoding((model if not opt.optimized else modelFS).encode_first_stage(init_image))  # move to latent space
+        
+        if opt.optimized:
+            mem = torch.cuda.memory_allocated()/1e6
+            modelFS.to("cpu")
+            while(torch.cuda.memory_allocated()/1e6 >= mem):
+                time.sleep(1)
 
         return init_latent,
 
@@ -936,7 +1009,6 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
                     init_img=init_img,
                     init_mask=init_mask,
                     keep_mask=keep_mask,
-                    mask_blur_strength=mask_blur_strength,
                     denoising_strength=denoising_strength,
                     resize_mode=resize_mode,
                     uses_loopback=loopback,
@@ -958,7 +1030,7 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
                 history.append(init_img)
 
             if not skip_grid:
-                grid_count = len([_ for x in os.listdir() if x.endswith(('.png', '.jpg'))]) - 1 # start at 0
+                grid_count = len(os.listdir(outpath)) - 1
                 grid = image_grid(history, batch_size, force_n_rows=1)
                 grid_file = f"grid-{grid_count:05}-{seed}_{prompt.replace(' ', '_').translate({ord(x): '' for x in invalid_filename_chars})[:128]}.jpg"
                 grid.save(os.path.join(outpath, grid_file), 'jpeg', quality=100, optimize=True)
@@ -992,7 +1064,6 @@ def img2img(prompt: str, image_editor_mode: str, init_info, mask_mode: str, mask
                 init_img=init_img,
                 init_mask=init_mask,
                 keep_mask=keep_mask,
-                mask_blur_strength=mask_blur_strength,
                 denoising_strength=denoising_strength,
                 resize_mode=resize_mode,
                 uses_loopback=loopback,
@@ -1187,8 +1258,8 @@ img2img_image_mode = 'sketch'
 
 def change_image_editor_mode(choice, cropped_image, resize_mode, width, height):
     if choice == "Mask":
-        return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True)]
-    return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)]
+        return [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)]
+    return [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)]
 
 def update_image_mask(cropped_image, resize_mode, width, height):
     resized_cropped_image = resize_image(resize_mode, cropped_image, width, height) if cropped_image else None
@@ -1203,28 +1274,6 @@ def copy_img_to_input(selected=1, imgs = []):
     except IndexError:
         return [None, None]
 
-help_text = """
-    ## Mask/Crop
-    * The masking/cropping is very temperamental.
-    * It may take some time for the image to show when switching from Crop to Mask.
-    * If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
-    * If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.
-
-    ## Advanced Editor
-    * For now the button needs to be clicked twice the first time.
-    * Once you have edited your image, you _need_ to click the save button for the next step to work.
-    * Clear the image from the crop editor (click the x)
-    * Click "Get Image from Advanced Editor" to get the image you saved. If it doesn't work, try opening the editor and saving again.
-
-    If it keeps not working, try switching modes again, switch tabs, clear the image or reload.
-"""
-
-def show_help():
-    return [gr.update(visible=False), gr.update(visible=True), gr.update(value=help_text)]
-
-def hide_help():
-    return [gr.update(visible=True), gr.update(visible=False), gr.update(value="")]
-
 with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion WebUI") as demo:
     with gr.Tabs():
         with gr.TabItem("Stable Diffusion Text-to-Image Unified"):
@@ -1265,16 +1314,19 @@ with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion WebUI")
                     gr.Markdown("Generate images from images with Stable Diffusion")
                     img2img_prompt = gr.Textbox(label="Prompt", placeholder="A fantasy landscape, trending on artstation.", lines=1, value=img2img_defaults['prompt'])
                     img2img_image_editor_mode = gr.Radio(choices=["Mask", "Crop"], label="Image Editor Mode", value="Crop")
-                    img2img_show_help_btn = gr.Button("Show Hints")
-                    img2img_hide_help_btn = gr.Button("Hide Hints", visible=False)
-                    img2img_help = gr.Markdown(visible=False, value="")
-                    with gr.Row():
-                        img2img_painterro_btn = gr.Button("Advanced Editor")
-                        img2img_copy_from_painterro_btn = gr.Button(value="Get Image from Advanced Editor")
+                    gr.Markdown(
+                        """
+                            The masking/cropping is very temperamental.
+                            It may take some time for the image to show when switching from Crop to Mask.
+                            * If the image doesn't appear after switching to Mask, switch back to Crop and then back again to Mask
+                            * If the mask appears distorted (the brush is weirdly shaped instead of round), switch back to Crop and then back again to Mask.
+
+                            If it keeps not working, try switching modes again, switch tabs, clear the image or reload.
+                        """
+                    )
                     img2img_image_editor = gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil", tool="select")
                     img2img_image_mask = gr.Image(value=sample_img2img, source="upload", interactive=True, type="pil", tool="sketch", visible=False)
-                    img2img_mask = gr.Radio(choices=["Keep masked area", "Regenerate only masked area"], label="Mask Mode", type="index", value=img2img_mask_modes[img2img_defaults['mask_mode']], visible=False)
-                    img2img_mask_blur_strength = gr.Slider(minimum=1, maximum=10, step=1, label="How much blurry should the mask be? (to avoid hard edges)", value=3, visible=False)
+                    img2img_mask = gr.Radio(choices=["Keep masked area", "Regenerate only masked area"], label="Mask Mode", type="index", value=img2img_mask_modes[img2img_defaults['mask_mode']])
                     img2img_steps = gr.Slider(minimum=1, maximum=250, step=1, label="Sampling Steps", value=img2img_defaults['ddim_steps'])
                     img2img_sampling = gr.Radio(label='Sampling method (k_lms is default k-diffusion sampler)', choices=["DDIM", 'k_dpm_2_a', 'k_dpm_2', 'k_euler_a', 'k_euler', 'k_heun', 'k_lms'], value=img2img_defaults['sampler_name'])
                     img2img_toggles = gr.CheckboxGroup(label='', choices=img2img_toggles, value=img2img_toggle_defaults, type="index")
@@ -1288,8 +1340,8 @@ with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion WebUI")
                     img2img_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=img2img_defaults["width"])
                     img2img_resize = gr.Radio(label="Resize mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value=img2img_resize_modes[img2img_defaults['resize_mode']])
                     img2img_embeddings = gr.File(label = "Embeddings file for textual inversion", visible=hasattr(model, "embedding_manager"))
-                    img2img_btn_mask = gr.Button("Generate", visible=False).style(full_width=True)
-                    img2img_btn_editor = gr.Button("Generate").style(full_width=True)
+                    img2img_btn_mask = gr.Button("Generate", visible=False)
+                    img2img_btn_editor = gr.Button("Generate")
                 with gr.Column():
                     output_img2img_gallery = gr.Gallery(label="Images")
                     output_img2img_select_image = gr.Number(label='Select image number from results for copying', value=1, precision=None)
@@ -1302,7 +1354,7 @@ with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion WebUI")
             img2img_image_editor_mode.change(
                 change_image_editor_mode,
                 [img2img_image_editor_mode, img2img_image_editor, img2img_resize, img2img_width, img2img_height],
-                [img2img_image_editor, img2img_image_mask, img2img_btn_editor, img2img_btn_mask, img2img_painterro_btn, img2img_copy_from_painterro_btn, img2img_mask, img2img_mask_blur_strength]
+                [img2img_image_editor, img2img_image_mask, img2img_btn_editor, img2img_btn_mask]
             )
 
             img2img_image_editor.edit(
@@ -1311,18 +1363,6 @@ with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion WebUI")
                 img2img_image_mask
             )
 
-            img2img_show_help_btn.click(
-                show_help,
-                None,
-                [img2img_show_help_btn, img2img_hide_help_btn, img2img_help]
-            )
-
-            img2img_hide_help_btn.click(
-                hide_help,
-                None,
-                [img2img_show_help_btn, img2img_hide_help_btn, img2img_help]
-            )
-
             output_img2img_copy_to_input_btn.click(
                 copy_img_to_input,
                 [output_img2img_select_image, output_img2img_gallery],
@@ -1337,41 +1377,16 @@ with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion WebUI")
 
             img2img_btn_mask.click(
                 img2img,
-                [img2img_prompt, img2img_image_editor_mode, img2img_image_mask, img2img_mask, img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles, img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg, img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize, img2img_embeddings],
+                [img2img_prompt, img2img_image_editor_mode, img2img_image_mask, img2img_mask, img2img_steps, img2img_sampling, img2img_toggles, img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg, img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize, img2img_embeddings],
                 [output_img2img_gallery, output_img2img_seed, output_img2img_params, output_img2img_stats]
             )
 
             img2img_btn_editor.click(
                 img2img,
-                [img2img_prompt, img2img_image_editor_mode, img2img_image_editor, img2img_mask, img2img_mask_blur_strength, img2img_steps, img2img_sampling, img2img_toggles, img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg, img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize, img2img_embeddings],
+                [img2img_prompt, img2img_image_editor_mode, img2img_image_editor, img2img_mask, img2img_steps, img2img_sampling, img2img_toggles, img2img_realesrgan_model_name, img2img_batch_count, img2img_batch_size, img2img_cfg, img2img_denoising, img2img_seed, img2img_height, img2img_width, img2img_resize, img2img_embeddings],
                 [output_img2img_gallery, output_img2img_seed, output_img2img_params, output_img2img_stats]
             )
 
-            img2img_painterro_btn.click(None, [img2img_image_editor], None, _js="""(img) => {
-                try {
-                    Painterro({
-                        hiddenTools: ['arrow'],
-                        saveHandler: function (image, done) {
-                            localStorage.setItem('painterro-image', image.asDataURL());
-                            done(true);
-                        },
-                    }).show(Array.isArray(img) ? img[0] : img);
-                } catch(e) {
-                    const script = document.createElement('script');
-                    script.src = 'https://unpkg.com/painterro@1.2.78/build/painterro.min.js';
-                    document.head.appendChild(script);
-                    const style = document.createElement('style');
-                    style.appendChild(document.createTextNode('.ptro-holder-wrapper { z-index: 9999 !important; }'));
-                    document.head.appendChild(style);
-                }
-                return [];
-            }""")
-
-            img2img_copy_from_painterro_btn.click(None, None, [img2img_image_editor, img2img_image_mask], _js="""() => {
-                const image = localStorage.getItem('painterro-image')
-                return [image, image];
-            }""")
-
         if GFPGAN is not None:
             gfpgan_defaults = {
                 'strength': 100,