Merge branch 'AUTOMATIC1111:master' into master

launch.py

@@ -94,6 +94,15 @@ def prepare_enviroment():
 
     gfpgan_package = os.environ.get('GFPGAN_PACKAGE', "git+https://github.com/TencentARC/GFPGAN.git@8d2447a2d918f8eba5a4a01463fd48e45126a379")
     clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLIP.git@d50d76daa670286dd6cacf3bcd80b5e4823fc8e1")
+    deepdanbooru_package = os.environ.get('DEEPDANBOORU_PACKAGE', "git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26")
+
+    xformers_windows_package = os.environ.get('XFORMERS_WINDOWS_PACKAGE', 'https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl')
+
+    stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/CompVis/stable-diffusion.git")
+    taming_transformers_repo = os.environ.get('TAMING_REANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
+    k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
+    codeformer_repo = os.environ.get('CODEFORMET_REPO', 'https://github.com/sczhou/CodeFormer.git')
+    blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
 
     stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
     taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
@@ -131,23 +140,23 @@ def prepare_enviroment():
 
     if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
-            run_pip("install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+            run_pip(f"install -U -I --no-deps {xformers_windows_package}", "xformers")
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 
     if not is_installed("deepdanbooru") and deepdanbooru:
-        run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
+        run_pip(f"install {deepdanbooru_package}#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
     if not is_installed("pyngrok") and ngrok:
         run_pip("install pyngrok", "ngrok")
 
     os.makedirs(dir_repos, exist_ok=True)
 
-    git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
-    git_clone("https://github.com/CompVis/taming-transformers.git", repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
-    git_clone("https://github.com/crowsonkb/k-diffusion.git", repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
-    git_clone("https://github.com/sczhou/CodeFormer.git", repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
-    git_clone("https://github.com/salesforce/BLIP.git", repo_dir('BLIP'), "BLIP", blip_commit_hash)
+    git_clone(stable_diffusion_repo, repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)
+    git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
+    git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
+    git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
+    git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
 
     if not is_installed("lpips"):
         run_pip(f"install -r {os.path.join(repo_dir('CodeFormer'), 'requirements.txt')}", "requirements for CodeFormer")

modules/extras.py

@@ -20,26 +20,40 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
-
+    outputs = []
+    
     if extras_mode == 1:
         #convert file to pillow image
         for img in image_folder:
             image = Image.open(img)
             imageArr.append(image)
             imageNameArr.append(os.path.splitext(img.orig_name)[0])
+    elif extras_mode == 2:
+        assert not shared.cmd_opts.hide_ui_dir_config, '--hide-ui-dir-config option must be disabled'
+
+        if input_dir == '':
+            return outputs, "Please select an input directory.", ''
+        image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)]
+        for img in image_list:
+            image = Image.open(img)
+            imageArr.append(image)
+            imageNameArr.append(img)
     else:
         imageArr.append(image)
         imageNameArr.append(None)
 
-    outpath = opts.outdir_samples or opts.outdir_extras_samples
+    if extras_mode == 2 and output_dir != '':
+        outpath = output_dir
+    else:
+        outpath = opts.outdir_samples or opts.outdir_extras_samples
 
-    outputs = []
+    
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
@@ -112,7 +126,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             image.info = existing_pnginfo
             image.info["extras"] = info
 
-        outputs.append(image)
+        if extras_mode != 2 or show_extras_results :
+            outputs.append(image)
 
     devices.torch_gc()
 

modules/ngrok.py

@@ -1,12 +1,14 @@
 from pyngrok import ngrok, conf, exception
 
 
-def connect(token, port):
+def connect(token, port, region):
     if token == None:
         token = 'None'
-    conf.get_default().auth_token = token
+    config = conf.PyngrokConfig(
+        auth_token=token, region=region
+    )
     try:
-        public_url = ngrok.connect(port).public_url
+        public_url = ngrok.connect(port, pyngrok_config=config).public_url
     except exception.PyngrokNgrokError:
         print(f'Invalid ngrok authtoken, ngrok connection aborted.\n'
               f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')

modules/processing.py

@@ -53,7 +53,7 @@ def get_correct_sampler(p):
         return sd_samplers.samplers_for_img2img
 
 class StableDiffusionProcessing:
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None, do_not_reload_embeddings=False):
         self.sd_model = sd_model
         self.outpath_samples: str = outpath_samples
         self.outpath_grids: str = outpath_grids
@@ -80,6 +80,7 @@ class StableDiffusionProcessing:
         self.extra_generation_params: dict = extra_generation_params or {}
         self.overlay_images = overlay_images
         self.eta = eta
+        self.do_not_reload_embeddings = do_not_reload_embeddings
         self.paste_to = None
         self.color_corrections = None
         self.denoising_strength: float = 0
@@ -364,7 +365,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
     def infotext(iteration=0, position_in_batch=0):
         return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch)
 
-    if os.path.exists(cmd_opts.embeddings_dir):
+    if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
         model_hijack.embedding_db.load_textual_inversion_embeddings()
 
     infotexts = []

modules/shared.py

@@ -40,6 +40,7 @@ parser.add_argument("--unload-gfpgan", action='store_true', help="does not do an
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
 parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
+parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us")
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))

modules/textual_inversion/textual_inversion.py

@@ -296,6 +296,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
+                do_not_reload_embeddings=True,
             )
 
             if preview_from_txt2img:

modules/ui.py

@@ -56,7 +56,7 @@ if not cmd_opts.share and not cmd_opts.listen:
 if cmd_opts.ngrok != None:
     import modules.ngrok as ngrok
     print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)
+    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
 
 
 def gr_show(visible=True):
@@ -508,9 +508,11 @@ def create_toprow(is_img2img):
             with gr.Row():
                 with gr.Column(scale=1, elem_id="style_pos_col"):
                     prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style.save_to_config = True
 
                 with gr.Column(scale=1, elem_id="style_neg_col"):
                     prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
+                    prompt_style2.save_to_config = True
 
     return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
@@ -566,6 +568,24 @@ def create_ui(wrap_gradio_gpu_call):
     import modules.img2img
     import modules.txt2img
 
+    def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
+        def refresh():
+            refresh_method()
+            args = refreshed_args() if callable(refreshed_args) else refreshed_args
+
+            for k, v in args.items():
+                setattr(refresh_component, k, v)
+
+            return gr.update(**(args or {}))
+
+        refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
+        refresh_button.click(
+            fn = refresh,
+            inputs = [],
+            outputs = [refresh_component]
+        )
+        return refresh_button
+
     with gr.Blocks(analytics_enabled=False) as txt2img_interface:
         txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
         dummy_component = gr.Label(visible=False)
@@ -1016,6 +1036,15 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Batch Process'):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
+                    with gr.TabItem('Batch from Directory'):
+                        extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,
+                            placeholder="A directory on the same machine where the server is running."
+                        )
+                        extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,
+                            placeholder="Leave blank to save images to the default path."
+                        )
+                        show_extras_results = gr.Checkbox(label='Show result images', value=True)
+
                 with gr.Tabs(elem_id="extras_resize_mode"):
                     with gr.TabItem('Scale by'):
                         upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
@@ -1060,6 +1089,9 @@ def create_ui(wrap_gradio_gpu_call):
                 dummy_component,
                 extras_image,
                 image_batch,
+                extras_batch_input_dir,
+                extras_batch_output_dir,
+                show_extras_results,
                 gfpgan_visibility,
                 codeformer_visibility,
                 codeformer_weight,
@@ -1191,8 +1223,12 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
-                    train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                    train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                    with gr.Row():
+                        train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
+                        create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
+                    with gr.Row():
+                        train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
+                        create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
                     learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     batch_size = gr.Number(label='Batch size', value=1, precision=0)
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
@@ -1343,26 +1379,11 @@ def create_ui(wrap_gradio_gpu_call):
         if info.refresh is not None:
             if is_quicksettings:
                 res = comp(label=info.label, value=fun, **(args or {}))
-                refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)
+                refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
             else:
                 with gr.Row(variant="compact"):
                     res = comp(label=info.label, value=fun, **(args or {}))
-                    refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)
-
-            def refresh():
-                info.refresh()
-                refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
-
-                for k, v in refreshed_args.items():
-                    setattr(res, k, v)
-
-                return gr.update(**(refreshed_args or {}))
-
-            refresh_button.click(
-                fn=refresh,
-                inputs=[],
-                outputs=[res],
-            )
+                    refresh_button = create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
         else:
             res = comp(label=info.label, value=fun, **(args or {}))
 
@@ -1703,7 +1724,9 @@ Requested path was: {f}
             saved_value = ui_settings.get(key, None)
             if saved_value is None:
                 ui_settings[key] = getattr(obj, field)
-            elif condition is None or condition(saved_value):
+            elif condition and not condition(saved_value):
+                print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
+            else:
                 setattr(obj, field, saved_value)
 
         if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
@@ -1727,6 +1750,11 @@ Requested path was: {f}
         if type(x) == gr.Number:
             apply_field(x, 'value')
 
+        # Since there are many dropdowns that shouldn't be saved,
+        # we only mark dropdowns that should be saved.
+        if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
+            apply_field(x, 'value', lambda val: val in x.choices)
+
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")

style.css

@@ -478,7 +478,7 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork{
+#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name{
     max-width: 2.5em;
     min-width: 2.5em;
     height: 2.4em;