Merge branch 'master' into patch-1

javascript/generationParams.js

@@ -0,0 +1,33 @@
+// attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
+
+let txt2img_gallery, img2img_gallery, modal = undefined;
+onUiUpdate(function(){
+	if (!txt2img_gallery) {
+		txt2img_gallery = attachGalleryListeners("txt2img")
+	}
+	if (!img2img_gallery) {
+		img2img_gallery = attachGalleryListeners("img2img")
+	}
+	if (!modal) {
+		modal = gradioApp().getElementById('lightboxModal')
+		modalObserver.observe(modal,  { attributes : true, attributeFilter : ['style'] });
+	}
+});
+
+let modalObserver = new MutationObserver(function(mutations) {
+	mutations.forEach(function(mutationRecord) {
+		let selectedTab = gradioApp().querySelector('#tabs div button.bg-white')?.innerText
+		if (mutationRecord.target.style.display === 'none' && selectedTab === 'txt2img' || selectedTab === 'img2img')
+			gradioApp().getElementById(selectedTab+"_generation_info_button").click()
+	});
+});
+
+function attachGalleryListeners(tab_name) {
+	gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
+	gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
+	gallery?.addEventListener('keydown', (e) => {
+		if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
+			gradioApp().getElementById(tab_name+"_generation_info_button").click()
+	});
+	return gallery;
+}

modules/api/api.py

@@ -15,6 +15,9 @@ from modules.sd_models import checkpoints_list
 from modules.realesrgan_model import get_realesrgan_models
 from typing import List
 
+if shared.cmd_opts.deepdanbooru:
+    from modules.deepbooru import get_deepbooru_tags
+
 def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
@@ -220,11 +223,20 @@ class Api:
         if image_b64 is None:
             raise HTTPException(status_code=404, detail="Image not found") 
 
-        img = self.__base64_to_image(image_b64)
+        img = decode_base64_to_image(image_b64)
+        img = img.convert('RGB')
 
         # Override object param
         with self.queue_lock:
-            processed = shared.interrogator.interrogate(img)
+            if interrogatereq.model == "clip":
+                processed = shared.interrogator.interrogate(img)
+            elif interrogatereq.model == "deepdanbooru":
+                if shared.cmd_opts.deepdanbooru:
+                    processed = get_deepbooru_tags(img)
+                else:
+                    raise HTTPException(status_code=404, detail="Model not found. Add --deepdanbooru when launching for using the model.")
+            else:
+                raise HTTPException(status_code=404, detail="Model not found")
         
         return InterrogateResponse(caption=processed)
 

modules/api/models.py

@@ -170,6 +170,7 @@ class ProgressResponse(BaseModel):
 
 class InterrogateRequest(BaseModel):
     image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.")
+    model: str = Field(default="clip", title="Model", description="The interrogate model used.")
 
 class InterrogateResponse(BaseModel):
     caption: str = Field(default=None, title="Caption", description="The generated caption for the image.")

modules/ngrok.py

@@ -1,14 +1,23 @@
 from pyngrok import ngrok, conf, exception
 
-
 def connect(token, port, region):
+    account = None
     if token == None:
         token = 'None'
+    else:
+        if ':' in token:
+            # token = authtoken:username:password
+            account = token.split(':')[1] + ':' + token.split(':')[-1]
+            token = token.split(':')[0]
+
     config = conf.PyngrokConfig(
         auth_token=token, region=region
     )
     try:
-        public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
+        if account == None:
+            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True).public_url
+        else:
+            public_url = ngrok.connect(port, pyngrok_config=config, bind_tls=True, auth=account).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/sd_models.py

@@ -163,13 +163,21 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     checkpoint_file = checkpoint_info.filename
     sd_model_hash = checkpoint_info.hash
 
-    if shared.opts.sd_checkpoint_cache > 0 and hasattr(model, "sd_checkpoint_info"):
+    cache_enabled = shared.opts.sd_checkpoint_cache > 0
+
+    if cache_enabled:
         sd_vae.restore_base_vae(model)
-        checkpoints_loaded[model.sd_checkpoint_info] = model.state_dict().copy()
 
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
 
-    if checkpoint_info not in checkpoints_loaded:
+    if cache_enabled and checkpoint_info in checkpoints_loaded:
+        # use checkpoint cache
+        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
+        model.load_state_dict(checkpoints_loaded[checkpoint_info])
+    else:
+        # load from file
         print(f"Loading weights [{sd_model_hash}] from {checkpoint_file}")
 
         pl_sd = torch.load(checkpoint_file, map_location=shared.weight_load_location)
@@ -180,6 +188,10 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
         del pl_sd
         model.load_state_dict(sd, strict=False)
         del sd
+        
+        if cache_enabled:
+            # cache newly loaded model
+            checkpoints_loaded[checkpoint_info] = model.state_dict().copy()
 
         if shared.cmd_opts.opt_channelslast:
             model.to(memory_format=torch.channels_last)
@@ -199,14 +211,9 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
 
         model.first_stage_model.to(devices.dtype_vae)
 
-    else:
+    # clean up cache if limit is reached
-        vae_name = sd_vae.get_filename(vae_file) if vae_file else None
+    if cache_enabled:
-        vae_message = f" with {vae_name} VAE" if vae_name else ""
+        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache + 1: # we need to count the current model
-        print(f"Loading weights [{sd_model_hash}]{vae_message} from cache")
-        model.load_state_dict(checkpoints_loaded[checkpoint_info])
-
-    if shared.opts.sd_checkpoint_cache > 0:
-        while len(checkpoints_loaded) > shared.opts.sd_checkpoint_cache:
             checkpoints_loaded.popitem(last=False)  # LRU
 
     model.sd_model_hash = sd_model_hash

modules/shared.py

@@ -319,6 +319,8 @@ options_templates.update(options_section(('system', "System"), {
 
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
+    "shuffle_tags": OptionInfo(False, "Shuffleing tags by ',' when create texts."),
+    "tag_drop_out": OptionInfo(0, "Dropout tags when create texts", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.1}),
     "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

modules/textual_inversion/dataset.py

@@ -98,7 +98,12 @@ class PersonalizedBase(Dataset):
     def create_text(self, filename_text):
         text = random.choice(self.lines)
         text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", filename_text)
+        tags = filename_text.split(',')
+        if shared.opts.tag_drop_out != 0:
+            tags = [t for t in tags if random.random() > shared.opts.tag_drop_out]
+        if shared.opts.shuffle_tags:
+            random.shuffle(tags)
+        text = text.replace("[filewords]", ','.join(tags))
         return text
 
     def __len__(self):

modules/ui.py

@@ -566,6 +566,19 @@ def apply_setting(key, value):
     return value
 
 
+def update_generation_info(args):
+    generation_info, html_info, img_index = args
+    try:
+        generation_info = json.loads(generation_info)
+        if img_index < 0 or img_index >= len(generation_info["infotexts"]):
+            return html_info
+        return plaintext_to_html(generation_info["infotexts"][img_index])
+    except Exception:
+        pass
+    # if the json parse or anything else fails, just return the old html_info
+    return html_info
+
+
 def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
     def refresh():
         refresh_method()
@@ -638,6 +651,15 @@ Requested path was: {f}
                     with gr.Group():
                         html_info = gr.HTML()
                         generation_info = gr.Textbox(visible=False)
+                        if tabname == 'txt2img' or tabname == 'img2img':
+                            generation_info_button = gr.Button(visible=False, elem_id=f"{tabname}_generation_info_button")
+                            generation_info_button.click(
+                                fn=update_generation_info,
+                                _js="(x, y) => [x, y, selected_gallery_index()]",
+                                inputs=[generation_info, html_info],
+                                outputs=[html_info],
+                                preprocess=False
+                            )
 
                         save.click(
                             fn=wrap_gradio_call(save_files),

scripts/prompt_matrix.py

@@ -80,6 +80,8 @@ class Script(scripts.Script):
         grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
         grid = images.draw_prompt_matrix(grid, p.width, p.height, prompt_matrix_parts)
         processed.images.insert(0, grid)
+        processed.index_of_first_image = 1
+        processed.infotexts.insert(0, processed.infotexts[0])
 
         if opts.grid_save:
             images.save_image(processed.images[0], p.outpath_grids, "prompt_matrix", prompt=original_prompt, seed=processed.seed, grid=True, p=p)

scripts/prompts_from_file.py

@@ -145,6 +145,8 @@ class Script(scripts.Script):
         state.job_count = job_count
 
         images = []
+        all_prompts = []
+        infotexts = []
         for n, args in enumerate(jobs):
             state.job = f"{state.job_no + 1} out of {state.job_count}"
 
@@ -157,5 +159,7 @@ class Script(scripts.Script):
             
             if checkbox_iterate:
                 p.seed = p.seed + (p.batch_size * p.n_iter)
+            all_prompts += proc.all_prompts
+            infotexts += proc.infotexts
 
-        return Processed(p, images, p.seed, "")
+        return Processed(p, images, p.seed, "", all_prompts=all_prompts, infotexts=infotexts)