Merge branch 'dev' into test-fp8

extensions-builtin/Lora/network_oft.py

@@ -21,6 +21,8 @@ class NetworkModuleOFT(network.NetworkModule):
         self.lin_module = None
         self.org_module: list[torch.Module] = [self.sd_module]
 
+        self.scale = 1.0
+
         # kohya-ss
         if "oft_blocks" in weights.w.keys():
             self.is_kohya = True
@@ -53,12 +55,18 @@ class NetworkModuleOFT(network.NetworkModule):
             self.constraint = None
             self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
 
-    def calc_updown_kb(self, orig_weight, multiplier):
+    def calc_updown(self, orig_weight):
         oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-        oft_blocks = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
+        eye = torch.eye(self.block_size, device=self.oft_blocks.device)
+
+        if self.is_kohya:
+            block_Q = oft_blocks - oft_blocks.transpose(1, 2) # ensure skew-symmetric orthogonal matrix
+            norm_Q = torch.norm(block_Q.flatten())
+            new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
+            block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
+            oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
 
         R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
-        R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
 
         # This errors out for MultiheadAttention, might need to be handled up-stream
         merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
@@ -72,26 +80,3 @@ class NetworkModuleOFT(network.NetworkModule):
         updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
         output_shape = orig_weight.shape
         return self.finalize_updown(updown, orig_weight, output_shape)
-
-    def calc_updown(self, orig_weight):
-        # if alpha is a very small number as in coft, calc_scale() will return a almost zero number so we ignore it
-        multiplier = self.multiplier()
-        return self.calc_updown_kb(orig_weight, multiplier)
-
-    # override to remove the multiplier/scale factor; it's already multiplied in get_weight
-    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
-        if self.bias is not None:
-            updown = updown.reshape(self.bias.shape)
-            updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
-            updown = updown.reshape(output_shape)
-
-        if len(output_shape) == 4:
-            updown = updown.reshape(output_shape)
-
-        if orig_weight.size().numel() == updown.size().numel():
-            updown = updown.reshape(orig_weight.shape)
-
-        if ex_bias is not None:
-            ex_bias = ex_bias * self.multiplier()
-
-        return updown, ex_bias

extensions-builtin/Lora/networks.py

@@ -159,7 +159,8 @@ def load_network(name, network_on_disk):
     bundle_embeddings = {}
 
     for key_network, weight in sd.items():
-        key_network_without_network_parts, network_part = key_network.split(".", 1)
+        key_network_without_network_parts, _, network_part = key_network.partition(".")
+
         if key_network_without_network_parts == "bundle_emb":
             emb_name, vec_name = network_part.split(".", 1)
             emb_dict = bundle_embeddings.get(emb_name, {})

extensions-builtin/extra-options-section/scripts/extra_options_section.py

@@ -23,11 +23,12 @@ class ExtraOptionsSection(scripts.Script):
         self.setting_names = []
         self.infotext_fields = []
         extra_options = shared.opts.extra_options_img2img if is_img2img else shared.opts.extra_options_txt2img
+        elem_id_tabname = "extra_options_" + ("img2img" if is_img2img else "txt2img")
 
         mapping = {k: v for v, k in generation_parameters_copypaste.infotext_to_setting_name_mapping}
 
         with gr.Blocks() as interface:
-            with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and extra_options else gr.Group():
+            with gr.Accordion("Options", open=False, elem_id=elem_id_tabname) if shared.opts.extra_options_accordion and extra_options else gr.Group(elem_id=elem_id_tabname):
 
                 row_count = math.ceil(len(extra_options) / shared.opts.extra_options_cols)
 
@@ -70,7 +71,7 @@ This page allows you to add some settings to the main interface of txt2img and i
 """),
     "extra_options_txt2img": shared.OptionInfo([], "Settings for txt2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img interfaces").needs_reload_ui(),
     "extra_options_img2img": shared.OptionInfo([], "Settings for img2img", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in img2img interfaces").needs_reload_ui(),
-    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Number, {"precision": 0}).needs_reload_ui(),
+    "extra_options_cols": shared.OptionInfo(1, "Number of columns for added settings", gr.Slider, {"step": 1, "minimum": 1, "maximum": 20}).info("displayed amount will depend on the actual browser window width").needs_reload_ui(),
     "extra_options_accordion": shared.OptionInfo(False, "Place added settings into an accordion").needs_reload_ui()
 }))
 

javascript/imageviewer.js

@@ -34,7 +34,7 @@ function updateOnBackgroundChange() {
     if (modalImage && modalImage.offsetParent) {
         let currentButton = selected_gallery_button();
         let preview = gradioApp().querySelectorAll('.livePreview > img');
-        if (preview.length > 0) {
+        if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) {
             // show preview image if available
             modalImage.src = preview[preview.length - 1].src;
         } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) {

javascript/ui.js

@@ -215,9 +215,33 @@ function restoreProgressImg2img() {
 }
 
 
+/**
+ * Configure the width and height elements on `tabname` to accept
+ * pasting of resolutions in the form of "width x height".
+ */
+function setupResolutionPasting(tabname) {
+    var width = gradioApp().querySelector(`#${tabname}_width input[type=number]`);
+    var height = gradioApp().querySelector(`#${tabname}_height input[type=number]`);
+    for (const el of [width, height]) {
+        el.addEventListener('paste', function(event) {
+            var pasteData = event.clipboardData.getData('text/plain');
+            var parsed = pasteData.match(/^\s*(\d+)\D+(\d+)\s*$/);
+            if (parsed) {
+                width.value = parsed[1];
+                height.value = parsed[2];
+                updateInput(width);
+                updateInput(height);
+                event.preventDefault();
+            }
+        });
+    }
+}
+
 onUiLoaded(function() {
     showRestoreProgressButton('txt2img', localGet("txt2img_task_id"));
     showRestoreProgressButton('img2img', localGet("img2img_task_id"));
+    setupResolutionPasting('txt2img');
+    setupResolutionPasting('img2img');
 });
 
 

modules/images.py

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

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)

script.js

@@ -121,16 +121,22 @@ document.addEventListener("DOMContentLoaded", function() {
 });
 
 /**
- * Add a ctrl+enter as a shortcut to start a generation
+ * Add keyboard shortcuts:
+ * Ctrl+Enter to start/restart a generation
+ * Alt/Option+Enter to skip a generation
+ * Esc to interrupt a generation
  */
 document.addEventListener('keydown', function(e) {
     const isEnter = e.key === 'Enter' || e.keyCode === 13;
-    const isModifierKey = e.metaKey || e.ctrlKey || e.altKey;
+    const isCtrlKey = e.metaKey || e.ctrlKey;
+    const isAltKey = e.altKey;
+    const isEsc = e.key === 'Escape';
 
-    const interruptButton = get_uiCurrentTabContent().querySelector('button[id$=_interrupt]');
     const generateButton = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
+    const interruptButton = get_uiCurrentTabContent().querySelector('button[id$=_interrupt]');
+    const skipButton = get_uiCurrentTabContent().querySelector('button[id$=_skip]');
 
-    if (isEnter && isModifierKey) {
+    if (isCtrlKey && isEnter) {
         if (interruptButton.style.display === 'block') {
             interruptButton.click();
             const callback = (mutationList) => {
@@ -150,6 +156,21 @@ document.addEventListener('keydown', function(e) {
         }
         e.preventDefault();
     }
+
+    if (isAltKey && isEnter) {
+        skipButton.click();
+        e.preventDefault();
+    }
+
+    if (isEsc) {
+        const globalPopup = document.querySelector('.global-popup');
+        const lightboxModal = document.querySelector('#lightboxModal');
+        if (!globalPopup || globalPopup.style.display === 'none') {
+            if (document.activeElement === lightboxModal) return;
+            interruptButton.click();
+            e.preventDefault();
+        }
+    }
 });
 
 /**

webui.sh

@@ -133,7 +133,7 @@ case "$gpu_info" in
             if [[ $(bc <<< "$pyv <= 3.10") -eq 1 ]] 
             then
                 # Navi users will still use torch 1.13 because 2.0 does not seem to work.
-                export TORCH_COMMAND="pip install torch==1.13.1+rocm5.2 torchvision==0.14.1+rocm5.2 --index-url https://download.pytorch.org/whl/rocm5.2"
+                export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.6"
             else
                 printf "\e[1m\e[31mERROR: RX 5000 series GPUs must be using at max python 3.10, aborting...\e[0m"
                 exit 1
@@ -143,8 +143,7 @@ case "$gpu_info" in
     *"Navi 2"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0
     ;;
     *"Navi 3"*) [[ -z "${TORCH_COMMAND}" ]] && \
-         export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/test/rocm5.6"
-        # Navi 3 needs at least 5.5 which is only on the torch 2.1.0 release candidates right now
+         export TORCH_COMMAND="pip install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm5.7"
     ;;
     *"Renoir"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0
         printf "\n%s\n" "${delimiter}"