Merge branch 'dev' into test-fp8

modules/images.py

@@ -791,3 +791,4 @@ def flatten(img, bgcolor):
         img = background
 
     return img.convert('RGB')
+

modules/processing.py

@@ -62,18 +62,22 @@ def apply_color_correction(correction, original_image):
     return image.convert('RGB')
 
 
-def apply_overlay(image, paste_loc, index, overlays):
-    if overlays is None or index >= len(overlays):
+def uncrop(image, dest_size, paste_loc):
+    x, y, w, h = paste_loc
+    base_image = Image.new('RGBA', dest_size)
+    image = images.resize_image(1, image, w, h)
+    base_image.paste(image, (x, y))
+    image = base_image
+
+    return image
+
+
+def apply_overlay(image, paste_loc, overlay):
+    if overlay is None:
         return image
 
-    overlay = overlays[index]
-
     if paste_loc is not None:
-        x, y, w, h = paste_loc
-        base_image = Image.new('RGBA', (overlay.width, overlay.height))
-        image = images.resize_image(1, image, w, h)
-        base_image.paste(image, (x, y))
-        image = base_image
+        image = uncrop(image, (overlay.width, overlay.height), paste_loc)
 
     image = image.convert('RGBA')
     image.alpha_composite(overlay)
@@ -81,9 +85,12 @@ def apply_overlay(image, paste_loc, index, overlays):
 
     return image
 
-def create_binary_mask(image):
+def create_binary_mask(image, round=True):
     if image.mode == 'RGBA' and image.getextrema()[-1] != (255, 255):
-        image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+        if round:
+            image = image.split()[-1].convert("L").point(lambda x: 255 if x > 128 else 0)
+        else:
+            image = image.split()[-1].convert("L")
     else:
         image = image.convert('L')
     return image
@@ -308,7 +315,7 @@ class StableDiffusionProcessing:
             c_adm = torch.cat((c_adm, noise_level_emb), 1)
         return c_adm
 
-    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
+    def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
         self.is_using_inpainting_conditioning = True
 
         # Handle the different mask inputs
@@ -320,8 +327,10 @@ class StableDiffusionProcessing:
                 conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
                 conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
 
-                # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
-                conditioning_mask = torch.round(conditioning_mask)
+                if round_image_mask:
+                    # Caller is requesting a discretized mask as input, so we round to either 1.0 or 0.0
+                    conditioning_mask = torch.round(conditioning_mask)
+
         else:
             conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
 
@@ -345,7 +354,7 @@ class StableDiffusionProcessing:
 
         return image_conditioning
 
-    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
+    def img2img_image_conditioning(self, source_image, latent_image, image_mask=None, round_image_mask=True):
         source_image = devices.cond_cast_float(source_image)
 
         # HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
@@ -357,7 +366,7 @@ class StableDiffusionProcessing:
             return self.edit_image_conditioning(source_image)
 
         if self.sampler.conditioning_key in {'hybrid', 'concat'}:
-            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
+            return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask, round_image_mask=round_image_mask)
 
         if self.sampler.conditioning_key == "crossattn-adm":
             return self.unclip_image_conditioning(source_image)
@@ -867,6 +876,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
                 samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
 
+            if p.scripts is not None:
+                ps = scripts.PostSampleArgs(samples_ddim)
+                p.scripts.post_sample(p, ps)
+                samples_ddim = ps.samples
+
             if getattr(samples_ddim, 'already_decoded', False):
                 x_samples_ddim = samples_ddim
             else:
@@ -922,13 +936,31 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                     pp = scripts.PostprocessImageArgs(image)
                     p.scripts.postprocess_image(p, pp)
                     image = pp.image
+
+                mask_for_overlay = getattr(p, "mask_for_overlay", None)
+                overlay_image = p.overlay_images[i] if getattr(p, "overlay_images", None) is not None and i < len(p.overlay_images) else None
+
+                if p.scripts is not None:
+                    ppmo = scripts.PostProcessMaskOverlayArgs(i, mask_for_overlay, overlay_image)
+                    p.scripts.postprocess_maskoverlay(p, ppmo)
+                    mask_for_overlay, overlay_image = ppmo.mask_for_overlay, ppmo.overlay_image
+
                 if p.color_corrections is not None and i < len(p.color_corrections):
                     if save_samples and opts.save_images_before_color_correction:
-                        image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
+                        image_without_cc = apply_overlay(image, p.paste_to, overlay_image)
                         images.save_image(image_without_cc, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-before-color-correction")
                     image = apply_color_correction(p.color_corrections[i], image)
 
-                image = apply_overlay(image, p.paste_to, i, p.overlay_images)
+                # If the intention is to show the output from the model
+                # that is being composited over the original image,
+                # we need to keep the original image around
+                # and use it in the composite step.
+                original_denoised_image = image.copy()
+
+                if p.paste_to is not None:
+                    original_denoised_image = uncrop(original_denoised_image, (overlay_image.width, overlay_image.height), p.paste_to)
+
+                image = apply_overlay(image, p.paste_to, overlay_image)
 
                 if save_samples:
                     images.save_image(image, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p)
@@ -938,16 +970,17 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if opts.enable_pnginfo:
                     image.info["parameters"] = text
                 output_images.append(image)
-                if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay:
+
+                if mask_for_overlay is not None:
                     if opts.return_mask or opts.save_mask:
-                        image_mask = p.mask_for_overlay.convert('RGB')
+                        image_mask = mask_for_overlay.convert('RGB')
                         if save_samples and opts.save_mask:
                             images.save_image(image_mask, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask")
                         if opts.return_mask:
                             output_images.append(image_mask)
 
                     if opts.return_mask_composite or opts.save_mask_composite:
-                        image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, p.mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
+                        image_mask_composite = Image.composite(original_denoised_image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), images.resize_image(2, mask_for_overlay, image.width, image.height).convert('L')).convert('RGBA')
                         if save_samples and opts.save_mask_composite:
                             images.save_image(image_mask_composite, p.outpath_samples, "", p.seeds[i], p.prompts[i], opts.samples_format, info=infotext(i), p=p, suffix="-mask-composite")
                         if opts.return_mask_composite:
@@ -1351,6 +1384,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
     mask_blur_x: int = 4
     mask_blur_y: int = 4
     mask_blur: int = None
+    mask_round: bool = True
     inpainting_fill: int = 0
     inpaint_full_res: bool = True
     inpaint_full_res_padding: int = 0
@@ -1396,7 +1430,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         if image_mask is not None:
             # image_mask is passed in as RGBA by Gradio to support alpha masks,
             # but we still want to support binary masks.
-            image_mask = create_binary_mask(image_mask)
+            image_mask = create_binary_mask(image_mask, round=self.mask_round)
 
             if self.inpainting_mask_invert:
                 image_mask = ImageOps.invert(image_mask)
@@ -1503,7 +1537,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
             latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
             latmask = latmask[0]
-            latmask = np.around(latmask)
+            if self.mask_round:
+                latmask = np.around(latmask)
             latmask = np.tile(latmask[None], (4, 1, 1))
 
             self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
@@ -1515,7 +1550,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             elif self.inpainting_fill == 3:
                 self.init_latent = self.init_latent * self.mask
 
-        self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask)
+        self.image_conditioning = self.img2img_image_conditioning(image * 2 - 1, self.init_latent, image_mask, self.mask_round)
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         x = self.rng.next()
@@ -1527,7 +1562,14 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
 
         if self.mask is not None:
-            samples = samples * self.nmask + self.init_latent * self.mask
+            blended_samples = samples * self.nmask + self.init_latent * self.mask
+
+            if self.scripts is not None:
+                mba = scripts.MaskBlendArgs(samples, self.nmask, self.init_latent, self.mask, blended_samples)
+                self.scripts.on_mask_blend(self, mba)
+                blended_samples = mba.blended_latent
+
+            samples = blended_samples
 
         del x
         devices.torch_gc()

modules/scripts.py

@@ -11,11 +11,31 @@ from modules import shared, paths, script_callbacks, extensions, script_loading,
 
 AlwaysVisible = object()
 
+class MaskBlendArgs:
+    def __init__(self, current_latent, nmask, init_latent, mask, blended_latent, denoiser=None, sigma=None):
+        self.current_latent = current_latent
+        self.nmask = nmask
+        self.init_latent = init_latent
+        self.mask = mask
+        self.blended_latent = blended_latent
+
+        self.denoiser = denoiser
+        self.is_final_blend = denoiser is None
+        self.sigma = sigma
+
+class PostSampleArgs:
+    def __init__(self, samples):
+        self.samples = samples
 
 class PostprocessImageArgs:
     def __init__(self, image):
         self.image = image
 
+class PostProcessMaskOverlayArgs:
+    def __init__(self, index, mask_for_overlay, overlay_image):
+        self.index = index
+        self.mask_for_overlay = mask_for_overlay
+        self.overlay_image = overlay_image
 
 class PostprocessBatchListArgs:
     def __init__(self, images):
@@ -206,6 +226,25 @@ class Script:
 
         pass
 
+    def on_mask_blend(self, p, mba: MaskBlendArgs, *args):
+        """
+        Called in inpainting mode when the original content is blended with the inpainted content.
+        This is called at every step in the denoising process and once at the end.
+        If is_final_blend is true, this is called for the final blending stage.
+        Otherwise, denoiser and sigma are defined and may be used to inform the procedure.
+        """
+
+        pass
+
+    def post_sample(self, p, ps: PostSampleArgs, *args):
+        """
+        Called after the samples have been generated,
+        but before they have been decoded by the VAE, if applicable.
+        Check getattr(samples, 'already_decoded', False) to test if the images are decoded.
+        """
+
+        pass
+
     def postprocess_image(self, p, pp: PostprocessImageArgs, *args):
         """
         Called for every image after it has been generated.
@@ -213,6 +252,13 @@ class Script:
 
         pass
 
+    def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs, *args):
+        """
+        Called for every image after it has been generated.
+        """
+
+        pass
+
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -767,6 +813,22 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running postprocess_batch_list: {script.filename}", exc_info=True)
 
+    def post_sample(self, p, ps: PostSampleArgs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.post_sample(p, ps, *script_args)
+            except Exception:
+                errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
+
+    def on_mask_blend(self, p, mba: MaskBlendArgs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.on_mask_blend(p, mba, *script_args)
+            except Exception:
+                errors.report(f"Error running post_sample: {script.filename}", exc_info=True)
+
     def postprocess_image(self, p, pp: PostprocessImageArgs):
         for script in self.alwayson_scripts:
             try:
@@ -775,6 +837,14 @@ class ScriptRunner:
             except Exception:
                 errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
 
+    def postprocess_maskoverlay(self, p, ppmo: PostProcessMaskOverlayArgs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_maskoverlay(p, ppmo, *script_args)
+            except Exception:
+                errors.report(f"Error running postprocess_image: {script.filename}", exc_info=True)
+
     def before_component(self, component, **kwargs):
         for callback, script in self.on_before_component_elem_id.get(kwargs.get("elem_id"), []):
             try:

modules/sd_disable_initialization.py

@@ -215,7 +215,7 @@ class LoadStateDictOnMeta(ReplaceHelper):
             would be on the meta device.
             """
 
-            if state_dict == sd:
+            if state_dict is sd:
                 state_dict = {k: v.to(device="meta", dtype=v.dtype) for k, v in state_dict.items()}
 
             original(module, state_dict, strict=strict)

modules/sd_samplers_cfg_denoiser.py

@@ -56,6 +56,9 @@ class CFGDenoiser(torch.nn.Module):
         self.sampler = sampler
         self.model_wrap = None
         self.p = None
+
+        # NOTE: masking before denoising can cause the original latents to be oversmoothed
+        # as the original latents do not have noise
         self.mask_before_denoising = False
 
     @property
@@ -105,8 +108,21 @@ class CFGDenoiser(torch.nn.Module):
 
         assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
 
+        # If we use masks, blending between the denoised and original latent images occurs here.
+        def apply_blend(current_latent):
+            blended_latent = current_latent * self.nmask + self.init_latent * self.mask
+
+            if self.p.scripts is not None:
+                from modules import scripts
+                mba = scripts.MaskBlendArgs(current_latent, self.nmask, self.init_latent, self.mask, blended_latent, denoiser=self, sigma=sigma)
+                self.p.scripts.on_mask_blend(self.p, mba)
+                blended_latent = mba.blended_latent
+
+            return blended_latent
+
+        # Blend in the original latents (before)
         if self.mask_before_denoising and self.mask is not None:
-            x = self.init_latent * self.mask + self.nmask * x
+            x = apply_blend(x)
 
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
@@ -207,8 +223,9 @@ class CFGDenoiser(torch.nn.Module):
         else:
             denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
 
+        # Blend in the original latents (after)
         if not self.mask_before_denoising and self.mask is not None:
-            denoised = self.init_latent * self.mask + self.nmask * denoised
+            denoised = apply_blend(denoised)
 
         self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
 

modules/shared_options.py

@@ -258,6 +258,7 @@ options_templates.update(options_section(('ui_prompt_editing', "Prompt editing",
     "keyedit_precision_extra": OptionInfo(0.05, "Precision for <extra networks:0.9> when editing the prompt with Ctrl+up/down", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "keyedit_delimiters": OptionInfo(r".,\/!?%^*;:{}=`~() ", "Word delimiters when editing the prompt with Ctrl+up/down"),
     "keyedit_delimiters_whitespace": OptionInfo(["Tab", "Carriage Return", "Line Feed"], "Ctrl+up/down whitespace delimiters", gr.CheckboxGroup, lambda: {"choices": ["Tab", "Carriage Return", "Line Feed"]}),
+    "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
     "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
 }))
 
@@ -332,6 +333,7 @@ options_templates.update(options_section(('ui', "Live previews", "ui"), {
     "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
     "live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
     "live_preview_fast_interrupt": OptionInfo(False, "Return image with chosen live preview method on interrupt").info("makes interrupts faster"),
+    "js_live_preview_in_modal_lightbox": OptionInfo(True, "Show Live preview in full page image viewer"),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters", "sd"), {

modules/styles.py

@@ -98,10 +98,8 @@ class StyleDatabase:
         self.path = path
 
         folder, file = os.path.split(self.path)
-        self.default_file = file.split("*")[0] + ".csv"
-        if self.default_file == ".csv":
-            self.default_file = "styles.csv"
-        self.default_path = os.path.join(folder, self.default_file)
+        filename, _, ext = file.partition('*')
+        self.default_path = os.path.join(folder, filename + ext)
 
         self.prompt_fields = [field for field in PromptStyle._fields if field != "path"]
 
@@ -155,10 +153,8 @@ class StyleDatabase:
                     row["name"], prompt, negative_prompt, path
                 )
 
-    def get_style_paths(self) -> list():
-        """
-        Returns a list of all distinct paths, including the default path, of
-        files that styles are loaded from."""
+    def get_style_paths(self) -> set:
+        """Returns a set of all distinct paths of files that styles are loaded from."""
         # Update any styles without a path to the default path
         for style in list(self.styles.values()):
             if not style.path:
@@ -172,9 +168,9 @@ class StyleDatabase:
                 style_paths.add(style.path)
 
         # Remove any paths for styles that are just list dividers
-        style_paths.remove("do_not_save")
+        style_paths.discard("do_not_save")
 
-        return list(style_paths)
+        return style_paths
 
     def get_style_prompts(self, styles):
         return [self.styles.get(x, self.no_style).prompt for x in styles]
@@ -196,20 +192,7 @@ class StyleDatabase:
         # The path argument is deprecated, but kept for backwards compatibility
         _ = path
 
-        # Update any styles without a path to the default path
-        for style in list(self.styles.values()):
-            if not style.path:
-                self.styles[style.name] = style._replace(path=self.default_path)
-
-        # Create a list of all distinct paths, including the default path
-        style_paths = set()
-        style_paths.add(self.default_path)
-        for _, style in self.styles.items():
-            if style.path:
-                style_paths.add(style.path)
-
-        # Remove any paths for styles that are just list dividers
-        style_paths.remove("do_not_save")
+        style_paths = self.get_style_paths()
 
         csv_names = [os.path.split(path)[1].lower() for path in style_paths]
 

modules/ui_toprow.py

@@ -79,11 +79,11 @@ class Toprow:
     def create_prompts(self):
         with gr.Column(elem_id=f"{self.id_part}_prompt_container", elem_classes=["prompt-container-compact"] if self.is_compact else [], scale=6):
             with gr.Row(elem_id=f"{self.id_part}_prompt_row", elem_classes=["prompt-row"]):
-                self.prompt = gr.Textbox(label="Prompt", elem_id=f"{self.id_part}_prompt", show_label=False, lines=3, placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+                self.prompt = gr.Textbox(label="Prompt", elem_id=f"{self.id_part}_prompt", show_label=False, lines=3, placeholder="Prompt\n(Press Ctrl+Enter to generate, Alt+Enter to skip, Esc to interrupt)", elem_classes=["prompt"])
                 self.prompt_img = gr.File(label="", elem_id=f"{self.id_part}_prompt_image", file_count="single", type="binary", visible=False)
 
             with gr.Row(elem_id=f"{self.id_part}_neg_prompt_row", elem_classes=["prompt-row"]):
-                self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{self.id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)", elem_classes=["prompt"])
+                self.negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{self.id_part}_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt\n(Press Ctrl+Enter to generate, Alt+Enter to skip, Esc to interrupt)", elem_classes=["prompt"])
 
         self.prompt_img.change(
             fn=modules.images.image_data,

modules/xpu_specific.py

@@ -48,3 +48,12 @@ if has_xpu:
     CondFunc('torch.nn.modules.conv.Conv2d.forward',
         lambda orig_func, self, input: orig_func(self, input.to(self.weight.data.dtype)),
         lambda orig_func, self, input: input.dtype != self.weight.data.dtype)
+    CondFunc('torch.bmm',
+        lambda orig_func, input, mat2, out=None: orig_func(input.to(mat2.dtype), mat2, out=out),
+        lambda orig_func, input, mat2, out=None: input.dtype != mat2.dtype)
+    CondFunc('torch.cat',
+        lambda orig_func, tensors, dim=0, out=None: orig_func([t.to(tensors[0].dtype) for t in tensors], dim=dim, out=out),
+        lambda orig_func, tensors, dim=0, out=None: not all(t.dtype == tensors[0].dtype for t in tensors))
+    CondFunc('torch.nn.functional.scaled_dot_product_attention',
+        lambda orig_func, query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False: orig_func(query, key.to(query.dtype), value.to(query.dtype), attn_mask, dropout_p, is_causal),
+        lambda orig_func, query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False: query.dtype != key.dtype or query.dtype != value.dtype)