Merge pull request #11 from AUTOMATIC1111/master

sync branches

modules/api/api.py

@@ -286,7 +286,7 @@ class Api:
         # copy from check_progress_call of ui.py
 
         if shared.state.job_count == 0:
-            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict())
+            return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo)
 
         # avoid dividing zero
         progress = 0.01
@@ -308,7 +308,7 @@ class Api:
         if shared.state.current_image and not req.skip_current_image:
             current_image = encode_pil_to_base64(shared.state.current_image)
 
-        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
+        return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo)
 
     def interrogateapi(self, interrogatereq: InterrogateRequest):
         image_b64 = interrogatereq.image

modules/api/models.py

@@ -168,6 +168,7 @@ class ProgressResponse(BaseModel):
     eta_relative: float = Field(title="ETA in secs")
     state: dict = Field(title="State", description="The current state snapshot")
     current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.")
+    textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.")
 
 class InterrogateRequest(BaseModel):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")

modules/hypernetworks/hypernetwork.py

@@ -619,7 +619,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 epoch_num = hypernetwork.step // steps_per_epoch
                 epoch_step = hypernetwork.step % steps_per_epoch
 
-                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                description = f"Training hypernetwork [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+                pbar.set_description(description)
+                shared.state.textinfo = description
                 if hypernetwork_dir is not None and steps_done % save_hypernetwork_every == 0:
                     # Before saving, change name to match current checkpoint.
                     hypernetwork_name_every = f'{hypernetwork_name}-{steps_done}'

modules/img2img.py

@@ -59,38 +59,34 @@ def process_batch(p, input_dir, output_dir, args):
                 processed_image.save(os.path.join(output_dir, filename))
 
 
-def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_with_mask_orig, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
-    is_inpaint = mode == 1
-    is_batch = mode == 2
+def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args):
+    is_batch = mode == 5
 
-    if is_inpaint:
-        # Drawn mask
-        if mask_mode == 0:
-            is_mask_sketch = isinstance(init_img_with_mask, dict)
-            is_mask_paint = not is_mask_sketch
-            if is_mask_sketch:
-                # Sketch: mask iff. not transparent
-                image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
-                alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
-                mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
-            else:
-                # Color-sketch: mask iff. painted over
-                image = init_img_with_mask
-                orig = init_img_with_mask_orig or init_img_with_mask
-                pred = np.any(np.array(image) != np.array(orig), axis=-1)
-                mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
-                mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
-                blur = ImageFilter.GaussianBlur(mask_blur)
-                image = Image.composite(image.filter(blur), orig, mask.filter(blur))
-
-            image = image.convert("RGB")
-        # Uploaded mask
-        else:
-            image = init_img_inpaint
-            mask = init_mask_inpaint
-    # No mask
+    if mode == 0:  # img2img
+        image = init_img.convert("RGB")
+        mask = None
+    elif mode == 1:  # img2img sketch
+        image = sketch.convert("RGB")
+        mask = None
+    elif mode == 2:  # inpaint
+        image, mask = init_img_with_mask["image"], init_img_with_mask["mask"]
+        alpha_mask = ImageOps.invert(image.split()[-1]).convert('L').point(lambda x: 255 if x > 0 else 0, mode='1')
+        mask = ImageChops.lighter(alpha_mask, mask.convert('L')).convert('L')
+        image = image.convert("RGB")
+    elif mode == 3:  # inpaint sketch
+        image = inpaint_color_sketch
+        orig = inpaint_color_sketch_orig or inpaint_color_sketch
+        pred = np.any(np.array(image) != np.array(orig), axis=-1)
+        mask = Image.fromarray(pred.astype(np.uint8) * 255, "L")
+        mask = ImageEnhance.Brightness(mask).enhance(1 - mask_alpha / 100)
+        blur = ImageFilter.GaussianBlur(mask_blur)
+        image = Image.composite(image.filter(blur), orig, mask.filter(blur))
+        image = image.convert("RGB")
+    elif mode == 4:  # inpaint upload mask
+        image = init_img_inpaint
+        mask = init_mask_inpaint
     else:
-        image = init_img
+        image = None
         mask = None
 
     # Use the EXIF orientation of photos taken by smartphones.

modules/prompt_parser.py

@@ -49,6 +49,8 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     [[5, 'a  c'], [10, 'a {b|d{ c']]
     >>> g("((a][:b:c [d:3]")
     [[3, '((a][:b:c '], [10, '((a][:b:c d']]
+    >>> g("[a|(b:1.1)]")
+    [[1, 'a'], [2, '(b:1.1)'], [3, 'a'], [4, '(b:1.1)'], [5, 'a'], [6, '(b:1.1)'], [7, 'a'], [8, '(b:1.1)'], [9, 'a'], [10, '(b:1.1)']]
     """
 
     def collect_steps(steps, tree):
@@ -84,7 +86,7 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
                 yield args[0].value
             def __default__(self, data, children, meta):
                 for child in children:
-                    yield from child
+                    yield child
         return AtStep().transform(tree)
 
     def get_schedule(prompt):

modules/sd_disable_initialization.py

@@ -20,6 +20,19 @@ class DisableInitialization:
     ```
     """
 
+    def __init__(self):
+        self.replaced = []
+
+    def replace(self, obj, field, func):
+        original = getattr(obj, field, None)
+        if original is None:
+            return None
+
+        self.replaced.append((obj, field, original))
+        setattr(obj, field, func)
+
+        return original
+
     def __enter__(self):
         def do_nothing(*args, **kwargs):
             pass
@@ -37,11 +50,14 @@ class DisableInitialization:
         def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
 
             # this file is always 404, prevent making request
-            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
-                raise transformers.utils.hub.EntryNotFoundError
+            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+                return None
 
             try:
-                return original(url, *args, local_files_only=True, **kwargs)
+                res = original(url, *args, local_files_only=True, **kwargs)
+                if res is None:
+                    res = original(url, *args, local_files_only=False, **kwargs)
+                return res
             except Exception as e:
                 return original(url, *args, local_files_only=False, **kwargs)
 
@@ -54,42 +70,19 @@ class DisableInitialization:
         def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
             return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
 
-        self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
-        self.init_no_grad_normal = torch.nn.init._no_grad_normal_
-        self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
-        self.create_model_and_transforms = open_clip.create_model_and_transforms
-        self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
-        self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
-        self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
-        self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
-        self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
-
-        torch.nn.init.kaiming_uniform_ = do_nothing
-        torch.nn.init._no_grad_normal_ = do_nothing
-        torch.nn.init._no_grad_uniform_ = do_nothing
-        open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
-        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
-        if self.transformers_modeling_utils_load_pretrained_model is not None:
-            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
-        if self.transformers_tokenization_utils_base_cached_file is not None:
-            transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
-        if self.transformers_configuration_utils_cached_file is not None:
-            transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
-        if self.transformers_utils_hub_get_from_cache is not None:
-            transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
+        self.replace(torch.nn.init, 'kaiming_uniform_', do_nothing)
+        self.replace(torch.nn.init, '_no_grad_normal_', do_nothing)
+        self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing)
+        self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained)
+        self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained)
+        self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model)
+        self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file)
+        self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file)
+        self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache)
 
     def __exit__(self, exc_type, exc_val, exc_tb):
-        torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
-        torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
-        torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
-        open_clip.create_model_and_transforms = self.create_model_and_transforms
-        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
-        if self.transformers_modeling_utils_load_pretrained_model is not None:
-            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
-        if self.transformers_tokenization_utils_base_cached_file is not None:
-            transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
-        if self.transformers_configuration_utils_cached_file is not None:
-            transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
-        if self.transformers_utils_hub_get_from_cache is not None:
-            transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
+        for obj, field, original in self.replaced:
+            setattr(obj, field, original)
+
+        self.replaced.clear()
 

modules/sd_models.py

@@ -334,6 +334,7 @@ def load_model(checkpoint_info=None):
     timer = Timer()
 
     sd_model = None
+
     try:
         with sd_disable_initialization.DisableInitialization():
             sd_model = instantiate_from_config(sd_config.model)

modules/shared.py

@@ -74,8 +74,8 @@ parser.add_argument("--freeze-settings", action='store_true', help="disable edit
 parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
 parser.add_argument("--gradio-debug",  action='store_true', help="launch gradio with --debug option")
 parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
-parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
-parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it")
+parser.add_argument("--gradio-img2img-tool", type=str, help='does not do anything')
+parser.add_argument("--gradio-inpaint-tool", type=str, help="does not do anything")
 parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
 parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
 parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)

modules/textual_inversion/preprocess.py

@@ -135,7 +135,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
     params.process_caption_deepbooru = process_caption_deepbooru
     params.preprocess_txt_action = preprocess_txt_action
 
-    for index, imagefile in enumerate(tqdm.tqdm(files)):
+    pbar = tqdm.tqdm(files)
+    for index, imagefile in enumerate(pbar):
         params.subindex = 0
         filename = os.path.join(src, imagefile)
         try:
@@ -143,6 +144,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
         except Exception:
             continue
 
+        description = f"Preprocessing [Image {index}/{len(files)}]"
+        pbar.set_description(description)
+        shared.state.textinfo = description
+
         params.src = filename
 
         existing_caption = None

modules/textual_inversion/textual_inversion.py

@@ -476,7 +476,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 epoch_num = embedding.step // steps_per_epoch
                 epoch_step = embedding.step % steps_per_epoch
 
-                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")
+                description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}"
+                pbar.set_description(description)
+                shared.state.textinfo = description
                 if embedding_dir is not None and steps_done % save_embedding_every == 0:
                     # Before saving, change name to match current checkpoint.
                     embedding_name_every = f'{embedding_name}-{steps_done}'

modules/ui.py

@@ -795,53 +795,67 @@ def create_ui():
 
         with FormRow().style(equal_height=False):
             with gr.Column(variant='panel', elem_id="img2img_settings"):
+                with gr.Tabs(elem_id="mode_img2img"):
+                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
+                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
 
-                with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
-                    with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab"):
-                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool, image_mode="RGBA").style(height=480)
+                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
+                        sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
 
-                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab"):
-                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_inpaint_tool, image_mode="RGBA").style(height=480)
-                        init_img_with_mask_orig = gr.State(None)
+                    with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
+                        init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
 
-                        use_color_sketch = cmd_opts.gradio_inpaint_tool == "color-sketch"
-                        if use_color_sketch:
-                            def update_orig(image, state):
-                                if image is not None:
-                                    same_size = state is not None and state.size == image.size
-                                    has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
-                                    edited = same_size and has_exact_match
-                                    return image if not edited or state is None else state
+                    with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
+                        inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
+                        inpaint_color_sketch_orig = gr.State(None)
 
-                            init_img_with_mask.change(update_orig, [init_img_with_mask, init_img_with_mask_orig], init_img_with_mask_orig)
+                        def update_orig(image, state):
+                            if image is not None:
+                                same_size = state is not None and state.size == image.size
+                                has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
+                                edited = same_size and has_exact_match
+                                return image if not edited or state is None else state
 
-                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
-                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
+                        inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
 
-                        with FormRow():
-                            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
-                            mask_alpha = gr.Slider(label="Mask transparency", interactive=use_color_sketch, visible=use_color_sketch, elem_id="img2img_mask_alpha")
+                    with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
+                        init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
+                        init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
 
-                        with FormRow():
-                            mask_mode = gr.Radio(label="Mask source", choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
-                            inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
-
-                        with FormRow():
-                            inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
-
-                        with FormRow():
-                            with gr.Column():
-                                inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
-
-                            with gr.Column(scale=4):
-                                inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
-
-                    with gr.TabItem('Batch img2img', id='batch', elem_id="img2img_batch_tab"):
+                    with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
                         gr.HTML(f"<p class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
                         img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
                         img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
 
+                with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
+                    with FormRow():
+                        mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
+                        mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
+
+                    with FormRow():
+                        inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
+
+                    with FormRow():
+                        inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
+
+                    with FormRow():
+                        with gr.Column():
+                            inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
+
+                        with gr.Column(scale=4):
+                            inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
+
+                def select_img2img_tab(tab):
+                    return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
+
+                for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]):
+                    elem.select(
+                        fn=lambda tab=i: select_img2img_tab(tab),
+                        inputs=[],
+                        outputs=[inpaint_controls, mask_alpha],
+                    )
+
                 with FormRow():
                     resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
 
@@ -900,20 +914,6 @@ def create_ui():
                 ]
             )
 
-            mask_mode.change(
-                lambda mode, img: {
-                    init_img_with_mask: gr_show(mode == 0),
-                    init_img_inpaint: gr_show(mode == 1),
-                    init_mask_inpaint: gr_show(mode == 1),
-                },
-                inputs=[mask_mode, init_img_with_mask],
-                outputs=[
-                    init_img_with_mask,
-                    init_img_inpaint,
-                    init_mask_inpaint,
-                ],
-            )
-
             img2img_args = dict(
                 fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
                 _js="submit_img2img",
@@ -924,11 +924,12 @@ def create_ui():
                     img2img_prompt_style,
                     img2img_prompt_style2,
                     init_img,
+                    sketch,
                     init_img_with_mask,
-                    init_img_with_mask_orig,
+                    inpaint_color_sketch,
+                    inpaint_color_sketch_orig,
                     init_img_inpaint,
                     init_mask_inpaint,
-                    mask_mode,
                     steps,
                     sampler_index,
                     mask_blur,

style.css

@@ -557,7 +557,9 @@ canvas[key="mask"] {
 }
 
 #img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,
-img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img
+#img2img_sketch, #img2img_sketch > .h-60, #img2img_sketch > .h-60 > div, #img2img_sketch > .h-60 > div > img,
+#img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img,
+#inpaint_sketch, #inpaint_sketch > .h-60, #inpaint_sketch > .h-60 > div, #inpaint_sketch > .h-60 > div > img
 {
     height: 480px !important;
     max-height: 480px !important;