Merge branch 'master' of github.com:AUTOMATIC1111/stable-diffusion-webui

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -11,25 +11,41 @@ from omegaconf import OmegaConf
 
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.util import instantiate_from_config, ismap
+from modules import shared, sd_hijack
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
+cached_ldsr_model: torch.nn.Module = None
+
 
 # Create LDSR Class
 class LDSR:
     def load_model_from_config(self, half_attention):
-        print(f"Loading model from {self.modelPath}")
-        pl_sd = torch.load(self.modelPath, map_location="cpu")
-        sd = pl_sd["state_dict"]
-        config = OmegaConf.load(self.yamlPath)
-        config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
-        model = instantiate_from_config(config.model)
-        model.load_state_dict(sd, strict=False)
-        model.cuda()
-        if half_attention:
-            model = model.half()
-
-        model.eval()
+        global cached_ldsr_model
+
+        if shared.opts.ldsr_cached and cached_ldsr_model is not None:
+            print(f"Loading model from cache")
+            model: torch.nn.Module = cached_ldsr_model
+        else:
+            print(f"Loading model from {self.modelPath}")
+            pl_sd = torch.load(self.modelPath, map_location="cpu")
+            sd = pl_sd["state_dict"]
+            config = OmegaConf.load(self.yamlPath)
+            config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
+            model: torch.nn.Module = instantiate_from_config(config.model)
+            model.load_state_dict(sd, strict=False)
+            model = model.to(shared.device)
+            if half_attention:
+                model = model.half()
+            if shared.cmd_opts.opt_channelslast:
+                model = model.to(memory_format=torch.channels_last)
+
+            sd_hijack.model_hijack.hijack(model) # apply optimization
+            model.eval()
+
+            if shared.opts.ldsr_cached:
+                cached_ldsr_model = model
+
         return {"model": model}
 
     def __init__(self, model_path, yaml_path):
@@ -94,7 +110,8 @@ class LDSR:
         down_sample_method = 'Lanczos'
 
         gc.collect()
-        torch.cuda.empty_cache()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
 
         im_og = image
         width_og, height_og = im_og.size
@@ -131,7 +148,9 @@ class LDSR:
 
         del model
         gc.collect()
-        torch.cuda.empty_cache()
+        if torch.cuda.is_available:
+            torch.cuda.empty_cache()
+
         return a
 
 
@@ -146,7 +165,7 @@ def get_cond(selected_path):
     c = rearrange(c, '1 c h w -> 1 h w c')
     c = 2. * c - 1.
 
-    c = c.to(torch.device("cuda"))
+    c = c.to(shared.device)
     example["LR_image"] = c
     example["image"] = c_up
 

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -59,6 +59,7 @@ def on_ui_settings():
     import gradio as gr
 
     shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
 
 
 script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js

@@ -88,7 +88,7 @@ function checkBrackets(evt) {
   if(counterElt.title != '') {
     counterElt.style = 'color: #FF5555;';
   } else {
-    counterElt.style = 'color: #000;';
+    counterElt.style = '';
   }
 }
 

modules/processing.py

@@ -13,13 +13,15 @@ from skimage import exposure
 from typing import Any, Dict, List, Optional
 
 import modules.sd_hijack
-from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste
+from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks
 from modules.sd_hijack import model_hijack
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.face_restoration
 import modules.images as images
 import modules.styles
+import modules.sd_models as sd_models
+import modules.sd_vae as sd_vae
 import logging
 from ldm.data.util import AddMiDaS
 from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
@@ -454,8 +456,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
 
     try:
         for k, v in p.override_settings.items():
-            setattr(opts, k, v)  # we don't call onchange for simplicity which makes changing model impossible
-            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet since it is relatively fast to load on-change, while SD models are not
+            setattr(opts, k, v)
+            if k == 'sd_hypernetwork': shared.reload_hypernetworks()  # make onchange call for changing hypernet
+            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()  # make onchange call for changing SD model
+            if k == 'sd_vae': sd_vae.reload_vae_weights()  # make onchange call for changing VAE
 
         res = process_images_inner(p)
 
@@ -463,6 +467,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
         for k, v in stored_opts.items():
             setattr(opts, k, v)
             if k == 'sd_hypernetwork': shared.reload_hypernetworks()
+            if k == 'sd_model_checkpoint': sd_models.reload_model_weights()
+            if k == 'sd_vae': sd_vae.reload_vae_weights()
 
     return res
 
@@ -571,9 +577,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
             devices.torch_gc()
 
-            if opts.filter_nsfw:
-                import modules.safety as safety
-                x_samples_ddim = modules.safety.censor_batch(x_samples_ddim)
+            if p.scripts is not None:
+                p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
 
             for i, x_sample in enumerate(x_samples_ddim):
                 x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)

modules/safety.py

-import torch
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from transformers import AutoFeatureExtractor
-from PIL import Image
-
-import modules.shared as shared
-
-safety_model_id = "CompVis/stable-diffusion-safety-checker"
-safety_feature_extractor = None
-safety_checker = None
-
-def numpy_to_pil(images):
-    """
-    Convert a numpy image or a batch of images to a PIL image.
-    """
-    if images.ndim == 3:
-        images = images[None, ...]
-    images = (images * 255).round().astype("uint8")
-    pil_images = [Image.fromarray(image) for image in images]
-
-    return pil_images
-
-# check and replace nsfw content
-def check_safety(x_image):
-    global safety_feature_extractor, safety_checker
-
-    if safety_feature_extractor is None:
-        safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
-        safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
-
-    safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
-    x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
-
-    return x_checked_image, has_nsfw_concept
-
-
-def censor_batch(x):
-    x_samples_ddim_numpy = x.cpu().permute(0, 2, 3, 1).numpy()
-    x_checked_image, has_nsfw_concept = check_safety(x_samples_ddim_numpy)
-    x = torch.from_numpy(x_checked_image).permute(0, 3, 1, 2)
-
-    return x

modules/scripts.py

@@ -88,6 +88,17 @@ class Script:
 
         pass
 
+    def postprocess_batch(self, p, *args, **kwargs):
+        """
+        Same as process_batch(), but called for every batch after it has been generated.
+
+        **kwargs will have same items as process_batch, and also:
+          - batch_number - index of current batch, from 0 to number of batches-1
+          - images - torch tensor with all generated images, with values ranging from 0 to 1;
+        """
+
+        pass
+
     def postprocess(self, p, processed, *args):
         """
         This function is called after processing ends for AlwaysVisible scripts.
@@ -347,6 +358,15 @@ class ScriptRunner:
                 print(f"Error running postprocess: {script.filename}", file=sys.stderr)
                 print(traceback.format_exc(), file=sys.stderr)
 
+    def postprocess_batch(self, p, images, **kwargs):
+        for script in self.alwayson_scripts:
+            try:
+                script_args = p.script_args[script.args_from:script.args_to]
+                script.postprocess_batch(p, *script_args, images=images, **kwargs)
+            except Exception:
+                print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
+                print(traceback.format_exc(), file=sys.stderr)
+
     def before_component(self, component, **kwargs):
         for script in self.scripts:
             try:

modules/shared.py

@@ -367,7 +367,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
     "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
-    "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
     "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
 }))

modules/ui_extensions.py

@@ -206,12 +206,13 @@ def refresh_available_extensions_from_data(hide_tags):
         if url is None:
             continue
 
+        existing = installed_extension_urls.get(normalize_git_url(url), None)
+        extension_tags = extension_tags + ["installed"] if existing else extension_tags
+
         if len([x for x in extension_tags if x in tags_to_hide]) > 0:
             hidden += 1
             continue
 
-        existing = installed_extension_urls.get(normalize_git_url(url), None)
-
         install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
 
         tags_text = ", ".join([f"<span class='extension-tag' title='{tags.get(x, '')}'>{x}</span>" for x in extension_tags])
@@ -222,7 +223,11 @@ def refresh_available_extensions_from_data(hide_tags):
                 <td>{html.escape(description)}</td>
                 <td>{install_code}</td>
             </tr>
-    """
+        
+        """
+
+        for tag in [x for x in extension_tags if x not in tags]:
+            tags[tag] = tag
 
     code += """
         </tbody>
@@ -272,7 +277,7 @@ def create_ui():
                     install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
 
                 with gr.Row():
-                    hide_tags = gr.CheckboxGroup(value=["ads", "localization"], label="Hide extensions with tags", choices=["script", "ads", "localization"])
+                    hide_tags = gr.CheckboxGroup(value=["ads", "localization", "installed"], label="Hide extensions with tags", choices=["script", "ads", "localization", "installed"])
 
                 install_result = gr.HTML()
                 available_extensions_table = gr.HTML()

requirements.txt

 accelerate
 basicsr
-diffusers
 fairscale==0.4.4
 fonts
 font-roboto

requirements_versions.txt

 transformers==4.19.2
-diffusers==0.3.0
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8

script.js

-function gradioApp(){
-    return document.getElementsByTagName('gradio-app')[0].shadowRoot;
+function gradioApp() {
+    const gradioShadowRoot = document.getElementsByTagName('gradio-app')[0].shadowRoot
+    return !!gradioShadowRoot ? gradioShadowRoot : document;
 }
 
 function get_uiCurrentTab() {
@@ -82,4 +83,4 @@ function uiElementIsVisible(el) {
         }
     }
     return isVisible;
-}
\ No newline at end of file
+}