Merge branch 'AUTOMATIC1111:master' into master

.github/workflows/on_pull_request.yaml

@@ -0,0 +1,36 @@
+# See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
+name: Run Linting/Formatting on Pull Requests
+
+on:
+  - push
+  - pull_request
+  # See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
+  # if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
+  # pull_request:  
+  #  branches:
+  #    - master
+  #  branches-ignore:
+  #    - development
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v3
+        with:
+          python-version: 3.10.6
+      - name: Install PyLint
+        run: | 
+          python -m pip install --upgrade pip
+          pip install pylint
+      # This lets PyLint check to see if it can resolve imports
+      - name: Install dependencies
+        run : |
+          export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
+          python launch.py
+      - name: Analysing the code with pylint
+        run: |
+          pylint $(git ls-files '*.py')

.pylintrc

@@ -0,0 +1,3 @@
+# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
+[MESSAGES CONTROL]
+disable=C,R,W,E,I

javascript/dragdrop.js

@@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) {
 window.document.addEventListener('dragover', e => {
     const target = e.composedPath()[0];
     const imgWrap = target.closest('[data-testid="image"]');
-    if ( !imgWrap && target.placeholder != "Prompt") {
+    if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     e.stopPropagation();
@@ -53,7 +53,7 @@ window.document.addEventListener('dragover', e => {
 
 window.document.addEventListener('drop', e => {
     const target = e.composedPath()[0];
-    if (target.placeholder === "Prompt") {
+    if (target.placeholder.indexOf("Prompt") == -1) {
         return;
     }
     const imgWrap = target.closest('[data-testid="image"]');

javascript/edit-attention.js

@@ -2,6 +2,8 @@ addEventListener('keydown', (event) => {
 	let target = event.originalTarget || event.composedPath()[0];
 	if (!target.hasAttribute("placeholder")) return;
 	if (!target.placeholder.toLowerCase().includes("prompt")) return;
+	if (! (event.metaKey || event.ctrlKey)) return;
+
 
 	let plus = "ArrowUp"
 	let minus = "ArrowDown"

javascript/hints.js

@@ -16,6 +16,8 @@ titles = {
     "\u{1f3a8}": "Add a random artist to the prompt.",
     "\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
     "\u{1f4c2}": "Open images output directory",
+    "\u{1f4be}": "Save style",
+    "\u{1f4cb}": "Apply selected styles to current prompt",
 
     "Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
     "SD upscale": "Upscale image normally, split result into tiles, improve each tile using img2img, merge whole image back",

javascript/imageParams.js

@@ -2,7 +2,7 @@ window.onload = (function(){
     window.addEventListener('drop', e => {
         const target = e.composedPath()[0];
         const idx = selected_gallery_index();
-        if (target.placeholder != "Prompt") return;
+        if (target.placeholder.indexOf("Prompt") == -1) return;
 
         let prompt_target = get_tab_index('tabs') == 1 ? "img2img_prompt_image" : "txt2img_prompt_image";
 

javascript/progressbar.js

@@ -1,5 +1,7 @@
 // code related to showing and updating progressbar shown as the image is being made
 global_progressbars = {}
+galleries = {}
+galleryObservers = {}
 
 function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip, id_interrupt, id_preview, id_gallery){
     var progressbar = gradioApp().getElementById(id_progressbar)
@@ -31,13 +33,24 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
                 preview.style.width = gallery.clientWidth + "px"
                 preview.style.height = gallery.clientHeight + "px"
 
+				//only watch gallery if there is a generation process going on
+                check_gallery(id_gallery);    
+
                 var progressDiv = gradioApp().querySelectorAll('#' + id_progressbar_span).length > 0;
                 if(!progressDiv){
                     if (skip) {
                         skip.style.display = "none"
                     }
                     interrupt.style.display = "none"
+			
+                    //disconnect observer once generation finished, so user can close selected image if they want
+                    if (galleryObservers[id_gallery]) {
+                        galleryObservers[id_gallery].disconnect();
+                        galleries[id_gallery] = null;
+                    }    
                 }
+
+
             }
 
             window.setTimeout(function() { requestMoreProgress(id_part, id_progressbar_span, id_skip, id_interrupt) }, 500)
@@ -46,6 +59,28 @@ function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_skip
 	}
 }
 
+function check_gallery(id_gallery){
+    let gallery = gradioApp().getElementById(id_gallery)
+    // if gallery has no change, no need to setting up observer again.
+    if (gallery && galleries[id_gallery] !== gallery){
+        galleries[id_gallery] = gallery;
+        if(galleryObservers[id_gallery]){
+            galleryObservers[id_gallery].disconnect();
+        }
+        let prevSelectedIndex = selected_gallery_index();
+        galleryObservers[id_gallery] = new MutationObserver(function (){
+            let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item')
+            let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2')
+            if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) {
+                //automatically re-open previously selected index (if exists)
+                galleryButtons[prevSelectedIndex].click();
+		showGalleryImage();
+            }
+        })
+        galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false })
+    }
+}
+
 onUiUpdate(function(){
     check_progressbar('txt2img', 'txt2img_progressbar', 'txt2img_progress_span', 'txt2img_skip', 'txt2img_interrupt', 'txt2img_preview', 'txt2img_gallery')
     check_progressbar('img2img', 'img2img_progressbar', 'img2img_progress_span', 'img2img_skip', 'img2img_interrupt', 'img2img_preview', 'img2img_gallery')

javascript/ui.js

@@ -141,7 +141,7 @@ function submit_img2img(){
 
 function ask_for_style_name(_, prompt_text, negative_prompt_text) {
     name_ = prompt('Style name:')
-    return name_ === null ? [null, null, null]: [name_, prompt_text, negative_prompt_text]
+    return [name_, prompt_text, negative_prompt_text]
 }
 
 
@@ -187,12 +187,10 @@ onUiUpdate(function(){
 	if (!txt2img_textarea) {
 		txt2img_textarea = gradioApp().querySelector("#txt2img_prompt > label > textarea");
 		txt2img_textarea?.addEventListener("input", () => update_token_counter("txt2img_token_button"));
-        txt2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "txt2img_generate"));
 	}
 	if (!img2img_textarea) {
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
-        img2img_textarea?.addEventListener("keyup", (event) => submit_prompt(event, "img2img_generate"));
 	}
 })
 
@@ -220,14 +218,6 @@ function update_token_counter(button_id) {
 	token_timeout = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time);
 }
 
-function submit_prompt(event, generate_button_id) {
-    if (event.altKey && event.keyCode === 13) {
-        event.preventDefault();
-        gradioApp().getElementById(generate_button_id).click();
-        return;
-    }
-}
-
 function restart_reload(){
     document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>';
     setTimeout(function(){location.reload()},2000)

launch.py

@@ -9,6 +9,7 @@ import platform
 dir_repos = "repositories"
 python = sys.executable
 git = os.environ.get('GIT', "git")
+index_url = os.environ.get('INDEX_URL', "")
 
 
 def extract_arg(args, name):
@@ -57,7 +58,8 @@ def run_python(code, desc=None, errdesc=None):
 
 
 def run_pip(args, desc=None):
-    return run(f'"{python}" -m pip {args} --prefer-binary', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
+    index_url_line = f' --index-url {index_url}' if index_url != '' else ''
+    return run(f'"{python}" -m pip {args} --prefer-binary{index_url_line}', desc=f"Installing {desc}", errdesc=f"Couldn't install {desc}")
 
 
 def check_run_python(code):
@@ -102,6 +104,7 @@ def prepare_enviroment():
     args = shlex.split(commandline_args)
 
     args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
+    args, reinstall_xformers = extract_arg(args, '--reinstall-xformers')
     xformers = '--xformers' in args
     deepdanbooru = '--deepdanbooru' in args
     ngrok = '--ngrok' in args
@@ -126,9 +129,9 @@ def prepare_enviroment():
     if not is_installed("clip"):
         run_pip(f"install {clip_package}", "clip")
 
-    if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
+    if (not is_installed("xformers") or reinstall_xformers) and xformers and platform.python_version().startswith("3.10"):
         if platform.system() == "Windows":
-            run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
+            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")
         elif platform.system() == "Linux":
             run_pip("install xformers", "xformers")
 

modules/deepbooru.py

@@ -102,7 +102,7 @@ def get_deepbooru_tags_model():
 
     tags = dd.project.load_tags_from_project(model_path)
     model = dd.project.load_model_from_project(
-        model_path, compile_model=True
+        model_path, compile_model=False
     )
     return model, tags
 

modules/hypernetworks/hypernetwork.py

@@ -182,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
     return self.to_out(out)
 
 
-def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def stack_conds(conds):
+    if len(conds) == 1:
+        return torch.stack(conds)
+
+    # same as in reconstruct_multicond_batch
+    token_count = max([x.shape[0] for x in conds])
+    for i in range(len(conds)):
+        if conds[i].shape[0] != token_count:
+            last_vector = conds[i][-1:]
+            last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
+            conds[i] = torch.vstack([conds[i], last_vector_repeated])
+
+    return torch.stack(conds)
+
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -211,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
 
     if unload:
         shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -235,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entry in pbar:
+    for i, entries in pbar:
         hypernetwork.step = i + ititial_step
 
         scheduler.apply(optimizer, hypernetwork.step)
@@ -246,26 +260,29 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             break
 
         with torch.autocast("cuda"):
-            cond = entry.cond.to(devices.device)
+            c = stack_conds([entry.cond for entry in entries]).to(devices.device)
-            x = entry.latent.to(devices.device)
+#            c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+            loss = shared.sd_model(x, c)[0]
             del x
-            del cond
+            del c
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
-
+        mean_loss = losses.mean()
-        pbar.set_description(f"loss: {losses.mean():.7f}")
+        if torch.isnan(mean_loss):
+            raise RuntimeError("Loss diverged.")
+        pbar.set_description(f"loss: {mean_loss:.7f}")
 
         if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
             last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
             hypernetwork.save(last_saved_file)
 
         textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
-            "loss": f"{losses.mean():.7f}",
+            "loss": f"{mean_loss:.7f}",
             "learn_rate": scheduler.learn_rate
         })
 
@@ -292,7 +309,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
                 p.width = preview_width
                 p.height = preview_height
             else:
-                p.prompt = entry.cond_text
+                p.prompt = entries[0].cond_text
                 p.steps = 20
 
             preview_text = p.prompt
@@ -313,9 +330,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
         shared.state.textinfo = f"""
 <p>
-Loss: {losses.mean():.7f}<br/>
+Loss: {mean_loss:.7f}<br/>
 Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>

modules/images_history.py

@@ -97,14 +97,16 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima
 
 
 def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict):
-    if tabname == "txt2img":
+    if opts.outdir_samples != "":
+        dir_name = opts.outdir_samples
+    elif tabname == "txt2img":
         dir_name = opts.outdir_txt2img_samples
     elif tabname == "img2img":
         dir_name = opts.outdir_img2img_samples
     elif tabname == "extras":
         dir_name = opts.outdir_extras_samples
     d = dir_name.split("/")
-    dir_name = d[0]
+    dir_name = "/" if dir_name.startswith("/") else d[0]
     for p in d[1:]:
         dir_name = os.path.join(dir_name, p)
     with gr.Row():

modules/processing.py

@@ -140,7 +140,7 @@ class Processed:
         self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
         self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
         self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
-        self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
+        self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
         self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
 
         self.all_prompts = all_prompts or [self.prompt]
@@ -528,7 +528,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                 firstphase_height_truncated = int(scale * self.height)
 
             else:
-                self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
 
                 width_ratio = self.width / self.firstphase_width
                 height_ratio = self.height / self.firstphase_height
@@ -540,6 +539,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
                     firstphase_width_truncated = self.firstphase_height * self.width / self.height
                     firstphase_height_truncated = self.firstphase_height
 
+            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
             self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
             self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
 
@@ -557,11 +557,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
 
-        decoded_samples = decode_first_stage(self.sd_model, samples)
+        if opts.use_scale_latent_for_hires_fix:
+            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
 
-        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
-            decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
         else:
+            decoded_samples = decode_first_stage(self.sd_model, samples)
             lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
 
             batch_images = []
@@ -578,7 +578,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
             decoded_samples = decoded_samples.to(shared.device)
             decoded_samples = 2. * decoded_samples - 1.
 
-        samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
+            samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
 
         shared.state.nextjob()
 

modules/sd_hijack.py

@@ -24,7 +24,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
 
-    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
+    if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward

modules/sd_models.py

@@ -1,4 +1,4 @@
-import glob
+import collections
 import os.path
 import sys
 from collections import namedtuple
@@ -15,6 +15,7 @@ model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
 CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
 checkpoints_list = {}
+checkpoints_loaded = collections.OrderedDict()
 
 try:
     # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
@@ -132,41 +133,45 @@ def load_model_weights(model, checkpoint_info):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
+    if checkpoint_info not in checkpoints_loaded:
+        print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
-    pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
+        if "global_step" in pl_sd:
+            print(f"Global Step: {pl_sd['global_step']}")
 
-    if "global_step" in pl_sd:
+        sd = get_state_dict_from_checkpoint(pl_sd)
-        print(f"Global Step: {pl_sd['global_step']}")
+        model.load_state_dict(sd, strict=False)
 
-    sd = get_state_dict_from_checkpoint(pl_sd)
+        if shared.cmd_opts.opt_channelslast:
+            model.to(memory_format=torch.channels_last)
 
-    model.load_state_dict(sd, strict=False)
+        if not shared.cmd_opts.no_half:
+            model.half()
 
-    if shared.cmd_opts.opt_channelslast:
+        devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
-        model.to(memory_format=torch.channels_last)
+        devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
 
-    if not shared.cmd_opts.no_half:
+        vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
-        model.half()
 
-    devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
+        if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
-    devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
+            vae_file = shared.cmd_opts.vae_path
 
-    vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
+        if os.path.exists(vae_file):
+            print(f"Loading VAE weights from: {vae_file}")
+            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+            vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+            model.first_stage_model.load_state_dict(vae_dict)
 
-    if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
+        model.first_stage_model.to(devices.dtype_vae)
-        vae_file = shared.cmd_opts.vae_path
 
-    if os.path.exists(vae_file):
+        checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
-        print(f"Loading VAE weights from: {vae_file}")
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
-
+            checkpoints_loaded.popitem(last=False)  # LRU
-        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+    else:
-
+        print(f"Loading weights [{sd_model_hash}] from cache")
-        vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
+        checkpoints_loaded.move_to_end(checkpoint_info)
-
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
-        model.first_stage_model.load_state_dict(vae_dict)
-
-    model.first_stage_model.to(devices.dtype_vae)
 
     model.sd_model_hash = sd_model_hash
     model.sd_model_checkpoint = checkpoint_file
@@ -205,6 +210,7 @@ def reload_model_weights(sd_model, info=None):
         return
 
     if sd_model.sd_checkpoint_info.config != checkpoint_info.config:
+        checkpoints_loaded.clear()
         shared.sd_model = load_model()
         return shared.sd_model
 

modules/shared.py

@@ -218,6 +218,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "SWIN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "ldsr_steps": OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -242,6 +243,7 @@ options_templates.update(options_section(('training', "Training"), {
 
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
+    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
     "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
     "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
@@ -255,7 +257,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", 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()}),
-    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
@@ -283,6 +284,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
 }))
 
 options_templates.update(options_section(('sampler-params', "Sampler parameters"), {

modules/textual_inversion/dataset.py

@@ -24,11 +24,12 @@ class DatasetEntry:
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
 
         self.placeholder_token = placeholder_token
 
+        self.batch_size = batch_size
         self.width = width
         self.height = height
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@@ -78,13 +79,14 @@ class PersonalizedBase(Dataset):
 
             if include_cond:
                 entry.cond_text = self.create_text(filename_text)
-                entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
+                entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
 
             self.dataset.append(entry)
 
-        self.length = len(self.dataset) * repeats
+        assert len(self.dataset) > 1, "No images have been found in the dataset."
+        self.length = len(self.dataset) * repeats // batch_size
 
-        self.initial_indexes = np.arange(self.length) % len(self.dataset)
+        self.initial_indexes = np.arange(len(self.dataset))
         self.indexes = None
         self.shuffle()
 
@@ -101,13 +103,19 @@ class PersonalizedBase(Dataset):
         return self.length
 
     def __getitem__(self, i):
-        if i % len(self.dataset) == 0:
+        res = []
-            self.shuffle()
 
-        index = self.indexes[i % len(self.indexes)]
+        for j in range(self.batch_size):
-        entry = self.dataset[index]
+            position = i * self.batch_size + j
+            if position % len(self.indexes) == 0:
+                self.shuffle()
 
-        if entry.cond is None:
+            index = self.indexes[position % len(self.indexes)]
-            entry.cond_text = self.create_text(entry.filename_text)
+            entry = self.dataset[index]
 
-        return entry
+            if entry.cond is None:
+                entry.cond_text = self.create_text(entry.filename_text)
+
+            res.append(entry)
+
+        return res

modules/textual_inversion/textual_inversion.py

@@ -88,9 +88,9 @@ class EmbeddingDatabase:
 
             data = []
 
-            if filename.upper().endswith('.PNG'):
+            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
-                if 'sd-ti-embedding' in embed_image.text:
+                if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
                     name = data.get('name', name)
                 else:
@@ -199,7 +199,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         })
 
 
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -231,7 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     with torch.autocast("cuda"):
-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
 
     hijack = sd_hijack.model_hijack
 
@@ -242,6 +242,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    embedding_yet_to_be_embedded = False
 
     ititial_step = embedding.step or 0
     if ititial_step > steps:
@@ -251,7 +252,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entry in pbar:
+    for i, entries in pbar:
         embedding.step = i + ititial_step
 
         scheduler.apply(optimizer, embedding.step)
@@ -262,10 +263,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text])
+            c = cond_model([entry.cond_text for entry in entries])
-
+            x = torch.stack([entry.latent for entry in entries]).to(devices.device)
-            x = entry.latent.to(devices.device)
+            loss = shared.sd_model(x, c)[0]
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
             del x
 
             losses[embedding.step % losses.shape[0]] = loss.item()
@@ -282,6 +282,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
             embedding.save(last_saved_file)
+            embedding_yet_to_be_embedded = True
 
         write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
             "loss": f"{losses.mean():.7f}",
@@ -307,7 +308,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 p.width = preview_width
                 p.height = preview_height
             else:
-                p.prompt = entry.cond_text
+                p.prompt = entries[0].cond_text
                 p.steps = 20
                 p.width = training_width
                 p.height = training_height
@@ -319,7 +320,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
             shared.state.current_image = image
 
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file):
+            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
 
                 last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
 
@@ -328,15 +329,22 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
                 info.add_text("sd-ti-embedding", embedding_to_b64(data))
 
                 title = "<{}>".format(data.get('name', '???'))
+
+                try:
+                    vectorSize = list(data['string_to_param'].values())[0].shape[0]
+                except Exception as e:
+                    vectorSize = '?'
+
                 checkpoint = sd_models.select_checkpoint()
                 footer_left = checkpoint.model_name
                 footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}'.format(embedding.step)
+                footer_right = '{}v {}s'.format(vectorSize, embedding.step)
 
                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                 captioned_image = insert_image_data_embed(captioned_image, data)
 
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
+                embedding_yet_to_be_embedded = False
 
             image.save(last_saved_image)
 
@@ -348,7 +356,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 <p>
 Loss: {losses.mean():.7f}<br/>
 Step: {embedding.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entries[0].cond_text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>

modules/ui.py

@@ -7,6 +7,7 @@ import mimetypes
 import os
 import random
 import sys
+import tempfile
 import time
 import traceback
 import platform
@@ -80,6 +81,8 @@ art_symbol = '\U0001f3a8'  # 🎨
 paste_symbol = '\u2199\ufe0f'  # ↙
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
+save_style_symbol = '\U0001f4be'  # 💾
+apply_style_symbol = '\U0001f4cb'  # 📋
 
 
 def plaintext_to_html(text):
@@ -88,6 +91,14 @@ def plaintext_to_html(text):
 
 
 def image_from_url_text(filedata):
+    if type(filedata) == dict and filedata["is_file"]:
+        filename = filedata["name"]
+        tempdir = os.path.normpath(tempfile.gettempdir())
+        normfn = os.path.normpath(filename)
+        assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
+
+        return Image.open(filename)
+
     if type(filedata) == list:
         if len(filedata) == 0:
             return None
@@ -143,10 +154,7 @@ def save_files(js_data, images, do_make_zip, index):
             writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
 
         for image_index, filedata in enumerate(images, start_index):
-            if filedata.startswith("data:image/png;base64,"):
+            image = image_from_url_text(filedata)
-                filedata = filedata[len("data:image/png;base64,"):]
-
-            image = Image.open(io.BytesIO(base64.decodebytes(filedata.encode('utf-8'))))
 
             is_grid = image_index < p.index_of_first_image
             i = 0 if is_grid else (image_index - p.index_of_first_image)
@@ -176,6 +184,23 @@ def save_files(js_data, images, do_make_zip, index):
     return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
 
 
+def save_pil_to_file(pil_image, dir=None):
+    use_metadata = False
+    metadata = PngImagePlugin.PngInfo()
+    for key, value in pil_image.info.items():
+        if isinstance(key, str) and isinstance(value, str):
+            metadata.add_text(key, value)
+            use_metadata = True
+
+    file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
+    pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
+    return file_obj
+
+
+# override save to file function so that it also writes PNG info
+gr.processing_utils.save_pil_to_file = save_pil_to_file
+
+
 def wrap_gradio_call(func, extra_outputs=None):
     def f(*args, extra_outputs_array=extra_outputs, **kwargs):
         run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
@@ -304,7 +329,7 @@ def visit(x, func, path=""):
 
 def add_style(name: str, prompt: str, negative_prompt: str):
     if name is None:
-        return [gr_show(), gr_show()]
+        return [gr_show() for x in range(4)]
 
     style = modules.styles.PromptStyle(name, prompt, negative_prompt)
     shared.prompt_styles.styles[style.name] = style
@@ -429,29 +454,38 @@ def create_toprow(is_img2img):
     id_part = "img2img" if is_img2img else "txt2img"
 
     with gr.Row(elem_id="toprow"):
-        with gr.Column(scale=4):
+        with gr.Column(scale=6):
             with gr.Row():
                 with gr.Column(scale=80):
                     with gr.Row():
-                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, placeholder="Prompt", lines=2)
+                        prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2, 
-                with gr.Column(scale=1, elem_id="roll_col"):
+                            placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                    roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+                        )
-                    paste = gr.Button(value=paste_symbol, elem_id="paste")
-                    token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
-                    token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
-
-                with gr.Column(scale=10, 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())), visible=len(shared.prompt_styles.styles) > 1)
 
             with gr.Row():
-                with gr.Column(scale=8):
+                with gr.Column(scale=80):
                     with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
+                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2, 
-                with gr.Column(scale=1, elem_id="roll_col"):
+                            placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
-                    sh = gr.Button(elem_id="sh", visible=True)                           
+                        )
 
-                with gr.Column(scale=1, elem_id="style_neg_col"):
+        with gr.Column(scale=1, elem_id="roll_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())), visible=len(shared.prompt_styles.styles) > 1)
+            roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+            paste = gr.Button(value=paste_symbol, elem_id="paste")
+            save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
+            prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
+
+            token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
+            token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
+
+        button_interrogate = None
+        button_deepbooru = None
+        if is_img2img:
+            with gr.Column(scale=1, elem_id="interrogate_col"):
+                button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+
+                if cmd_opts.deepdanbooru:
+                    button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
 
         with gr.Column(scale=1):
             with gr.Row():
@@ -471,20 +505,14 @@ def create_toprow(is_img2img):
                     outputs=[],
                 )
 
-            with gr.Row(scale=1):
+            with gr.Row():
-                if is_img2img:
+                with gr.Column(scale=1, elem_id="style_pos_col"):
-                    interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
+                    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())))
-                    if cmd_opts.deepdanbooru:
-                        deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
-                    else:
-                        deepbooru = None
-                else:
-                    interrogate = None
-                    deepbooru = None
-                prompt_style_apply = gr.Button('Apply style', elem_id="style_apply")
-                save_style = gr.Button('Create style', elem_id="style_create")
 
-    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, interrogate, deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
+                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())))
+
+    return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
 
 
 def setup_progressbar(progressbar, preview, id_part, textinfo=None):
@@ -588,7 +616,7 @@ def create_ui(wrap_gradio_gpu_call):
                     txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
                     txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
 
-                with gr.Group():
+                with gr.Column():
                     with gr.Row():
                         save = gr.Button('Save')
                         send_to_img2img = gr.Button('Send to img2img')
@@ -744,10 +772,10 @@ def create_ui(wrap_gradio_gpu_call):
 
                 with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
                     with gr.TabItem('img2img', id='img2img'):
-                        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)
+                        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).style(height=480)
 
                     with gr.TabItem('Inpaint', id='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")
+                        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)
 
                         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")
@@ -803,7 +831,7 @@ def create_ui(wrap_gradio_gpu_call):
                     img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
                     img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
 
-                with gr.Group():
+                with gr.Column():
                     with gr.Row():
                         save = gr.Button('Save')
                         img2img_send_to_img2img = gr.Button('Send to img2img')
@@ -1166,6 +1194,7 @@ def create_ui(wrap_gradio_gpu_call):
                     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()])
                     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")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
@@ -1244,6 +1273,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 train_embedding_name,
                 learn_rate,
+                batch_size,
                 dataset_directory,
                 log_directory,
                 training_width,
@@ -1268,6 +1298,7 @@ def create_ui(wrap_gradio_gpu_call):
             inputs=[
                 train_hypernetwork_name,
                 learn_rate,
+                batch_size,
                 dataset_directory,
                 log_directory,
                 steps,

requirements.txt

@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.4.1
+gradio==3.5
 invisible-watermark
 numpy
 omegaconf

requirements_versions.txt

@@ -2,7 +2,7 @@ transformers==4.19.2
 diffusers==0.3.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.4.1
+gradio==3.5
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0

script.js

@@ -50,9 +50,9 @@ document.addEventListener("DOMContentLoaded", function() {
  document.addEventListener('keydown', function(e) {
     var handled = false;
     if (e.key !== undefined) {
-        if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true;
+        if((e.key == "Enter" && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
     } else if (e.keyCode !== undefined) {
-        if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
+        if((e.keyCode == 13 && (e.metaKey || e.ctrlKey || e.altKey))) handled = true;
     }
     if (handled) {
         button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');

scripts/prompts_from_file.py

@@ -1,7 +1,9 @@
+import copy
 import math
 import os
 import sys
 import traceback
+import shlex
 
 import modules.scripts as scripts
 import gradio as gr
@@ -10,6 +12,75 @@ from modules.processing import Processed, process_images
 from PIL import Image
 from modules.shared import opts, cmd_opts, state
 
+
+def process_string_tag(tag):
+    return tag
+
+
+def process_int_tag(tag):
+    return int(tag)
+
+
+def process_float_tag(tag):
+    return float(tag)
+
+
+def process_boolean_tag(tag):
+    return True if (tag == "true") else False
+
+
+prompt_tags = {
+    "sd_model": None,
+    "outpath_samples": process_string_tag,
+    "outpath_grids": process_string_tag,
+    "prompt_for_display": process_string_tag,
+    "prompt": process_string_tag,
+    "negative_prompt": process_string_tag,
+    "styles": process_string_tag,
+    "seed": process_int_tag,
+    "subseed_strength": process_float_tag,
+    "subseed": process_int_tag,
+    "seed_resize_from_h": process_int_tag,
+    "seed_resize_from_w": process_int_tag,
+    "sampler_index": process_int_tag,
+    "batch_size": process_int_tag,
+    "n_iter": process_int_tag,
+    "steps": process_int_tag,
+    "cfg_scale": process_float_tag,
+    "width": process_int_tag,
+    "height": process_int_tag,
+    "restore_faces": process_boolean_tag,
+    "tiling": process_boolean_tag,
+    "do_not_save_samples": process_boolean_tag,
+    "do_not_save_grid": process_boolean_tag
+}
+
+
+def cmdargs(line):
+    args = shlex.split(line)
+    pos = 0
+    res = {}
+
+    while pos < len(args):
+        arg = args[pos]
+
+        assert arg.startswith("--"), f'must start with "--": {arg}'
+        tag = arg[2:]
+
+        func = prompt_tags.get(tag, None)
+        assert func, f'unknown commandline option: {arg}'
+
+        assert pos+1 < len(args), f'missing argument for command line option {arg}'
+
+        val = args[pos+1]
+
+        res[tag] = func(val)
+
+        pos += 2
+
+    return res
+
+
 class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
@@ -32,26 +103,48 @@ class Script(scripts.Script):
         return [ gr.Checkbox.update(visible = True), gr.File.update(visible = not checkbox_txt), gr.TextArea.update(visible = checkbox_txt) ]
 
     def run(self, p, checkbox_txt, data: bytes, prompt_txt: str):
-        if (checkbox_txt):
+        if checkbox_txt:
             lines = [x.strip() for x in prompt_txt.splitlines()]
         else:
             lines = [x.strip() for x in data.decode('utf8', errors='ignore').split("\n")]
         lines = [x for x in lines if len(x) > 0]
 
-        img_count = len(lines) * p.n_iter
-        batch_count = math.ceil(img_count / p.batch_size)
-        loop_count = math.ceil(batch_count / p.n_iter)
-        print(f"Will process {img_count} images in {batch_count} batches.")
-
         p.do_not_save_grid = True
 
-        state.job_count = batch_count
+        job_count = 0
+        jobs = []
+
+        for line in lines:
+            if "--" in line:
+                try:
+                    args = cmdargs(line)
+                except Exception:
+                    print(f"Error parsing line [line] as commandline:", file=sys.stderr)
+                    print(traceback.format_exc(), file=sys.stderr)
+                    args = {"prompt": line}
+            else:
+                args = {"prompt": line}
+
+            n_iter = args.get("n_iter", 1)
+            if n_iter != 1:
+                job_count += n_iter
+            else:
+                job_count += 1
+
+            jobs.append(args)
+
+        print(f"Will process {len(lines)} lines in {job_count} jobs.")
+        state.job_count = job_count
 
         images = []
-        for loop_no in range(loop_count):
+        for n, args in enumerate(jobs):
-            state.job = f"{loop_no + 1} out of {loop_count}"
+            state.job = f"{state.job_no + 1} out of {state.job_count}"
-            p.prompt = lines[loop_no*p.batch_size:(loop_no+1)*p.batch_size] * p.n_iter
+
-            proc = process_images(p)
+            copy_p = copy.copy(p)
+            for k, v in args.items():
+                setattr(copy_p, k, v)
+
+            proc = process_images(copy_p)
             images += proc.images
 
         return Processed(p, images, p.seed, "")

scripts/xy_grid.py

@@ -12,7 +12,7 @@ import gradio as gr
 
 from modules import images
 from modules.hypernetworks import hypernetwork
-from modules.processing import process_images, Processed, get_correct_sampler
+from modules.processing import process_images, Processed, get_correct_sampler, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
 import modules.sd_samplers
@@ -354,6 +354,9 @@ class Script(scripts.Script):
         else:
             total_steps = p.steps * len(xs) * len(ys)
 
+        if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:
+            total_steps *= 2
+
         print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
         shared.total_tqdm.updateTotal(total_steps * p.n_iter)
 

style.css

@@ -115,7 +115,7 @@
     padding: 0.4em 0;
 }
 
-#roll, #paste{
+#roll, #paste, #style_create, #style_apply{
     min-width: 2em;
     min-height: 2em;
     max-width: 2em;
@@ -126,14 +126,14 @@
     margin: 0.1em 0;
 }
 
-#style_apply, #style_create, #interrogate{
+#interrogate_col{
-    margin: 0.75em 0.25em 0.25em 0.25em;
+    min-width: 0 !important;
-    min-width: 5em;
+    max-width: 8em !important;
 }
-
+#interrogate, #deepbooru{
-#style_apply, #style_create, #deepbooru{
+    margin: 0em 0.25em 0.9em 0.25em;
-    margin: 0.75em 0.25em 0.25em 0.25em;
+    min-width: 8em;
-    min-width: 5em;
+    max-width: 8em;
 }
 
 #style_pos_col, #style_neg_col{
@@ -167,10 +167,6 @@ button{
     align-self: stretch !important;
 }
 
-#img2maskimg .h-60{
-    height: 30rem;
-}
-
 .overflow-hidden, .gr-panel{
     overflow: visible !important;
 }
@@ -241,13 +237,6 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
     margin: 0;
 }
 
-.gr-panel div.flex-col div.justify-between div{
-  position: absolute;
-  top: -0.1em;
-  right: 1em;
-  padding: 0 0.5em;
-}
-
 #settings .gr-panel div.flex-col div.justify-between div{
     position: relative;
     z-index: 200;
@@ -320,6 +309,8 @@ input[type="range"]{
   height: 100%;
   overflow: auto;
   background-color: rgba(20, 20, 20, 0.95);
+  user-select: none;
+  -webkit-user-select: none;
 }
 
 .modalControls {
@@ -443,10 +434,6 @@ input[type="range"]{
     --tw-bg-opacity: 0 !important;
 }
 
-#img2img_image div.h-60{
-    height: 480px;
-}
-
 #context-menu{
     z-index:9999;
     position:absolute;
@@ -521,3 +508,11 @@ canvas[key="mask"] {
 .row.gr-compact{
     overflow: visible;
 }
+
+#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
+{
+    height: 480px !important;
+    max-height: 480px !important;
+    min-height: 480px !important;
+}

webui.sh

@@ -82,8 +82,8 @@ then
     clone_dir="${PWD##*/}"
 fi
 
-# Check prequisites
+# Check prerequisites
-for preq in git python3
+for preq in "${GIT}" "${python_cmd}"
 do
     if ! hash "${preq}" &>/dev/null
     then