Merge branch 'master' into varsize

.github/ISSUE_TEMPLATE/feature_request.yml

 name: Feature request
 description: Suggest an idea for this project
 title: "[Feature Request]: "
-labels: ["suggestion"]
+labels: ["enhancement"]
 
 body:
   - type: checkboxes

.github/workflows/on_pull_request.yaml

@@ -19,22 +19,19 @@ jobs:
       - name: Checkout Code
         uses: actions/checkout@v3
       - name: Set up Python 3.10
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: 3.10.6
-      - uses: actions/cache@v2
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
-          restore-keys: |
-            ${{ runner.os }}-pip-
+          cache: pip
+          cache-dependency-path: |
+            **/requirements*txt
       - 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 : |
+        run: |
           export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
           python launch.py
       - name: Analysing the code with pylint

.github/workflows/run_tests.yaml

@@ -14,11 +14,9 @@ jobs:
         uses: actions/setup-python@v4
         with:
           python-version: 3.10.6
-      - uses: actions/cache@v3
-        with:
-          path: ~/.cache/pip
-          key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
-          restore-keys: ${{ runner.os }}-pip-
+          cache: pip
+          cache-dependency-path: |
+            **/requirements*txt
       - name: Run tests
         run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
       - name: Upload main app stdout-stderr

javascript/hints.js

@@ -4,7 +4,7 @@ titles = {
     "Sampling steps": "How many times to improve the generated image iteratively; higher values take longer; very low values can produce bad results",
     "Sampling method": "Which algorithm to use to produce the image",
 	"GFPGAN": "Restore low quality faces using GFPGAN neural network",
-	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help",
+	"Euler a": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps higher than 30-40 does not help",
 	"DDIM": "Denoising Diffusion Implicit Models - best at inpainting",
 	"DPM adaptive": "Ignores step count - uses a number of steps determined by the CFG and resolution", 
 
@@ -74,7 +74,7 @@ titles = {
     "Style 1": "Style to apply; styles have components for both positive and negative prompts and apply to both",
     "Style 2": "Style to apply; styles have components for both positive and negative prompts and apply to both",
     "Apply style": "Insert selected styles into prompt fields",
-    "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style use that as placeholder for your prompt when you use the style in the future.",
+    "Create style": "Save current prompts as a style. If you add the token {prompt} to the text, the style uses that as a placeholder for your prompt when you use the style in the future.",
 
     "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.",
     "Inpainting conditioning mask strength": "Only applies to inpainting models. Determines how strongly to mask off the original image for inpainting and img2img. 1.0 means fully masked, which is the default behaviour. 0.0 means a fully unmasked conditioning. Lower values will help preserve the overall composition of the image, but will struggle with large changes.",
@@ -92,12 +92,12 @@ titles = {
     "Weighted sum": "Result = A * (1 - M) + B * M",
     "Add difference": "Result = A + (B - C) * M",
 
-    "Learning rate": "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
+    "Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n   rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG:   0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",
 
     "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
 
-    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resoluton and lower quality.",
-    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality.",
+    "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resolution and lower quality.",
+    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resolution and extremely low quality.",
 
     "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
     "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",

javascript/hires_fix.js

+
+function setInactive(elem, inactive){
+    console.log(elem)
+    if(inactive){
+        elem.classList.add('inactive')
+    } else{
+        elem.classList.remove('inactive')
+    }
+}
+
+function onCalcResolutionHires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y){
+    console.log(enable, width, height, hr_scale, hr_resize_x, hr_resize_y)
+
+    hrUpscaleBy = gradioApp().getElementById('txt2img_hr_scale')
+    hrResizeX = gradioApp().getElementById('txt2img_hr_resize_x')
+    hrResizeY = gradioApp().getElementById('txt2img_hr_resize_y')
+
+    gradioApp().getElementById('txt2img_hires_fix_row2').style.display = opts.use_old_hires_fix_width_height ? "none" : ""
+
+    setInactive(hrUpscaleBy, opts.use_old_hires_fix_width_height || hr_resize_x > 0 || hr_resize_y > 0)
+    setInactive(hrResizeX, opts.use_old_hires_fix_width_height || hr_resize_x == 0)
+    setInactive(hrResizeY, opts.use_old_hires_fix_width_height || hr_resize_y == 0)
+
+    return [enable, width, height, hr_scale, hr_resize_x, hr_resize_y]
+}

modules/api/api.py

@@ -11,7 +11,7 @@ from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
 import modules.shared as shared
-from modules import sd_samplers, deepbooru, sd_hijack, images
+from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui
 from modules.api.models import *
 from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
 from modules.extras import run_extras
@@ -28,8 +28,13 @@ def upscaler_to_index(name: str):
     try:
         return [x.name.lower() for x in shared.sd_upscalers].index(name.lower())
     except:
-        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be on of these: {' , '.join([x.name for x in sd_upscalers])}")
+        raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}")
 
+def script_name_to_index(name, scripts):
+    try:
+        return [script.title().lower() for script in scripts].index(name.lower())
+    except:
+        raise HTTPException(status_code=422, detail=f"Script '{name}' not found")
 
 def validate_sampler_name(name):
     config = sd_samplers.all_samplers_map.get(name, None)
@@ -143,7 +148,21 @@ class Api:
 
         raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"})
 
+    def get_script(self, script_name, script_runner):
+        if script_name is None:
+            return None, None
+
+        if not script_runner.scripts:
+            script_runner.initialize_scripts(False)
+            ui.create_ui()
+
+        script_idx = script_name_to_index(script_name, script_runner.selectable_scripts)
+        script = script_runner.selectable_scripts[script_idx]
+        return script, script_idx
+
     def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
+        script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img)
+
         populate = txt2imgreq.copy(update={ # Override __init__ params
             "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index),
             "do_not_save_samples": True,
@@ -153,14 +172,22 @@ class Api:
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
 
+        args = vars(populate)
+        args.pop('script_name', None)
+
         with self.queue_lock:
-            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate))
+            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args)
 
             shared.state.begin()
-            processed = process_images(p)
+            if script is not None:
+                p.outpath_grids = opts.outdir_txt2img_grids
+                p.outpath_samples = opts.outdir_txt2img_samples
+                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
+                processed = scripts.scripts_txt2img.run(p, *p.script_args)
+            else:
+                processed = process_images(p)
             shared.state.end()
 
-
         b64images = list(map(encode_pil_to_base64, processed.images))
 
         return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js())
@@ -170,6 +197,8 @@ class Api:
         if init_images is None:
             raise HTTPException(status_code=404, detail="Init image not found")
 
+        script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img)
+
         mask = img2imgreq.mask
         if mask:
             mask = decode_base64_to_image(mask)
@@ -186,13 +215,20 @@ class Api:
 
         args = vars(populate)
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
+        args.pop('script_name', None)
 
         with self.queue_lock:
             p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
             p.init_images = [decode_base64_to_image(x) for x in init_images]
 
             shared.state.begin()
-            processed = process_images(p)
+            if script is not None:
+                p.outpath_grids = opts.outdir_img2img_grids
+                p.outpath_samples = opts.outdir_img2img_samples
+                p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args
+                processed = scripts.scripts_img2img.run(p, *p.script_args)
+            else:
+                processed = process_images(p)
             shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))

modules/api/models.py

@@ -100,13 +100,13 @@ class PydanticModelGenerator:
 StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingTxt2Img",
     StableDiffusionProcessingTxt2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
 ).generate_model()
 
 StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator(
     "StableDiffusionProcessingImg2Img",
     StableDiffusionProcessingImg2Img,
-    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}]
+    [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}]
 ).generate_model()
 
 class TextToImageResponse(BaseModel):

modules/generation_parameters_copypaste.py

@@ -197,6 +197,15 @@ def restore_old_hires_fix_params(res):
     firstpass_width = res.get('First pass size-1', None)
     firstpass_height = res.get('First pass size-2', None)
 
+    if shared.opts.use_old_hires_fix_width_height:
+        hires_width = int(res.get("Hires resize-1", None))
+        hires_height = int(res.get("Hires resize-2", None))
+
+        if hires_width is not None and hires_height is not None:
+            res['Size-1'] = hires_width
+            res['Size-2'] = hires_height
+            return
+
     if firstpass_width is None or firstpass_height is None:
         return
 
@@ -205,12 +214,8 @@ def restore_old_hires_fix_params(res):
     height = int(res.get("Size-2", 512))
 
     if firstpass_width == 0 or firstpass_height == 0:
-        # old algorithm for auto-calculating first pass size
-        desired_pixel_count = 512 * 512
-        actual_pixel_count = width * height
-        scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-        firstpass_width = math.ceil(scale * width / 64) * 64
-        firstpass_height = math.ceil(scale * height / 64) * 64
+        from modules import processing
+        firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)
 
     res['Size-1'] = firstpass_width
     res['Size-2'] = firstpass_height

modules/processing.py

@@ -98,7 +98,7 @@ class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
     """
-    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None):
+    def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
         if sampler_index is not None:
             print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
 
@@ -149,7 +149,7 @@ class StableDiffusionProcessing():
             self.seed_resize_from_w = 0
 
         self.scripts = None
-        self.script_args = None
+        self.script_args = script_args
         self.all_prompts = None
         self.all_negative_prompts = None
         self.all_seeds = None
@@ -687,6 +687,18 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
     return res
 
 
+def old_hires_fix_first_pass_dimensions(width, height):
+    """old algorithm for auto-calculating first pass size"""
+
+    desired_pixel_count = 512 * 512
+    actual_pixel_count = width * height
+    scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+    width = math.ceil(scale * width / 64) * 64
+    height = math.ceil(scale * height / 64) * 64
+
+    return width, height
+
+
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
@@ -703,16 +715,26 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         self.hr_upscale_to_y = hr_resize_y
 
         if firstphase_width != 0 or firstphase_height != 0:
-            print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr)
-            self.hr_scale = self.width / firstphase_width
+            self.hr_upscale_to_x = self.width
+            self.hr_upscale_to_y = self.height
             self.width = firstphase_width
             self.height = firstphase_height
 
         self.truncate_x = 0
         self.truncate_y = 0
+        self.applied_old_hires_behavior_to = None
 
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
+            if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
+                self.hr_resize_x = self.width
+                self.hr_resize_y = self.height
+                self.hr_upscale_to_x = self.width
+                self.hr_upscale_to_y = self.height
+
+                self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
+                self.applied_old_hires_behavior_to = (self.width, self.height)
+
             if self.hr_resize_x == 0 and self.hr_resize_y == 0:
                 self.extra_generation_params["Hires upscale"] = self.hr_scale
                 self.hr_upscale_to_x = int(self.width * self.hr_scale)

modules/sd_hijack.py

@@ -83,10 +83,12 @@ class StableDiffusionModelHijack:
     clip = None
     optimization_method = None
 
-    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
+    embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
 
-    def hijack(self, m):
+    def __init__(self):
+        self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
 
+    def hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             model_embeddings = m.cond_stage_model.roberta.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
@@ -117,7 +119,6 @@ class StableDiffusionModelHijack:
         self.layers = flatten(m)
 
     def undo_hijack(self, m):
-
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             m.cond_stage_model = m.cond_stage_model.wrapped 
 

modules/sd_hijack_clip.py

@@ -247,9 +247,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
         # restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
         batch_multipliers = torch.asarray(batch_multipliers).to(devices.device)
         original_mean = z.mean()
-        z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
+        z = z * batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
         new_mean = z.mean()
-        z *= original_mean / new_mean
+        z = z * (original_mean / new_mean)
 
         return z
 

modules/sd_vae.py

 import torch
+import safetensors.torch
 import os
 import collections
 from collections import namedtuple
-from modules import shared, devices, script_callbacks
+from modules import shared, devices, script_callbacks, sd_models
 from modules.paths import models_path
 import glob
 from copy import deepcopy
@@ -72,8 +73,10 @@ def refresh_vae_list(vae_path=vae_path, model_path=model_path):
     candidates = [
         *glob.iglob(os.path.join(model_path, '**/*.vae.ckpt'), recursive=True),
         *glob.iglob(os.path.join(model_path, '**/*.vae.pt'), recursive=True),
+        *glob.iglob(os.path.join(model_path, '**/*.vae.safetensors'), recursive=True),
         *glob.iglob(os.path.join(vae_path, '**/*.ckpt'), recursive=True),
-        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True)
+        *glob.iglob(os.path.join(vae_path, '**/*.pt'), recursive=True),
+        *glob.iglob(os.path.join(vae_path, '**/*.safetensors'), recursive=True),
     ]
     if shared.cmd_opts.vae_path is not None and os.path.isfile(shared.cmd_opts.vae_path):
         candidates.append(shared.cmd_opts.vae_path)
@@ -137,6 +140,12 @@ def resolve_vae(checkpoint_file=None, vae_file="auto"):
         if os.path.isfile(vae_file_try):
             vae_file = vae_file_try
             print(f"Using VAE found similar to selected model: {vae_file}")
+    # if still not found, try look for ".vae.safetensors" beside model
+    if vae_file == "auto":
+        vae_file_try = model_path + ".vae.safetensors"
+        if os.path.isfile(vae_file_try):
+            vae_file = vae_file_try
+            print(f"Using VAE found similar to selected model: {vae_file}")
     # No more fallbacks for auto
     if vae_file == "auto":
         vae_file = None
@@ -163,8 +172,9 @@ def load_vae(model, vae_file=None):
             assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
             print(f"Loading VAE weights from: {vae_file}")
             store_base_vae(model)
-            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-            vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+
+            vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
+            vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
             _load_vae_dict(model, vae_dict_1)
 
             if cache_enabled:
@@ -195,10 +205,12 @@ def _load_vae_dict(model, vae_dict_1):
     model.first_stage_model.load_state_dict(vae_dict_1)
     model.first_stage_model.to(devices.dtype_vae)
 
+
 def clear_loaded_vae():
     global loaded_vae_file
     loaded_vae_file = None
 
+
 def reload_vae_weights(sd_model=None, vae_file="auto"):
     from modules import lowvram, devices, sd_hijack
 

modules/shared.py

@@ -398,6 +398,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
 options_templates.update(options_section(('compatibility', "Compatibility"), {
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
+    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
 }))
 
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {

modules/sub_quadratic_attention.py

@@ -15,7 +15,8 @@ import torch
 from torch import Tensor
 from torch.utils.checkpoint import checkpoint
 import math
-from typing import Optional, NamedTuple, Protocol, List
+from typing import Optional, NamedTuple, List
+
 
 def narrow_trunc(
     input: Tensor,
@@ -25,12 +26,14 @@ def narrow_trunc(
 ) -> Tensor:
     return torch.narrow(input, dim, start, length if input.shape[dim] >= start + length else input.shape[dim] - start)
 
+
 class AttnChunk(NamedTuple):
     exp_values: Tensor
     exp_weights_sum: Tensor
     max_score: Tensor
 
-class SummarizeChunk(Protocol):
+
+class SummarizeChunk:
     @staticmethod
     def __call__(
         query: Tensor,
@@ -38,7 +41,8 @@ class SummarizeChunk(Protocol):
         value: Tensor,
     ) -> AttnChunk: ...
 
-class ComputeQueryChunkAttn(Protocol):
+
+class ComputeQueryChunkAttn:
     @staticmethod
     def __call__(
         query: Tensor,
@@ -46,6 +50,7 @@ class ComputeQueryChunkAttn(Protocol):
         value: Tensor,
     ) -> Tensor: ...
 
+
 def _summarize_chunk(
     query: Tensor,
     key: Tensor,
@@ -66,6 +71,7 @@ def _summarize_chunk(
     max_score = max_score.squeeze(-1)
     return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
 
+
 def _query_chunk_attention(
     query: Tensor,
     key: Tensor,
@@ -106,6 +112,7 @@ def _query_chunk_attention(
     all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
     return all_values / all_weights
 
+
 # TODO: refactor CrossAttention#get_attention_scores to share code with this
 def _get_attention_scores_no_kv_chunking(
     query: Tensor,
@@ -125,10 +132,12 @@ def _get_attention_scores_no_kv_chunking(
     hidden_states_slice = torch.bmm(attn_probs, value)
     return hidden_states_slice
 
+
 class ScannedChunk(NamedTuple):
     chunk_idx: int
     attn_chunk: AttnChunk
 
+
 def efficient_dot_product_attention(
     query: Tensor,
     key: Tensor,

modules/textual_inversion/textual_inversion.py

@@ -66,17 +66,41 @@ class Embedding:
         return self.cached_checksum
 
 
+class DirWithTextualInversionEmbeddings:
+    def __init__(self, path):
+        self.path = path
+        self.mtime = None
+
+    def has_changed(self):
+        if not os.path.isdir(self.path):
+            return False
+
+        mt = os.path.getmtime(self.path)
+        if self.mtime is None or mt > self.mtime:
+            return True
+
+    def update(self):
+        if not os.path.isdir(self.path):
+            return
+
+        self.mtime = os.path.getmtime(self.path)
+
+
 class EmbeddingDatabase:
-    def __init__(self, embeddings_dir):
+    def __init__(self):
         self.ids_lookup = {}
         self.word_embeddings = {}
         self.skipped_embeddings = {}
-        self.dir_mtime = None
-        self.embeddings_dir = embeddings_dir
         self.expected_shape = -1
+        self.embedding_dirs = {}
 
-    def register_embedding(self, embedding, model):
+    def add_embedding_dir(self, path):
+        self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
+
+    def clear_embedding_dirs(self):
+        self.embedding_dirs.clear()
 
+    def register_embedding(self, embedding, model):
         self.word_embeddings[embedding.name] = embedding
 
         ids = model.cond_stage_model.tokenize([embedding.name])[0]
@@ -93,65 +117,62 @@ class EmbeddingDatabase:
         vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
         return vec.shape[1]
 
-    def load_textual_inversion_embeddings(self, force_reload = False):
-        mt = os.path.getmtime(self.embeddings_dir)
-        if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime:
-            return
+    def load_from_file(self, path, filename):
+        name, ext = os.path.splitext(filename)
+        ext = ext.upper()
 
-        self.dir_mtime = mt
-        self.ids_lookup.clear()
-        self.word_embeddings.clear()
-        self.skipped_embeddings.clear()
-        self.expected_shape = self.get_expected_shape()
-
-        def process_file(path, filename):
-            name, ext = os.path.splitext(filename)
-            ext = ext.upper()
-
-            if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
-                embed_image = Image.open(path)
-                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:
-                    data = extract_image_data_embed(embed_image)
-                    name = data.get('name', name)
-            elif ext in ['.BIN', '.PT']:
-                data = torch.load(path, map_location="cpu")
-            else:
+        if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
+            _, second_ext = os.path.splitext(name)
+            if second_ext.upper() == '.PREVIEW':
                 return
 
-            # textual inversion embeddings
-            if 'string_to_param' in data:
-                param_dict = data['string_to_param']
-                if hasattr(param_dict, '_parameters'):
-                    param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
-                assert len(param_dict) == 1, 'embedding file has multiple terms in it'
-                emb = next(iter(param_dict.items()))[1]
-            # diffuser concepts
-            elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
-                assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
-
-                emb = next(iter(data.values()))
-                if len(emb.shape) == 1:
-                    emb = emb.unsqueeze(0)
-            else:
-                raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
-
-            vec = emb.detach().to(devices.device, dtype=torch.float32)
-            embedding = Embedding(vec, name)
-            embedding.step = data.get('step', None)
-            embedding.sd_checkpoint = data.get('sd_checkpoint', None)
-            embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
-            embedding.vectors = vec.shape[0]
-            embedding.shape = vec.shape[-1]
-
-            if self.expected_shape == -1 or self.expected_shape == embedding.shape:
-                self.register_embedding(embedding, shared.sd_model)
+            embed_image = Image.open(path)
+            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:
-                self.skipped_embeddings[name] = embedding
+                data = extract_image_data_embed(embed_image)
+                name = data.get('name', name)
+        elif ext in ['.BIN', '.PT']:
+            data = torch.load(path, map_location="cpu")
+        else:
+            return
+
+        # textual inversion embeddings
+        if 'string_to_param' in data:
+            param_dict = data['string_to_param']
+            if hasattr(param_dict, '_parameters'):
+                param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11
+            assert len(param_dict) == 1, 'embedding file has multiple terms in it'
+            emb = next(iter(param_dict.items()))[1]
+        # diffuser concepts
+        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
+            assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
+
+            emb = next(iter(data.values()))
+            if len(emb.shape) == 1:
+                emb = emb.unsqueeze(0)
+        else:
+            raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")
+
+        vec = emb.detach().to(devices.device, dtype=torch.float32)
+        embedding = Embedding(vec, name)
+        embedding.step = data.get('step', None)
+        embedding.sd_checkpoint = data.get('sd_checkpoint', None)
+        embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
+        embedding.vectors = vec.shape[0]
+        embedding.shape = vec.shape[-1]
+
+        if self.expected_shape == -1 or self.expected_shape == embedding.shape:
+            self.register_embedding(embedding, shared.sd_model)
+        else:
+            self.skipped_embeddings[name] = embedding
 
-        for root, dirs, fns in os.walk(self.embeddings_dir):
+    def load_from_dir(self, embdir):
+        if not os.path.isdir(embdir.path):
+            return
+
+        for root, dirs, fns in os.walk(embdir.path):
             for fn in fns:
                 try:
                     fullfn = os.path.join(root, fn)
@@ -159,12 +180,32 @@ class EmbeddingDatabase:
                     if os.stat(fullfn).st_size == 0:
                         continue
 
-                    process_file(fullfn, fn)
+                    self.load_from_file(fullfn, fn)
                 except Exception:
                     print(f"Error loading embedding {fn}:", file=sys.stderr)
                     print(traceback.format_exc(), file=sys.stderr)
                     continue
 
+    def load_textual_inversion_embeddings(self, force_reload=False):
+        if not force_reload:
+            need_reload = False
+            for path, embdir in self.embedding_dirs.items():
+                if embdir.has_changed():
+                    need_reload = True
+                    break
+
+            if not need_reload:
+                return
+
+        self.ids_lookup.clear()
+        self.word_embeddings.clear()
+        self.skipped_embeddings.clear()
+        self.expected_shape = self.get_expected_shape()
+
+        for path, embdir in self.embedding_dirs.items():
+            self.load_from_dir(embdir)
+            embdir.update()
+
         print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
         if len(self.skipped_embeddings) > 0:
             print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
@@ -247,11 +288,11 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
     assert os.path.isfile(template_file), "Prompt template file doesn't exist"
     assert steps, "Max steps is empty or 0"
     assert isinstance(steps, int), "Max steps must be integer"
-    assert steps > 0 , "Max steps must be positive"
+    assert steps > 0, "Max steps must be positive"
     assert isinstance(save_model_every, int), "Save {name} must be integer"
-    assert save_model_every >= 0 , "Save {name} must be positive or 0"
+    assert save_model_every >= 0, "Save {name} must be positive or 0"
     assert isinstance(create_image_every, int), "Create image must be integer"
-    assert create_image_every >= 0 , "Create image must be positive or 0"
+    assert create_image_every >= 0, "Create image must be positive or 0"
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 

modules/ui.py

@@ -267,7 +267,7 @@ def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resiz
     with devices.autocast():
         p.init([""], [0], [0])
 
-    return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{p.hr_upscale_to_x}x{p.hr_upscale_to_y}</span>"
+    return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
 
 
 def apply_styles(prompt, prompt_neg, style1_name, style2_name):
@@ -745,15 +745,20 @@ def create_ui():
                             custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
 
             hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
-            hr_resolution_preview_args = dict(
-                fn=calc_resolution_hires,
-                inputs=hr_resolution_preview_inputs,
-                outputs=[hr_final_resolution],
-                show_progress=False
-            )
-
             for input in hr_resolution_preview_inputs:
-                input.change(**hr_resolution_preview_args)
+                input.change(
+                    fn=calc_resolution_hires,
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[hr_final_resolution],
+                    show_progress=False,
+                )
+                input.change(
+                    None,
+                    _js="onCalcResolutionHires",
+                    inputs=hr_resolution_preview_inputs,
+                    outputs=[],
+                    show_progress=False,
+                )
 
             txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
             parameters_copypaste.bind_buttons({"txt2img": txt2img_paste}, None, txt2img_prompt)

scripts/sd_upscale.py

@@ -25,6 +25,8 @@ class Script(scripts.Script):
         return [info, overlap, upscaler_index, scale_factor]
 
     def run(self, p, _, overlap, upscaler_index, scale_factor):
+        if isinstance(upscaler_index, str):
+            upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())
         processing.fix_seed(p)
         upscaler = shared.sd_upscalers[upscaler_index]
 

style.css

@@ -512,7 +512,7 @@ input[type="range"]{
     border: none;
     background: none;
     flex: unset;
-    gap: 0.5em;
+    gap: 1em;
 }
 
 #quicksettings > div > div{
@@ -521,6 +521,17 @@ input[type="range"]{
     padding: 0;
 }
 
+#quicksettings > div > div > div > div > label > span {
+    position: relative;
+    margin-right: 9em;
+    margin-bottom: -1em;
+}
+
+#quicksettings > div > div > label > span {
+    position: relative;
+    margin-bottom: -1em;
+}
+
 canvas[key="mask"] {
     z-index: 12 !important;
     filter: invert();
@@ -659,6 +670,10 @@ footer {
     min-width: auto;
 }
 
+.inactive{
+    opacity: 0.5;
+}
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running

test/basic_features/img2img_test.py

@@ -50,6 +50,12 @@ class TestImg2ImgWorking(unittest.TestCase):
         self.simple_img2img["mask"] = encode_pil_to_base64(Image.open(r"test/test_files/mask_basic.png"))
         self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
 
+    def test_img2img_sd_upscale_performed(self):
+        self.simple_img2img["script_name"] = "sd upscale"
+        self.simple_img2img["script_args"] = ["", 8, "Lanczos", 2.0]
+
+        self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)
+
 
 if __name__ == "__main__":
     unittest.main()