Make cancel generate forever let the current gen complete

.github/ISSUE_TEMPLATE/feature_request.md

@@ -2,7 +2,7 @@
 name: Feature request
 about: Suggest an idea for this project
 title: ''
-labels: 'suggestion'
+labels: ''
 assignees: ''
 
 ---

CODEOWNERS

@@ -1 +0,0 @@
-*       @AUTOMATIC1111

README.md

@@ -28,12 +28,10 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
     - CodeFormer, face restoration tool as an alternative to GFPGAN
     - RealESRGAN, neural network upscaler
     - ESRGAN, neural network upscaler with a lot of third party models
-    - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+    - SwinIR, neural network upscaler
     - LDSR, Latent diffusion super resolution upscaling
 - Resizing aspect ratio options
 - Sampling method selection
-    - Adjust sampler eta values (noise multiplier)
-    - More advanced noise setting options
 - Interrupt processing at any time
 - 4GB video card support (also reports of 2GB working)
 - Correct seeds for batches
@@ -69,7 +67,6 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web
      - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
 - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
 - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args)
-- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args)
 
 ## Installation and Running
 Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
@@ -119,17 +116,13 @@ The documentation was moved from this README over to the project's [wiki](https:
 - CodeFormer - https://github.com/sczhou/CodeFormer
 - ESRGAN - https://github.com/xinntao/ESRGAN
 - SwinIR - https://github.com/JingyunLiang/SwinIR
-- Swin2SR - https://github.com/mv-lab/swin2sr
 - LDSR - https://github.com/Hafiidz/latent-diffusion
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Doggettx - Cross Attention layer optimization - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
-- InvokeAI, lstein - Cross Attention layer optimization - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
 - Rinon Gal - Textual Inversion - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
 - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
-- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
-- xformers - https://github.com/facebookresearch/xformers
-- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
 - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
+- DeepDanbooru - interrogator for anime diffusors https://github.com/KichangKim/DeepDanbooru
 - (You)

artists.csv

@@ -1045,6 +1045,7 @@ Bakemono Zukushi,0.67051035,anime
 Lucy Madox Brown,0.67032814,fineart
 Paul Wonner,0.6700563,scribbles
 Guido Borelli Da Caluso,0.66966087,digipa-high-impact
+Guido Borelli da Caluso,0.66966087,digipa-high-impact
 Emil Alzamora,0.5844039,nudity
 Heinrich Brocksieper,0.64469147,fineart
 Dan Smith,0.669563,digipa-high-impact

environment-wsl2.yaml

@@ -3,9 +3,9 @@ channels:
   - pytorch
   - defaults
 dependencies:
-  - python=3.10
-  - pip=22.2.2
+  - python=3.8.5
+  - pip=20.3
   - cudatoolkit=11.3
-  - pytorch=1.12.1
-  - torchvision=0.13.1
-  - numpy=1.23.1
+  - pytorch=1.11.0
+  - torchvision=0.12.0
+  - numpy=1.19.2

javascript/contextMenus.js

@@ -16,7 +16,7 @@ contextMenuInit = function(){
       oldMenu.remove()
     }
 
-    let tabButton = uiCurrentTab
+    let tabButton = gradioApp().querySelector('button')
     let baseStyle = window.getComputedStyle(tabButton)
 
     const contextMenu = document.createElement('nav')
@@ -94,7 +94,7 @@ contextMenuInit = function(){
     }
     gradioApp().addEventListener("click", function(e) {
       let source = e.composedPath()[0]
-      if(source.id && source.id.indexOf('check_progress')>-1){
+      if(source.id && source.indexOf('check_progress')>-1){
         return
       }
       
@@ -123,53 +123,44 @@ contextMenuInit = function(){
   return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
 }
 
-initResponse = contextMenuInit();
-appendContextMenuOption     = initResponse[0];
-removeContextMenuOption     = initResponse[1];
-addContextMenuEventListener = initResponse[2];
+initResponse = contextMenuInit()
+appendContextMenuOption     = initResponse[0]
+removeContextMenuOption     = initResponse[1]
+addContextMenuEventListener = initResponse[2]
 
-(function(){
-  //Start example Context Menu Items
-  let generateOnRepeat = function(genbuttonid,interruptbuttonid){
-    let genbutton = gradioApp().querySelector(genbuttonid);
-    let interruptbutton = gradioApp().querySelector(interruptbuttonid);
+
+//Start example Context Menu Items
+generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forever',function(){
+  let genbutton = gradioApp().querySelector('#txt2img_generate');
+  let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
+  if(!interruptbutton.offsetParent){
+    genbutton.click();
+  }
+  clearInterval(window.generateOnRepeatInterval)
+  window.generateOnRepeatInterval = setInterval(function(){
     if(!interruptbutton.offsetParent){
       genbutton.click();
     }
-    clearInterval(window.generateOnRepeatInterval)
-    window.generateOnRepeatInterval = setInterval(function(){
-      if(!interruptbutton.offsetParent){
-        genbutton.click();
-      }
-    },
-    500)
+  },
+  500)}
+)
+
+cancelGenerateForever = function(){ 
+  clearInterval(window.generateOnRepeatInterval) 
+}
+
+appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
+appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
+
+
+appendContextMenuOption('#roll','Roll three',
+  function(){ 
+    let rollbutton = gradioApp().querySelector('#roll');
+    setTimeout(function(){rollbutton.click()},100)
+    setTimeout(function(){rollbutton.click()},200)
+    setTimeout(function(){rollbutton.click()},300)
   }
-
-  appendContextMenuOption('#txt2img_generate','Generate forever',function(){
-    generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
-  })
-  appendContextMenuOption('#img2img_generate','Generate forever',function(){
-    generateOnRepeat('#img2img_generate','#img2img_interrupt');
-  })
-
-  let cancelGenerateForever = function(){ 
-    clearInterval(window.generateOnRepeatInterval) 
-  }
-
-  appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
-  appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
-  appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
-  appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
-
-  appendContextMenuOption('#roll','Roll three',
-    function(){ 
-      let rollbutton = get_uiCurrentTabContent().querySelector('#roll');
-      setTimeout(function(){rollbutton.click()},100)
-      setTimeout(function(){rollbutton.click()},200)
-      setTimeout(function(){rollbutton.click()},300)
-    }
-  )
-})();
+)
 //End example Context Menu Items
 
 onUiUpdate(function(){

javascript/edit-attention.js

@@ -25,7 +25,6 @@ addEventListener('keydown', (event) => {
 	} else {
 		end = target.value.slice(selectionEnd + 1).indexOf(")") + 1;
 		weight = parseFloat(target.value.slice(selectionEnd + 1, selectionEnd + 1 + end));
-		if (isNaN(weight)) return;
 		if (event.key == minus) weight -= 0.1;
 		if (event.key == plus) weight += 0.1;
 
@@ -39,7 +38,4 @@ addEventListener('keydown', (event) => {
 		target.selectionStart = selectionStart;
 		target.selectionEnd = selectionEnd;
 	}
-	// Since we've modified a Gradio Textbox component manually, we need to simulate an `input` DOM event to ensure its
-	// internal Svelte data binding remains in sync.
-	target.dispatchEvent(new Event("input", { bubbles: true }));
 });

javascript/hints.js

@@ -80,10 +80,7 @@ titles = {
     "Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
 
     "Eta noise seed delta": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.",
-    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.",
-
-    "Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
-    "Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
+    "Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be bevaing in an unethical manner.",
 }
 
 

javascript/ui.js

@@ -101,8 +101,7 @@ function create_tab_index_args(tabId, args){
 }
 
 function get_extras_tab_index(){
-    const [,,...args] = [...arguments]
-    return [get_tab_index('mode_extras'), get_tab_index('extras_resize_mode'), ...args]
+    return create_tab_index_args('mode_extras', arguments)
 }
 
 function create_submit_args(args){

launch.py

@@ -104,7 +104,6 @@ def prepare_enviroment():
     args, skip_torch_cuda_test = extract_arg(args, '--skip-torch-cuda-test')
     xformers = '--xformers' in args
     deepdanbooru = '--deepdanbooru' in args
-    ngrok = '--ngrok' in args
 
     try:
         commit = run(f"{git} rev-parse HEAD").strip()
@@ -135,9 +134,6 @@ def prepare_enviroment():
     if not is_installed("deepdanbooru") and deepdanbooru:
         run_pip("install git+https://github.com/KichangKim/DeepDanbooru.git@edf73df4cdaeea2cf00e9ac08bd8a9026b7a7b26#egg=deepdanbooru[tensorflow] tensorflow==2.10.0 tensorflow-io==0.27.0", "deepdanbooru")
 
-    if not is_installed("pyngrok") and ngrok:
-        run_pip("install pyngrok", "ngrok")
-
     os.makedirs(dir_repos, exist_ok=True)
 
     git_clone("https://github.com/CompVis/stable-diffusion.git", repo_dir('stable-diffusion'), "Stable Diffusion", stable_diffusion_commit_hash)

modules/deepbooru.py

@@ -1,99 +1,21 @@
 import os.path
 from concurrent.futures import ProcessPoolExecutor
-import multiprocessing
-import time
-import re
-
-re_special = re.compile(r'([\\()])')
-
-def get_deepbooru_tags(pil_image):
-    """
-    This method is for running only one image at a time for simple use.  Used to the img2img interrogate.
-    """
-    from modules import shared  # prevents circular reference
-
-    try:
-        create_deepbooru_process(shared.opts.interrogate_deepbooru_score_threshold, create_deepbooru_opts())
-        return get_tags_from_process(pil_image)
-    finally:
-        release_process()
+from multiprocessing import get_context
 
 
-def create_deepbooru_opts():
-    from modules import shared
-
-    return {
-        "use_spaces": shared.opts.deepbooru_use_spaces,
-        "use_escape": shared.opts.deepbooru_escape,
-        "alpha_sort": shared.opts.deepbooru_sort_alpha,
-    }
-
-
-def deepbooru_process(queue, deepbooru_process_return, threshold, deepbooru_opts):
-    model, tags = get_deepbooru_tags_model()
-    while True: # while process is running, keep monitoring queue for new image
-        pil_image = queue.get()
-        if pil_image == "QUIT":
-            break
-        else:
-            deepbooru_process_return["value"] = get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts)
-
-
-def create_deepbooru_process(threshold, deepbooru_opts):
-    """
-    Creates deepbooru process.  A queue is created to send images into the process.  This enables multiple images
-    to be processed in a row without reloading the model or creating a new process.  To return the data, a shared
-    dictionary is created to hold the tags created.  To wait for tags to be returned, a value of -1 is assigned
-    to the dictionary and the method adding the image to the queue should wait for this value to be updated with
-    the tags.
-    """
-    from modules import shared  # prevents circular reference
-    shared.deepbooru_process_manager = multiprocessing.Manager()
-    shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
-    shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts))
-    shared.deepbooru_process.start()
-
-
-def get_tags_from_process(image):
-    from modules import shared
-
-    shared.deepbooru_process_return["value"] = -1
-    shared.deepbooru_process_queue.put(image)
-    while shared.deepbooru_process_return["value"] == -1:
-        time.sleep(0.2)
-    caption = shared.deepbooru_process_return["value"]
-    shared.deepbooru_process_return["value"] = -1
-
-    return caption
-
-
-def release_process():
-    """
-    Stops the deepbooru process to return used memory
-    """
-    from modules import shared  # prevents circular reference
-    shared.deepbooru_process_queue.put("QUIT")
-    shared.deepbooru_process.join()
-    shared.deepbooru_process_queue = None
-    shared.deepbooru_process = None
-    shared.deepbooru_process_return = None
-    shared.deepbooru_process_manager = None
-
-def get_deepbooru_tags_model():
+def _load_tf_and_return_tags(pil_image, threshold):
     import deepdanbooru as dd
     import tensorflow as tf
     import numpy as np
+
     this_folder = os.path.dirname(__file__)
     model_path = os.path.abspath(os.path.join(this_folder, '..', 'models', 'deepbooru'))
     if not os.path.exists(os.path.join(model_path, 'project.json')):
         # there is no point importing these every time
         import zipfile
         from basicsr.utils.download_util import load_file_from_url
-        load_file_from_url(
-            r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
-            model_path)
+        load_file_from_url(r"https://github.com/KichangKim/DeepDanbooru/releases/download/v3-20211112-sgd-e28/deepdanbooru-v3-20211112-sgd-e28.zip",
+                           model_path)
         with zipfile.ZipFile(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"), "r") as zip_ref:
             zip_ref.extractall(model_path)
         os.remove(os.path.join(model_path, "deepdanbooru-v3-20211112-sgd-e28.zip"))
@@ -102,17 +24,6 @@ def get_deepbooru_tags_model():
     model = dd.project.load_model_from_project(
         model_path, compile_model=True
     )
-    return model, tags
-
-
-def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_opts):
-    import deepdanbooru as dd
-    import tensorflow as tf
-    import numpy as np
-
-    alpha_sort = deepbooru_opts['alpha_sort']
-    use_spaces = deepbooru_opts['use_spaces']
-    use_escape = deepbooru_opts['use_escape']
 
     width = model.input_shape[2]
     height = model.input_shape[1]
@@ -135,35 +46,28 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
 
     for i, tag in enumerate(tags):
         result_dict[tag] = y[i]
-
-    unsorted_tags_in_theshold = []
+    result_tags_out = []
     result_tags_print = []
     for tag in tags:
         if result_dict[tag] >= threshold:
             if tag.startswith("rating:"):
                 continue
-            unsorted_tags_in_theshold.append((result_dict[tag], tag))
+            result_tags_out.append(tag)
             result_tags_print.append(f'{result_dict[tag]} {tag}')
 
-    # sort tags
-    result_tags_out = []
-    sort_ndx = 0
-    if alpha_sort:
-        sort_ndx = 1
-
-    # sort by reverse by likelihood and normal for alpha
-    unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
-    for weight, tag in unsorted_tags_in_theshold:
-        result_tags_out.append(tag)
-
     print('\n'.join(sorted(result_tags_print, reverse=True)))
 
-    tags_text = ', '.join(result_tags_out)
+    return ', '.join(result_tags_out).replace('_', ' ').replace(':', ' ')
 
-    if use_spaces:
-        tags_text = tags_text.replace('_', ' ')
 
-    if use_escape:
-        tags_text = re.sub(re_special, r'\\\1', tags_text)
+def subprocess_init_no_cuda():
+    import os
+    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
-    return tags_text.replace(':', ' ')
+
+def get_deepbooru_tags(pil_image, threshold=0.5):
+    context = get_context('spawn')
+    with ProcessPoolExecutor(initializer=subprocess_init_no_cuda, mp_context=context) as executor:
+        f = executor.submit(_load_tf_and_return_tags, pil_image, threshold, )
+        ret = f.result()  # will rethrow any exceptions
+    return ret

modules/extras.py

@@ -1,4 +1,3 @@
-import math
 import os
 
 import numpy as np
@@ -20,7 +19,7 @@ import gradio as gr
 cached_images = {}
 
 
-def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
+def run_extras(extras_mode, image, image_folder, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility):
     devices.torch_gc()
 
     imageArr = []
@@ -68,13 +67,8 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
             info += f"CodeFormer w: {round(codeformer_weight, 2)}, CodeFormer visibility:{round(codeformer_visibility, 2)}\n"
             image = res
 
-        if resize_mode == 1:
-            upscaling_resize = max(upscaling_resize_w/image.width, upscaling_resize_h/image.height)
-            crop_info = " (crop)" if upscaling_crop else ""
-            info += f"Resize to: {upscaling_resize_w:g}x{upscaling_resize_h:g}{crop_info}\n"
-
         if upscaling_resize != 1.0:
-            def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+            def upscale(image, scaler_index, resize):
                 small = image.crop((image.width // 2, image.height // 2, image.width // 2 + 10, image.height // 2 + 10))
                 pixels = tuple(np.array(small).flatten().tolist())
                 key = (resize, scaler_index, image.width, image.height, gfpgan_visibility, codeformer_visibility, codeformer_weight) + pixels
@@ -83,19 +77,15 @@ def run_extras(extras_mode, resize_mode, image, image_folder, gfpgan_visibility,
                 if c is None:
                     upscaler = shared.sd_upscalers[scaler_index]
                     c = upscaler.scaler.upscale(image, resize, upscaler.data_path)
-                    if mode == 1 and crop:
-                        cropped = Image.new("RGB", (resize_w, resize_h))
-                        cropped.paste(c, box=(resize_w // 2 - c.width // 2, resize_h // 2 - c.height // 2))
-                        c = cropped
                     cached_images[key] = c
 
                 return c
 
             info += f"Upscale: {round(upscaling_resize, 3)}, model:{shared.sd_upscalers[extras_upscaler_1].name}\n"
-            res = upscale(image, extras_upscaler_1, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+            res = upscale(image, extras_upscaler_1, upscaling_resize)
 
             if extras_upscaler_2 != 0 and extras_upscaler_2_visibility > 0:
-                res2 = upscale(image, extras_upscaler_2, upscaling_resize, resize_mode, upscaling_resize_w, upscaling_resize_h, upscaling_crop)
+                res2 = upscale(image, extras_upscaler_2, upscaling_resize)
                 info += f"Upscale: {round(upscaling_resize, 3)}, visibility: {round(extras_upscaler_2_visibility, 3)}, model:{shared.sd_upscalers[extras_upscaler_2].name}\n"
                 res = Image.blend(res, res2, extras_upscaler_2_visibility)
 
@@ -200,7 +190,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
             theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount))  # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
             if save_as_half:
                 theta_0[key] = theta_0[key].half()
-
+    
     for key in theta_1.keys():
         if 'model' in key and key not in theta_0:
             theta_0[key] = theta_1[key]

modules/hypernetwork.py

@@ -0,0 +1,98 @@
+import glob
+import os
+import sys
+import traceback
+
+import torch
+
+from ldm.util import default
+from modules import devices, shared
+import torch
+from torch import einsum
+from einops import rearrange, repeat
+
+
+class HypernetworkModule(torch.nn.Module):
+    def __init__(self, dim, state_dict):
+        super().__init__()
+
+        self.linear1 = torch.nn.Linear(dim, dim * 2)
+        self.linear2 = torch.nn.Linear(dim * 2, dim)
+
+        self.load_state_dict(state_dict, strict=True)
+        self.to(devices.device)
+
+    def forward(self, x):
+        return x + (self.linear2(self.linear1(x)))
+
+
+class Hypernetwork:
+    filename = None
+    name = None
+
+    def __init__(self, filename):
+        self.filename = filename
+        self.name = os.path.splitext(os.path.basename(filename))[0]
+        self.layers = {}
+
+        state_dict = torch.load(filename, map_location='cpu')
+        for size, sd in state_dict.items():
+            self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
+
+
+def list_hypernetworks(path):
+    res = {}
+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
+        name = os.path.splitext(os.path.basename(filename))[0]
+        res[name] = filename
+    return res
+
+
+def load_hypernetwork(filename):
+    path = shared.hypernetworks.get(filename, None)
+    if path is not None:
+        print(f"Loading hypernetwork {filename}")
+        try:
+            shared.loaded_hypernetwork = Hypernetwork(path)
+        except Exception:
+            print(f"Error loading hypernetwork {path}", file=sys.stderr)
+            print(traceback.format_exc(), file=sys.stderr)
+    else:
+        if shared.loaded_hypernetwork is not None:
+            print(f"Unloading hypernetwork")
+
+        shared.loaded_hypernetwork = None
+
+
+def attention_CrossAttention_forward(self, x, context=None, mask=None):
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    hypernetwork = shared.loaded_hypernetwork
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k = self.to_k(hypernetwork_layers[0](context))
+        v = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+    if mask is not None:
+        mask = rearrange(mask, 'b ... -> b (...)')
+        max_neg_value = -torch.finfo(sim.dtype).max
+        mask = repeat(mask, 'b j -> (b h) () j', h=h)
+        sim.masked_fill_(~mask, max_neg_value)
+
+    # attention, what we cannot get enough of
+    attn = sim.softmax(dim=-1)
+
+    out = einsum('b i j, b j d -> b i d', attn, v)
+    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+    return self.to_out(out)

modules/hypernetworks/hypernetwork.py

@@ -1,308 +0,0 @@
-import datetime
-import glob
-import html
-import os
-import sys
-import traceback
-import tqdm
-
-import torch
-
-from ldm.util import default
-from modules import devices, shared, processing, sd_models
-import torch
-from torch import einsum
-from einops import rearrange, repeat
-import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnRateScheduler
-
-
-class HypernetworkModule(torch.nn.Module):
-    def __init__(self, dim, state_dict=None):
-        super().__init__()
-
-        self.linear1 = torch.nn.Linear(dim, dim * 2)
-        self.linear2 = torch.nn.Linear(dim * 2, dim)
-
-        if state_dict is not None:
-            self.load_state_dict(state_dict, strict=True)
-        else:
-
-            self.linear1.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear1.bias.data.zero_()
-            self.linear2.weight.data.normal_(mean=0.0, std=0.01)
-            self.linear2.bias.data.zero_()
-
-        self.to(devices.device)
-
-    def forward(self, x):
-        return x + (self.linear2(self.linear1(x)))
-
-
-class Hypernetwork:
-    filename = None
-    name = None
-
-    def __init__(self, name=None, enable_sizes=None):
-        self.filename = None
-        self.name = name
-        self.layers = {}
-        self.step = 0
-        self.sd_checkpoint = None
-        self.sd_checkpoint_name = None
-
-        for size in enable_sizes or []:
-            self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
-
-    def weights(self):
-        res = []
-
-        for k, layers in self.layers.items():
-            for layer in layers:
-                layer.train()
-                res += [layer.linear1.weight, layer.linear1.bias, layer.linear2.weight, layer.linear2.bias]
-
-        return res
-
-    def save(self, filename):
-        state_dict = {}
-
-        for k, v in self.layers.items():
-            state_dict[k] = (v[0].state_dict(), v[1].state_dict())
-
-        state_dict['step'] = self.step
-        state_dict['name'] = self.name
-        state_dict['sd_checkpoint'] = self.sd_checkpoint
-        state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
-
-        torch.save(state_dict, filename)
-
-    def load(self, filename):
-        self.filename = filename
-        if self.name is None:
-            self.name = os.path.splitext(os.path.basename(filename))[0]
-
-        state_dict = torch.load(filename, map_location='cpu')
-
-        for size, sd in state_dict.items():
-            if type(size) == int:
-                self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
-
-        self.name = state_dict.get('name', self.name)
-        self.step = state_dict.get('step', 0)
-        self.sd_checkpoint = state_dict.get('sd_checkpoint', None)
-        self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)
-
-
-def list_hypernetworks(path):
-    res = {}
-    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):
-        name = os.path.splitext(os.path.basename(filename))[0]
-        res[name] = filename
-    return res
-
-
-def load_hypernetwork(filename):
-    path = shared.hypernetworks.get(filename, None)
-    if path is not None:
-        print(f"Loading hypernetwork {filename}")
-        try:
-            shared.loaded_hypernetwork = Hypernetwork()
-            shared.loaded_hypernetwork.load(path)
-
-        except Exception:
-            print(f"Error loading hypernetwork {path}", file=sys.stderr)
-            print(traceback.format_exc(), file=sys.stderr)
-    else:
-        if shared.loaded_hypernetwork is not None:
-            print(f"Unloading hypernetwork")
-
-        shared.loaded_hypernetwork = None
-
-
-def find_closest_hypernetwork_name(search: str):
-    if not search:
-        return None
-    search = search.lower()
-    applicable = [name for name in shared.hypernetworks if search in name.lower()]
-    if not applicable:
-        return None
-    applicable = sorted(applicable, key=lambda name: len(name))
-    return applicable[0]
-
-
-def apply_hypernetwork(hypernetwork, context, layer=None):
-    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
-
-    if hypernetwork_layers is None:
-        return context, context
-
-    if layer is not None:
-        layer.hyper_k = hypernetwork_layers[0]
-        layer.hyper_v = hypernetwork_layers[1]
-
-    context_k = hypernetwork_layers[0](context)
-    context_v = hypernetwork_layers[1](context)
-    return context_k, context_v
-
-
-def attention_CrossAttention_forward(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)
-    k = self.to_k(context_k)
-    v = self.to_v(context_v)
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-
-    sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
-
-    if mask is not None:
-        mask = rearrange(mask, 'b ... -> b (...)')
-        max_neg_value = -torch.finfo(sim.dtype).max
-        mask = repeat(mask, 'b j -> (b h) () j', h=h)
-        sim.masked_fill_(~mask, max_neg_value)
-
-    # attention, what we cannot get enough of
-    attn = sim.softmax(dim=-1)
-
-    out = einsum('b i j, b j d -> b i d', attn, v)
-    out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
-    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_image_prompt):
-    assert hypernetwork_name, 'hypernetwork not selected'
-
-    path = shared.hypernetworks.get(hypernetwork_name, None)
-    shared.loaded_hypernetwork = Hypernetwork()
-    shared.loaded_hypernetwork.load(path)
-
-    shared.state.textinfo = "Initializing hypernetwork training..."
-    shared.state.job_count = steps
-
-    filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
-
-    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
-    unload = shared.opts.unload_models_when_training
-
-    if save_hypernetwork_every > 0:
-        hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
-        os.makedirs(hypernetwork_dir, exist_ok=True)
-    else:
-        hypernetwork_dir = None
-
-    if create_image_every > 0:
-        images_dir = os.path.join(log_directory, "images")
-        os.makedirs(images_dir, exist_ok=True)
-    else:
-        images_dir = None
-
-    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=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
-
-    if unload:
-        shared.sd_model.cond_stage_model.to(devices.cpu)
-        shared.sd_model.first_stage_model.to(devices.cpu)
-
-    hypernetwork = shared.loaded_hypernetwork
-    weights = hypernetwork.weights()
-    for weight in weights:
-        weight.requires_grad = True
-
-    losses = torch.zeros((32,))
-
-    last_saved_file = "<none>"
-    last_saved_image = "<none>"
-
-    ititial_step = hypernetwork.step or 0
-    if ititial_step > steps:
-        return hypernetwork, filename
-
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
-
-    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, entry in pbar:
-        hypernetwork.step = i + ititial_step
-
-        scheduler.apply(optimizer, hypernetwork.step)
-        if scheduler.finished:
-            break
-
-        if shared.state.interrupted:
-            break
-
-        with torch.autocast("cuda"):
-            cond = entry.cond.to(devices.device)
-            x = entry.latent.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
-            del x
-            del cond
-
-            losses[hypernetwork.step % losses.shape[0]] = loss.item()
-
-            optimizer.zero_grad()
-            loss.backward()
-            optimizer.step()
-
-        pbar.set_description(f"loss: {losses.mean():.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)
-
-        if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
-
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
-            optimizer.zero_grad()
-            shared.sd_model.cond_stage_model.to(devices.device)
-            shared.sd_model.first_stage_model.to(devices.device)
-
-            p = processing.StableDiffusionProcessingTxt2Img(
-                sd_model=shared.sd_model,
-                prompt=preview_text,
-                steps=20,
-                do_not_save_grid=True,
-                do_not_save_samples=True,
-            )
-
-            processed = processing.process_images(p)
-            image = processed.images[0] if len(processed.images)>0 else None
-
-            if unload:
-                shared.sd_model.cond_stage_model.to(devices.cpu)
-                shared.sd_model.first_stage_model.to(devices.cpu)
-
-            if image is not None:
-                shared.state.current_image = image
-                image.save(last_saved_image)
-                last_saved_image += f", prompt: {preview_text}"
-
-        shared.state.job_no = hypernetwork.step
-
-        shared.state.textinfo = f"""
-<p>
-Loss: {losses.mean():.7f}<br/>
-Step: {hypernetwork.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
-Last saved embedding: {html.escape(last_saved_file)}<br/>
-Last saved image: {html.escape(last_saved_image)}<br/>
-</p>
-"""
-
-    checkpoint = sd_models.select_checkpoint()
-
-    hypernetwork.sd_checkpoint = checkpoint.hash
-    hypernetwork.sd_checkpoint_name = checkpoint.model_name
-    hypernetwork.save(filename)
-
-    return hypernetwork, filename
-
-

modules/hypernetworks/ui.py

@@ -1,47 +0,0 @@
-import html
-import os
-
-import gradio as gr
-
-import modules.textual_inversion.textual_inversion
-import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared, devices
-from modules.hypernetworks import hypernetwork
-
-
-def create_hypernetwork(name, enable_sizes):
-    fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt")
-    assert not os.path.exists(fn), f"file {fn} already exists"
-
-    hypernet = modules.hypernetworks.hypernetwork.Hypernetwork(name=name, enable_sizes=[int(x) for x in enable_sizes])
-    hypernet.save(fn)
-
-    shared.reload_hypernetworks()
-
-    return gr.Dropdown.update(choices=sorted([x for x in shared.hypernetworks.keys()])), f"Created: {fn}", ""
-
-
-def train_hypernetwork(*args):
-
-    initial_hypernetwork = shared.loaded_hypernetwork
-
-    assert not shared.cmd_opts.lowvram, 'Training models with lowvram is not possible'
-
-    try:
-        sd_hijack.undo_optimizations()
-
-        hypernetwork, filename = modules.hypernetworks.hypernetwork.train_hypernetwork(*args)
-
-        res = f"""
-Training {'interrupted' if shared.state.interrupted else 'finished'} at {hypernetwork.step} steps.
-Hypernetwork saved to {html.escape(filename)}
-"""
-        return res, ""
-    except Exception:
-        raise
-    finally:
-        shared.loaded_hypernetwork = initial_hypernetwork
-        shared.sd_model.cond_stage_model.to(devices.device)
-        shared.sd_model.first_stage_model.to(devices.device)
-        sd_hijack.apply_optimizations()
-

modules/ngrok.py

@@ -1,15 +0,0 @@
-from pyngrok import ngrok, conf, exception
-
-
-def connect(token, port):
-    if token == None:
-        token = 'None'
-    conf.get_default().auth_token = token
-    try:
-        public_url = ngrok.connect(port).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')
-    else:
-        print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
-               'You can use this link after the launch is complete.')

modules/safe.py

@@ -10,7 +10,6 @@ import torch
 import numpy
 import _codecs
 import zipfile
-import re
 
 
 # PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
@@ -55,27 +54,11 @@ class RestrictedUnpickler(pickle.Unpickler):
         raise pickle.UnpicklingError(f"global '{module}/{name}' is forbidden")
 
 
-allowed_zip_names = ["archive/data.pkl", "archive/version"]
-allowed_zip_names_re = re.compile(r"^archive/data/\d+$")
-
-
-def check_zip_filenames(filename, names):
-    for name in names:
-        if name in allowed_zip_names:
-            continue
-        if allowed_zip_names_re.match(name):
-            continue
-
-        raise Exception(f"bad file inside {filename}: {name}")
-
-
 def check_pt(filename):
     try:
 
         # new pytorch format is a zip file
         with zipfile.ZipFile(filename) as z:
-            check_zip_filenames(filename, z.namelist())
-
             with z.open('archive/data.pkl') as file:
                 unpickler = RestrictedUnpickler(file)
                 unpickler.load()

modules/sd_hijack.py

@@ -8,9 +8,8 @@ from torch import einsum
 from torch.nn.functional import silu
 
 import modules.textual_inversion.textual_inversion
-from modules import prompt_parser, devices, sd_hijack_optimizations, shared
+from modules import prompt_parser, devices, sd_hijack_optimizations, shared, hypernetwork
 from modules.shared import opts, device, cmd_opts
-from modules.sd_hijack_optimizations import invokeAI_mps_available
 
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
@@ -31,23 +30,13 @@ def apply_optimizations():
     elif cmd_opts.opt_split_attention_v1:
         print("Applying v1 cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-    elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
-        if not invokeAI_mps_available and shared.device.type == 'mps':
-            print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
-            print("Applying v1 cross attention optimization.")
-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-        else:
-            print("Applying cross attention optimization (InvokeAI).")
-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
-        print("Applying cross attention optimization (Doggettx).")
+        print("Applying cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.cross_attention_attnblock_forward
 
 
 def undo_optimizations():
-    from modules.hypernetworks import hypernetwork
-
     ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
     ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
     ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
@@ -118,8 +107,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         self.tokenizer = wrapped.tokenizer
         self.token_mults = {}
 
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
-
         tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
@@ -149,7 +136,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         fixes = []
         remade_tokens = []
         multipliers = []
-        last_comma = -1
 
         for tokens, (text, weight) in zip(tokenized, parsed):
             i = 0
@@ -158,20 +144,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
 
                 embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
 
-                if token == self.comma_token:
-                    last_comma = len(remade_tokens)
-                elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
-                    last_comma += 1
-                    reloc_tokens = remade_tokens[last_comma:]
-                    reloc_mults = multipliers[last_comma:]
-
-                    remade_tokens = remade_tokens[:last_comma]
-                    length = len(remade_tokens)
-                    
-                    rem = int(math.ceil(length / 75)) * 75 - length
-                    remade_tokens += [id_end] * rem + reloc_tokens
-                    multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
-                
                 if embedding is None:
                     remade_tokens.append(token)
                     multipliers.append(weight)
@@ -312,7 +284,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
         while max(map(len, remade_batch_tokens)) != 0:
             rem_tokens = [x[75:] for x in remade_batch_tokens]
             rem_multipliers = [x[75:] for x in batch_multipliers]
-
+            
             self.hijack.fixes = []
             for unfiltered in hijack_fixes:
                 fixes = []
@@ -321,17 +293,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
                         fixes.append(fix[1])
                 self.hijack.fixes.append(fixes)
             
-            tokens = []
-            multipliers = []
-            for j in range(len(remade_batch_tokens)):
-                if len(remade_batch_tokens[j]) > 0:
-                    tokens.append(remade_batch_tokens[j][:75])
-                    multipliers.append(batch_multipliers[j][:75])
-                else:
-                    tokens.append([self.wrapped.tokenizer.eos_token_id] * 75)
-                    multipliers.append([1.0] * 75)
-
-            z1 = self.process_tokens(tokens, multipliers)
+            z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
             z = z1 if z is None else torch.cat((z, z1), axis=-2)
             
             remade_batch_tokens = rem_tokens

modules/sd_hijack_optimizations.py

@@ -1,7 +1,6 @@
 import math
 import sys
 import traceback
-import importlib
 
 import torch
 from torch import einsum
@@ -10,8 +9,6 @@ from ldm.util import default
 from einops import rearrange
 
 from modules import shared
-from modules.hypernetworks import hypernetwork
-
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
@@ -29,10 +26,16 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     q_in = self.to_q(x)
     context = default(context, x)
 
-    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
-    k_in = self.to_k(context_k)
-    v_in = self.to_v(context_v)
-    del context, context_k, context_v, x
+    hypernetwork = shared.loaded_hypernetwork
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
+    del context, x
 
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
@@ -56,16 +59,22 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     return self.to_out(r2)
 
 
-# taken from https://github.com/Doggettx/stable-diffusion and modified
+# taken from https://github.com/Doggettx/stable-diffusion
 def split_cross_attention_forward(self, x, context=None, mask=None):
     h = self.heads
 
     q_in = self.to_q(x)
     context = default(context, x)
 
-    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
-    k_in = self.to_k(context_k)
-    v_in = self.to_v(context_v)
+    hypernetwork = shared.loaded_hypernetwork
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
 
     k_in *= self.scale
 
@@ -117,111 +126,18 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     return self.to_out(r2)
 
-
-def check_for_psutil():
-    try:
-        spec = importlib.util.find_spec('psutil')
-        return spec is not None
-    except ModuleNotFoundError:
-        return False
-
-invokeAI_mps_available = check_for_psutil()
-
-# -- Taken from https://github.com/invoke-ai/InvokeAI --
-if invokeAI_mps_available:
-    import psutil
-    mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-
-def einsum_op_compvis(q, k, v):
-    s = einsum('b i d, b j d -> b i j', q, k)
-    s = s.softmax(dim=-1, dtype=s.dtype)
-    return einsum('b i j, b j d -> b i d', s, v)
-
-def einsum_op_slice_0(q, k, v, slice_size):
-    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-    for i in range(0, q.shape[0], slice_size):
-        end = i + slice_size
-        r[i:end] = einsum_op_compvis(q[i:end], k[i:end], v[i:end])
-    return r
-
-def einsum_op_slice_1(q, k, v, slice_size):
-    r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-    for i in range(0, q.shape[1], slice_size):
-        end = i + slice_size
-        r[:, i:end] = einsum_op_compvis(q[:, i:end], k, v)
-    return r
-
-def einsum_op_mps_v1(q, k, v):
-    if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
-        return einsum_op_compvis(q, k, v)
-    else:
-        slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
-        return einsum_op_slice_1(q, k, v, slice_size)
-
-def einsum_op_mps_v2(q, k, v):
-    if mem_total_gb > 8 and q.shape[1] <= 4096:
-        return einsum_op_compvis(q, k, v)
-    else:
-        return einsum_op_slice_0(q, k, v, 1)
-
-def einsum_op_tensor_mem(q, k, v, max_tensor_mb):
-    size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
-    if size_mb <= max_tensor_mb:
-        return einsum_op_compvis(q, k, v)
-    div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
-    if div <= q.shape[0]:
-        return einsum_op_slice_0(q, k, v, q.shape[0] // div)
-    return einsum_op_slice_1(q, k, v, max(q.shape[1] // div, 1))
-
-def einsum_op_cuda(q, k, v):
-    stats = torch.cuda.memory_stats(q.device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
-    mem_free_cuda, _ = torch.cuda.mem_get_info(q.device)
-    mem_free_torch = mem_reserved - mem_active
-    mem_free_total = mem_free_cuda + mem_free_torch
-    # Divide factor of safety as there's copying and fragmentation
-    return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
-
-def einsum_op(q, k, v):
-    if q.device.type == 'cuda':
-        return einsum_op_cuda(q, k, v)
-
-    if q.device.type == 'mps':
-        if mem_total_gb >= 32:
-            return einsum_op_mps_v1(q, k, v)
-        return einsum_op_mps_v2(q, k, v)
-
-    # Smaller slices are faster due to L2/L3/SLC caches.
-    # Tested on i7 with 8MB L3 cache.
-    return einsum_op_tensor_mem(q, k, v, 32)
-
-def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
-    h = self.heads
-
-    q = self.to_q(x)
-    context = default(context, x)
-
-    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
-    k = self.to_k(context_k) * self.scale
-    v = self.to_v(context_v)
-    del context, context_k, context_v, x
-
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
-    r = einsum_op(q, k, v)
-    return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
-
-# -- End of code from https://github.com/invoke-ai/InvokeAI --
-
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
     context = default(context, x)
-
-    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
-    k_in = self.to_k(context_k)
-    v_in = self.to_v(context_v)
-
+    hypernetwork = shared.loaded_hypernetwork
+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
+    if hypernetwork_layers is not None:
+        k_in = self.to_k(hypernetwork_layers[0](context))
+        v_in = self.to_v(hypernetwork_layers[1](context))
+    else:
+        k_in = self.to_k(context)
+        v_in = self.to_v(context)
     q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
     del q_in, k_in, v_in
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)

modules/sd_samplers.py

@@ -57,7 +57,7 @@ def set_samplers():
     global samplers, samplers_for_img2img
 
     hidden = set(opts.hide_samplers)
-    hidden_img2img = set(opts.hide_samplers + ['PLMS'])
+    hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
 
     samplers = [x for x in all_samplers if x.name not in hidden]
     samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
@@ -365,26 +365,16 @@ class KDiffusionSampler:
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 
-        sigma_sched = sigmas[steps - t_enc - 1:]
-        xi = x + noise * sigma_sched[0]
-        
+        noise = noise * sigmas[steps - t_enc - 1]
+        xi = x + noise
+
         extra_params_kwargs = self.initialize(p)
-        if 'sigma_min' in inspect.signature(self.func).parameters:
-            ## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
-            extra_params_kwargs['sigma_min'] = sigma_sched[-2]
-        if 'sigma_max' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['sigma_max'] = sigma_sched[0]
-        if 'n' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['n'] = len(sigma_sched) - 1
-        if 'sigma_sched' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['sigma_sched'] = sigma_sched
-        if 'sigmas' in inspect.signature(self.func).parameters:
-            extra_params_kwargs['sigmas'] = sigma_sched
+
+        sigma_sched = sigmas[steps - t_enc - 1:]
 
         self.model_wrap_cfg.init_latent = x
 
-        return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
-
+        return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
 
     def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
         steps = steps or p.steps

modules/shared.py

@@ -13,8 +13,7 @@ import modules.memmon
 import modules.sd_models
 import modules.styles
 import modules.devices as devices
-from modules import sd_samplers
-from modules.hypernetworks import hypernetwork
+from modules import sd_samplers, hypernetwork
 from modules.paths import models_path, script_path, sd_path
 
 sd_model_file = os.path.join(script_path, 'model.ckpt')
@@ -30,7 +29,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th
 parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)")
 parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI")
 parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)")
-parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory")
 parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui")
 parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage")
 parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage")
@@ -38,7 +36,6 @@ parser.add_argument("--always-batch-cond-uncond", action='store_true', help="dis
 parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
 parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
 parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site (doesn't work for me but you might have better luck)")
-parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)
 parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer'))
 parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN'))
 parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN'))
@@ -50,10 +47,9 @@ parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with
 parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
-parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
-parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
-parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
+parser.add_argument("--opt-split-attention", action='store_true', help="force-enables cross-attention layer optimization. By default, it's on for torch.cuda and off for other torch devices.")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
+parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--use-cpu", nargs='+',choices=['SD', 'GFPGAN', 'BSRGAN', 'ESRGAN', 'SCUNet', 'CodeFormer'], help="use CPU as torch device for specified modules", default=[])
 parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests")
 parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None)
@@ -86,18 +82,10 @@ parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
 xformers_available = False
 config_filename = cmd_opts.ui_settings_file
 
-os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
-hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
+hypernetworks = hypernetwork.list_hypernetworks(os.path.join(models_path, 'hypernetworks'))
 loaded_hypernetwork = None
 
 
-def reload_hypernetworks():
-    global hypernetworks
-
-    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
-    hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
-
-
 class State:
     skipped = False
     interrupted = False
@@ -229,13 +217,6 @@ options_templates.update(options_section(('system', "System"), {
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
 }))
 
-options_templates.update(options_section(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"),
-    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
-    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
-    "training_image_repeats_per_epoch": OptionInfo(100, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
-}))
-
 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()}, show_on_main_page=True),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
@@ -246,7 +227,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"),
     "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
     "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
-    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
     "filter_nsfw": OptionInfo(False, "Filter NSFW content"),
     'CLIP_stop_at_last_layers': OptionInfo(1, "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()}),
@@ -258,10 +238,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
     "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
     "interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
     "interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
-    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
-    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
+    "interrogate_clip_dict_limit": OptionInfo(1500, "Interrogate: maximum number of lines in text file (0 = No limit)"),
 }))
 
 options_templates.update(options_section(('ui', "User interface"), {

modules/textual_inversion/dataset.py

@@ -8,24 +8,14 @@ from torchvision import transforms
 
 import random
 import tqdm
-from modules import devices, shared
+from modules import devices
 import re
 
-re_numbers_at_start = re.compile(r"^[-\d]+\s*")
-
-
-class DatasetEntry:
-    def __init__(self, filename=None, latent=None, filename_text=None):
-        self.filename = filename
-        self.latent = latent
-        self.filename_text = filename_text
-        self.cond = None
-        self.cond_text = None
+re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 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):
-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else None
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
 
         self.placeholder_token = placeholder_token
 
@@ -42,28 +32,16 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
-        cond_model = shared.sd_model.cond_stage_model
-
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
-            try:
-                image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)
-            except Exception:
-                continue
+            image = Image.open(path)
+            image = image.convert('RGB')
+            image = image.resize((self.width, self.height), PIL.Image.BICUBIC)
 
-            text_filename = os.path.splitext(path)[0] + ".txt"
             filename = os.path.basename(path)
-
-            if os.path.exists(text_filename):
-                with open(text_filename, "r", encoding="utf8") as file:
-                    filename_text = file.read()
-            else:
-                filename_text = os.path.splitext(filename)[0]
-                filename_text = re.sub(re_numbers_at_start, '', filename_text)
-                if re_word:
-                    tokens = re_word.findall(filename_text)
-                    filename_text = (shared.opts.dataset_filename_join_string or "").join(tokens)
+            filename_tokens = os.path.splitext(filename)[0]
+            filename_tokens = re_tag.findall(filename_tokens)
 
             npimage = np.array(image).astype(np.uint8)
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
@@ -74,13 +52,7 @@ class PersonalizedBase(Dataset):
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
             init_latent = init_latent.to(devices.cpu)
 
-            entry = DatasetEntry(filename=path, filename_text=filename_text, latent=init_latent)
-
-            if include_cond:
-                entry.cond_text = self.create_text(filename_text)
-                entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
-
-            self.dataset.append(entry)
+            self.dataset.append((init_latent, filename_tokens))
 
         self.length = len(self.dataset) * repeats
 
@@ -91,12 +63,6 @@ class PersonalizedBase(Dataset):
     def shuffle(self):
         self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
 
-    def create_text(self, filename_text):
-        text = random.choice(self.lines)
-        text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", filename_text)
-        return text
-
     def __len__(self):
         return self.length
 
@@ -105,9 +71,10 @@ class PersonalizedBase(Dataset):
             self.shuffle()
 
         index = self.indexes[i % len(self.indexes)]
-        entry = self.dataset[index]
+        x, filename_tokens = self.dataset[index]
 
-        if entry.cond is None:
-            entry.cond_text = self.create_text(entry.filename_text)
+        text = random.choice(self.lines)
+        text = text.replace("[name]", self.placeholder_token)
+        text = text.replace("[filewords]", ' '.join(filename_tokens))
 
-        return entry
+        return x, text

modules/textual_inversion/image_embedding.py

@@ -1,219 +0,0 @@
-import base64
-import json
-import numpy as np
-import zlib
-from PIL import Image, PngImagePlugin, ImageDraw, ImageFont
-from fonts.ttf import Roboto
-import torch
-
-
-class EmbeddingEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, torch.Tensor):
-            return {'TORCHTENSOR': obj.cpu().detach().numpy().tolist()}
-        return json.JSONEncoder.default(self, obj)
-
-
-class EmbeddingDecoder(json.JSONDecoder):
-    def __init__(self, *args, **kwargs):
-        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)
-
-    def object_hook(self, d):
-        if 'TORCHTENSOR' in d:
-            return torch.from_numpy(np.array(d['TORCHTENSOR']))
-        return d
-
-
-def embedding_to_b64(data):
-    d = json.dumps(data, cls=EmbeddingEncoder)
-    return base64.b64encode(d.encode())
-
-
-def embedding_from_b64(data):
-    d = base64.b64decode(data)
-    return json.loads(d, cls=EmbeddingDecoder)
-
-
-def lcg(m=2**32, a=1664525, c=1013904223, seed=0):
-    while True:
-        seed = (a * seed + c) % m
-        yield seed % 255
-
-
-def xor_block(block):
-    g = lcg()
-    randblock = np.array([next(g) for _ in range(np.product(block.shape))]).astype(np.uint8).reshape(block.shape)
-    return np.bitwise_xor(block.astype(np.uint8), randblock & 0x0F)
-
-
-def style_block(block, sequence):
-    im = Image.new('RGB', (block.shape[1], block.shape[0]))
-    draw = ImageDraw.Draw(im)
-    i = 0
-    for x in range(-6, im.size[0], 8):
-        for yi, y in enumerate(range(-6, im.size[1], 8)):
-            offset = 0
-            if yi % 2 == 0:
-                offset = 4
-            shade = sequence[i % len(sequence)]
-            i += 1
-            draw.ellipse((x+offset, y, x+6+offset, y+6), fill=(shade, shade, shade))
-
-    fg = np.array(im).astype(np.uint8) & 0xF0
-
-    return block ^ fg
-
-
-def insert_image_data_embed(image, data):
-    d = 3
-    data_compressed = zlib.compress(json.dumps(data, cls=EmbeddingEncoder).encode(), level=9)
-    data_np_ = np.frombuffer(data_compressed, np.uint8).copy()
-    data_np_high = data_np_ >> 4
-    data_np_low = data_np_ & 0x0F
-
-    h = image.size[1]
-    next_size = data_np_low.shape[0] + (h-(data_np_low.shape[0] % h))
-    next_size = next_size + ((h*d)-(next_size % (h*d)))
-
-    data_np_low.resize(next_size)
-    data_np_low = data_np_low.reshape((h, -1, d))
-
-    data_np_high.resize(next_size)
-    data_np_high = data_np_high.reshape((h, -1, d))
-
-    edge_style = list(data['string_to_param'].values())[0].cpu().detach().numpy().tolist()[0][:1024]
-    edge_style = (np.abs(edge_style)/np.max(np.abs(edge_style))*255).astype(np.uint8)
-
-    data_np_low = style_block(data_np_low, sequence=edge_style)
-    data_np_low = xor_block(data_np_low)
-    data_np_high = style_block(data_np_high, sequence=edge_style[::-1])
-    data_np_high = xor_block(data_np_high)
-
-    im_low = Image.fromarray(data_np_low, mode='RGB')
-    im_high = Image.fromarray(data_np_high, mode='RGB')
-
-    background = Image.new('RGB', (image.size[0]+im_low.size[0]+im_high.size[0]+2, image.size[1]), (0, 0, 0))
-    background.paste(im_low, (0, 0))
-    background.paste(image, (im_low.size[0]+1, 0))
-    background.paste(im_high, (im_low.size[0]+1+image.size[0]+1, 0))
-
-    return background
-
-
-def crop_black(img, tol=0):
-    mask = (img > tol).all(2)
-    mask0, mask1 = mask.any(0), mask.any(1)
-    col_start, col_end = mask0.argmax(), mask.shape[1]-mask0[::-1].argmax()
-    row_start, row_end = mask1.argmax(), mask.shape[0]-mask1[::-1].argmax()
-    return img[row_start:row_end, col_start:col_end]
-
-
-def extract_image_data_embed(image):
-    d = 3
-    outarr = crop_black(np.array(image.convert('RGB').getdata()).reshape(image.size[1], image.size[0], d).astype(np.uint8)) & 0x0F
-    black_cols = np.where(np.sum(outarr, axis=(0, 2)) == 0)
-    if black_cols[0].shape[0] < 2:
-        print('No Image data blocks found.')
-        return None
-
-    data_block_lower = outarr[:, :black_cols[0].min(), :].astype(np.uint8)
-    data_block_upper = outarr[:, black_cols[0].max()+1:, :].astype(np.uint8)
-
-    data_block_lower = xor_block(data_block_lower)
-    data_block_upper = xor_block(data_block_upper)
-
-    data_block = (data_block_upper << 4) | (data_block_lower)
-    data_block = data_block.flatten().tobytes()
-
-    data = zlib.decompress(data_block)
-    return json.loads(data, cls=EmbeddingDecoder)
-
-
-def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, textfont=None):
-    from math import cos
-
-    image = srcimage.copy()
-
-    if textfont is None:
-        try:
-            textfont = ImageFont.truetype(opts.font or Roboto, fontsize)
-            textfont = opts.font or Roboto
-        except Exception:
-            textfont = Roboto
-
-    factor = 1.5
-    gradient = Image.new('RGBA', (1, image.size[1]), color=(0, 0, 0, 0))
-    for y in range(image.size[1]):
-        mag = 1-cos(y/image.size[1]*factor)
-        mag = max(mag, 1-cos((image.size[1]-y)/image.size[1]*factor*1.1))
-        gradient.putpixel((0, y), (0, 0, 0, int(mag*255)))
-    image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size))
-
-    draw = ImageDraw.Draw(image)
-    fontsize = 32
-    font = ImageFont.truetype(textfont, fontsize)
-    padding = 10
-
-    _, _, w, h = draw.textbbox((0, 0), title, font=font)
-    fontsize = min(int(fontsize * (((image.size[0]*0.75)-(padding*4))/w)), 72)
-    font = ImageFont.truetype(textfont, fontsize)
-    _, _, w, h = draw.textbbox((0, 0), title, font=font)
-    draw.text((padding, padding), title, anchor='lt', font=font, fill=(255, 255, 255, 230))
-
-    _, _, w, h = draw.textbbox((0, 0), footerLeft, font=font)
-    fontsize_left = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
-    _, _, w, h = draw.textbbox((0, 0), footerMid, font=font)
-    fontsize_mid = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
-    _, _, w, h = draw.textbbox((0, 0), footerRight, font=font)
-    fontsize_right = min(int(fontsize * (((image.size[0]/3)-(padding))/w)), 72)
-
-    font = ImageFont.truetype(textfont, min(fontsize_left, fontsize_mid, fontsize_right))
-
-    draw.text((padding, image.size[1]-padding),               footerLeft, anchor='ls', font=font, fill=(255, 255, 255, 230))
-    draw.text((image.size[0]/2, image.size[1]-padding),       footerMid, anchor='ms', font=font, fill=(255, 255, 255, 230))
-    draw.text((image.size[0]-padding, image.size[1]-padding), footerRight, anchor='rs', font=font, fill=(255, 255, 255, 230))
-
-    return image
-
-
-if __name__ == '__main__':
-
-    testEmbed = Image.open('test_embedding.png')
-    data = extract_image_data_embed(testEmbed)
-    assert data is not None
-
-    data = embedding_from_b64(testEmbed.text['sd-ti-embedding'])
-    assert data is not None
-
-    image = Image.new('RGBA', (512, 512), (255, 255, 200, 255))
-    cap_image = caption_image_overlay(image, 'title', 'footerLeft', 'footerMid', 'footerRight')
-
-    test_embed = {'string_to_param': {'*': torch.from_numpy(np.random.random((2, 4096)))}}
-
-    embedded_image = insert_image_data_embed(cap_image, test_embed)
-
-    retrived_embed = extract_image_data_embed(embedded_image)
-
-    assert str(retrived_embed) == str(test_embed)
-
-    embedded_image2 = insert_image_data_embed(cap_image, retrived_embed)
-
-    assert embedded_image == embedded_image2
-
-    g = lcg()
-    shared_random = np.array([next(g) for _ in range(100)]).astype(np.uint8).tolist()
-
-    reference_random = [253, 242, 127,  44, 157,  27, 239, 133,  38,  79, 167,   4, 177,
-                         95, 130,  79,  78,  14,  52, 215, 220, 194, 126,  28, 240, 179,
-                        160, 153, 149,  50, 105,  14,  21, 218, 199,  18,  54, 198, 193,
-                         38, 128,  19,  53, 195, 124,  75, 205,  12,   6, 145,   0,  28,
-                         30, 148,   8,  45, 218, 171,  55, 249,  97, 166,  12,  35,   0,
-                         41, 221, 122, 215, 170,  31, 113, 186,  97, 119,  31,  23, 185,
-                         66, 140,  30,  41,  37,  63, 137, 109, 216,  55, 159, 145,  82,
-                         204, 86,  73, 222,  44, 198, 118, 240,  97]
-
-    assert shared_random == reference_random
-
-    hunna_kay_random_sum = sum(np.array([next(g) for _ in range(100000)]).astype(np.uint8).tolist())
-
-    assert 12731374 == hunna_kay_random_sum

modules/textual_inversion/learn_schedule.py

@@ -1,69 +0,0 @@
-import tqdm
-
-
-class LearnScheduleIterator:
-    def __init__(self, learn_rate, max_steps, cur_step=0):
-        """
-        specify learn_rate as "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, 1e-5:10000 until 10000
-        """
-
-        pairs = learn_rate.split(',')
-        self.rates = []
-        self.it = 0
-        self.maxit = 0
-        for i, pair in enumerate(pairs):
-            tmp = pair.split(':')
-            if len(tmp) == 2:
-                step = int(tmp[1])
-                if step > cur_step:
-                    self.rates.append((float(tmp[0]), min(step, max_steps)))
-                    self.maxit += 1
-                    if step > max_steps:
-                        return
-                elif step == -1:
-                    self.rates.append((float(tmp[0]), max_steps))
-                    self.maxit += 1
-                    return
-            else:
-                self.rates.append((float(tmp[0]), max_steps))
-                self.maxit += 1
-                return
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.it < self.maxit:
-            self.it += 1
-            return self.rates[self.it - 1]
-        else:
-            raise StopIteration
-
-
-class LearnRateScheduler:
-    def __init__(self, learn_rate, max_steps, cur_step=0, verbose=True):
-        self.schedules = LearnScheduleIterator(learn_rate, max_steps, cur_step)
-        (self.learn_rate,  self.end_step) = next(self.schedules)
-        self.verbose = verbose
-
-        if self.verbose:
-            print(f'Training at rate of {self.learn_rate} until step {self.end_step}')
-
-        self.finished = False
-
-    def apply(self, optimizer, step_number):
-        if step_number <= self.end_step:
-            return
-
-        try:
-            (self.learn_rate, self.end_step) = next(self.schedules)
-        except Exception:
-            self.finished = True
-            return
-
-        if self.verbose:
-            tqdm.tqdm.write(f'Training at rate of {self.learn_rate} until step {self.end_step}')
-
-        for pg in optimizer.param_groups:
-            pg['lr'] = self.learn_rate
-

modules/textual_inversion/preprocess.py

@@ -3,35 +3,11 @@ from PIL import Image, ImageOps
 import platform
 import sys
 import tqdm
-import time
 
 from modules import shared, images
-from modules.shared import opts, cmd_opts
-if cmd_opts.deepdanbooru:
-    import modules.deepbooru as deepbooru
 
 
-def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
-    try:
-        if process_caption:
-            shared.interrogator.load()
-
-        if process_caption_deepbooru:
-            deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
-
-        preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)
-
-    finally:
-
-        if process_caption:
-            shared.interrogator.send_blip_to_ram()
-
-        if process_caption_deepbooru:
-            deepbooru.release_process()
-
-
-
-def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False):
+def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
     width = process_width
     height = process_height
     src = os.path.abspath(process_src)
@@ -46,28 +22,19 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 
+    if process_caption:
+        shared.interrogator.load()
+
     def save_pic_with_caption(image, index):
-        caption = ""
-
         if process_caption:
-            caption += shared.interrogator.generate_caption(image)
-
-        if process_caption_deepbooru:
-            if len(caption) > 0:
-                caption += ", "
-            caption += deepbooru.get_tags_from_process(image)
-
-        filename_part = filename
-        filename_part = os.path.splitext(filename_part)[0]
-        filename_part = os.path.basename(filename_part)
-
-        basename = f"{index:05}-{subindex[0]}-{filename_part}"
-        image.save(os.path.join(dst, f"{basename}.png"))
-
-        if len(caption) > 0:
-            with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
-                file.write(caption)
+            caption = "-" + shared.interrogator.generate_caption(image)
+            caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
+        else:
+            caption = filename
+            caption = os.path.splitext(caption)[0]
+            caption = os.path.basename(caption)
 
+        image.save(os.path.join(dst, f"{index:05}-{subindex[0]}{caption}.png"))
         subindex[0] += 1
 
     def save_pic(image, index):
@@ -79,10 +46,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
     for index, imagefile in enumerate(tqdm.tqdm(files)):
         subindex = [0]
         filename = os.path.join(src, imagefile)
-        try:
-            img = Image.open(filename).convert("RGB")
-        except Exception:
-            continue
+        img = Image.open(filename).convert("RGB")
 
         if shared.state.interrupted:
             break
@@ -112,3 +76,30 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro
             save_pic(img, index)
 
         shared.state.nextjob()
+
+    if process_caption:
+        shared.interrogator.send_blip_to_ram()
+
+def sanitize_caption(base_path, original_caption, suffix):
+    operating_system = platform.system().lower()
+    if (operating_system == "windows"):
+        invalid_path_characters = "\\/:*?\"<>|"
+        max_path_length = 259
+    else:
+        invalid_path_characters = "/" #linux/macos
+        max_path_length = 1023
+    caption = original_caption
+    for invalid_character in invalid_path_characters:
+        caption = caption.replace(invalid_character, "")
+    fixed_path_length = len(base_path) + len(suffix) 
+    if fixed_path_length + len(caption) <= max_path_length:
+        return caption
+    caption_tokens = caption.split()
+    new_caption = ""
+    for token in caption_tokens:
+        last_caption = new_caption
+        new_caption = new_caption + token + " "
+        if (len(new_caption) + fixed_path_length - 1  > max_path_length):
+            break
+    print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
+    return last_caption.strip()

modules/textual_inversion/textual_inversion.py

@@ -7,15 +7,10 @@ import tqdm
 import html
 import datetime
 
-from PIL import Image, PngImagePlugin
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
-from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
-from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
-                                                       insert_image_data_embed, extract_image_data_embed,
-                                                       caption_image_overlay)
 
 class Embedding:
     def __init__(self, vec, name, step=None):
@@ -85,18 +80,7 @@ class EmbeddingDatabase:
         def process_file(path, filename):
             name = os.path.splitext(filename)[0]
 
-            data = []
-
-            if filename.upper().endswith('.PNG'):
-                embed_image = Image.open(path)
-                if '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)
-            else:
-                data = torch.load(path, map_location="cpu")
+            data = torch.load(path, map_location="cpu")
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -172,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn
 
 
-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_image_prompt):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
     assert embedding_name, 'embedding not selected'
 
     shared.state.textinfo = "Initializing textual inversion training..."
@@ -194,23 +178,19 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     else:
         images_dir = None
 
-    if create_image_every > 0 and save_image_with_stored_embedding:
-        images_embeds_dir = os.path.join(log_directory, "image_embeddings")
-        os.makedirs(images_embeds_dir, exist_ok=True)
-    else:
-        images_embeds_dir = None
-        
     cond_model = shared.sd_model.cond_stage_model
 
     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=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
 
     hijack = sd_hijack.model_hijack
 
     embedding = hijack.embedding_db.word_embeddings[embedding_name]
     embedding.vec.requires_grad = True
 
+    optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
+
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -220,24 +200,23 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
-    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
+    epoch_len = (tr_img_len * num_repeats) + tr_img_len
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, entry in pbar:
+    for i, (x, text) in pbar:
         embedding.step = i + ititial_step
 
-        scheduler.apply(optimizer, embedding.step)
-        if scheduler.finished:
+        if embedding.step > steps:
             break
 
         if shared.state.interrupted:
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([entry.cond_text])
+            c = cond_model([text])
 
-            x = entry.latent.to(devices.device)
+            x = x.to(devices.device)
             loss = shared.sd_model(x.unsqueeze(0), c)[0]
             del x
 
@@ -247,10 +226,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             loss.backward()
             optimizer.step()
 
-        epoch_num = embedding.step // len(ds)
-        epoch_step = embedding.step - (epoch_num * len(ds)) + 1
+        epoch_num = embedding.step // epoch_len
+        epoch_step = embedding.step - (epoch_num * epoch_len) + 1
 
-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
 
         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')
@@ -259,14 +238,12 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
             last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
 
-            preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
-                prompt=preview_text,
+                prompt=text,
                 steps=20,
-                height=training_height,
-                width=training_width,
+				height=training_height,
+				width=training_width,
                 do_not_save_grid=True,
                 do_not_save_samples=True,
             )
@@ -275,29 +252,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
             image = processed.images[0]
 
             shared.state.current_image = image
-
-            if save_image_with_stored_embedding and os.path.exists(last_saved_file):
-
-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
-
-                info = PngImagePlugin.PngInfo()
-                data = torch.load(last_saved_file)
-                info.add_text("sd-ti-embedding", embedding_to_b64(data))
-
-                title = "<{}>".format(data.get('name', '???'))
-                checkpoint = sd_models.select_checkpoint()
-                footer_left = checkpoint.model_name
-                footer_mid = '[{}]'.format(checkpoint.hash)
-                footer_right = '{}'.format(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)
-
             image.save(last_saved_image)
 
-            last_saved_image += f", prompt: {preview_text}"
+            last_saved_image += f", prompt: {text}"
 
         shared.state.job_no = embedding.step
 
@@ -305,7 +262,7 @@ 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(text)}<br/>
 Last saved embedding: {html.escape(last_saved_file)}<br/>
 Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
@@ -319,3 +276,4 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.save(filename)
 
     return embedding, filename
+

modules/textual_inversion/ui.py

@@ -23,8 +23,6 @@ def preprocess(*args):
 
 def train_embedding(*args):
 
-    assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
-
     try:
         sd_hijack.undo_optimizations()
 

modules/ui.py

@@ -39,7 +39,6 @@ import modules.generation_parameters_copypaste
 from modules import prompt_parser
 from modules.images import save_image
 import modules.textual_inversion.ui
-import modules.hypernetworks.ui
 
 # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
 mimetypes.init()
@@ -51,11 +50,6 @@ if not cmd_opts.share and not cmd_opts.listen:
     gradio.utils.version_check = lambda: None
     gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
 
-if cmd_opts.ngrok != None:
-    import modules.ngrok as ngrok
-    print('ngrok authtoken detected, trying to connect...')
-    ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860)
-
 
 def gr_show(visible=True):
     return {"visible": visible, "__type__": "update"}
@@ -107,7 +101,7 @@ def send_gradio_gallery_to_image(x):
 
 
 def save_files(js_data, images, do_make_zip, index):
-    import csv
+    import csv    
     filenames = []
     fullfns = []
 
@@ -131,8 +125,6 @@ def save_files(js_data, images, do_make_zip, index):
         images = [images[index]]
         start_index = index
 
-    os.makedirs(opts.outdir_save, exist_ok=True)
-
     with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
         at_start = file.tell() == 0
         writer = csv.writer(file)
@@ -183,15 +175,8 @@ def wrap_gradio_call(func, extra_outputs=None):
         try:
             res = list(func(*args, **kwargs))
         except Exception as e:
-            # When printing out our debug argument list, do not print out more than a MB of text
-            max_debug_str_len = 131072 # (1024*1024)/8
-
             print("Error completing request", file=sys.stderr)
-            argStr = f"Arguments: {str(args)} {str(kwargs)}"
-            print(argStr[:max_debug_str_len], file=sys.stderr)
-            if len(argStr) > max_debug_str_len:
-                print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
-
+            print("Arguments:", args, kwargs, file=sys.stderr)
             print(traceback.format_exc(), file=sys.stderr)
 
             shared.state.job = ""
@@ -443,10 +428,7 @@ def create_toprow(is_img2img):
 
             with gr.Row():
                 with gr.Column(scale=8):
-                    with gr.Row():
-                        negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
-                with gr.Column(scale=1, elem_id="roll_col"):
-                    sh = gr.Button(elem_id="sh", visible=True)                           
+                    negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
 
                 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())), visible=len(shared.prompt_styles.styles) > 1)
@@ -569,13 +551,13 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
+                    
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+                with gr.Group():
+                    html_info = gr.HTML()
+                    generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -757,13 +739,13 @@ def create_ui(wrap_gradio_gpu_call):
 
                     with gr.Row():
                         do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
-
+                    
                     with gr.Row():
                         download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
 
-                    with gr.Group():
-                        html_info = gr.HTML()
-                        generation_info = gr.Textbox(visible=False)
+                with gr.Group():
+                    html_info = gr.HTML()
+                    generation_info = gr.Textbox(visible=False)
 
             connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
             connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
@@ -921,15 +903,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.TabItem('Batch Process'):
                         image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
 
-                with gr.Tabs(elem_id="extras_resize_mode"):
-                    with gr.TabItem('Scale by'):
-                        upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
-                    with gr.TabItem('Scale to'):
-                        with gr.Group():
-                            with gr.Row():
-                                upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
-                                upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
-                            upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
+                upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
 
                 with gr.Group():
                     extras_upscaler_1 = gr.Radio(label='Upscaler 1', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
@@ -960,7 +934,6 @@ def create_ui(wrap_gradio_gpu_call):
             fn=wrap_gradio_gpu_call(modules.extras.run_extras),
             _js="get_extras_tab_index",
             inputs=[
-                dummy_component,
                 dummy_component,
                 extras_image,
                 image_batch,
@@ -968,9 +941,6 @@ def create_ui(wrap_gradio_gpu_call):
                 codeformer_visibility,
                 codeformer_weight,
                 upscaling_resize,
-                upscaling_resize_w,
-                upscaling_resize_h,
-                upscaling_crop,
                 extras_upscaler_1,
                 extras_upscaler_2,
                 extras_upscaler_2_visibility,
@@ -981,14 +951,14 @@ def create_ui(wrap_gradio_gpu_call):
                 html_info,
             ]
         )
-
+     
         extras_send_to_img2img.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_img2img",
             inputs=[result_images],
             outputs=[init_img],
         )
-
+        
         extras_send_to_inpaint.click(
             fn=lambda x: image_from_url_text(x),
             _js="extract_image_from_gallery_inpaint",
@@ -1035,14 +1005,14 @@ def create_ui(wrap_gradio_gpu_call):
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    with gr.Blocks() as train_interface:
+    with gr.Blocks() as textual_inversion_interface:
         with gr.Row().style(equal_height=False):
-            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+            with gr.Column():
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
-        with gr.Row().style(equal_height=False):
-            with gr.Tabs(elem_id="train_tabs"):
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
 
-                with gr.Tab(label="Create embedding"):
                     new_embedding_name = gr.Textbox(label="Name")
                     initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
@@ -1052,20 +1022,11 @@ def create_ui(wrap_gradio_gpu_call):
                             gr.HTML(value="")
 
                         with gr.Column():
-                            create_embedding = gr.Button(value="Create embedding", variant='primary')
+                            create_embedding = gr.Button(value="Create", variant='primary')
 
-                with gr.Tab(label="Create hypernetwork"):
-                    new_hypernetwork_name = gr.Textbox(label="Name")
-                    new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
+                with gr.Group():
+                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
 
-                    with gr.Row():
-                        with gr.Column(scale=3):
-                            gr.HTML(value="")
-
-                        with gr.Column():
-                            create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
-
-                with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
@@ -1074,8 +1035,7 @@ def create_ui(wrap_gradio_gpu_call):
                     with gr.Row():
                         process_flip = gr.Checkbox(label='Create flipped copies')
                         process_split = gr.Checkbox(label='Split oversized images into two')
-                        process_caption = gr.Checkbox(label='Use BLIP for caption')
-                        process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
+                        process_caption = gr.Checkbox(label='Use BLIP caption as filename')
 
                     with gr.Row():
                         with gr.Column(scale=3):
@@ -1084,26 +1044,28 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
-                with gr.Tab(label="Train"):
+                with gr.Group():
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
-                    train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
+                    learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
                     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"))
                     training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
                     training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
                     steps = gr.Number(label='Max steps', value=100000, precision=0)
+                    num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
                     create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
                     save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
-                    save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
-                    preview_image_prompt = gr.Textbox(label='Preview prompt', value="")
 
                     with gr.Row():
-                        interrupt_training = gr.Button(value="Interrupt")
-                        train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
-                        train_embedding = gr.Button(value="Train Embedding", variant='primary')
+                        with gr.Column(scale=2):
+                            gr.HTML(value="")
+
+                        with gr.Column():
+                            with gr.Row():
+                                interrupt_training = gr.Button(value="Interrupt")
+                                train_embedding = gr.Button(value="Train", variant='primary')
 
             with gr.Column():
                 progressbar = gr.HTML(elem_id="ti_progressbar")
@@ -1129,19 +1091,6 @@ def create_ui(wrap_gradio_gpu_call):
             ]
         )
 
-        create_hypernetwork.click(
-            fn=modules.hypernetworks.ui.create_hypernetwork,
-            inputs=[
-                new_hypernetwork_name,
-                new_hypernetwork_sizes,
-            ],
-            outputs=[
-                train_hypernetwork_name,
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
         run_preprocess.click(
             fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
             _js="start_training_textual_inversion",
@@ -1153,7 +1102,6 @@ def create_ui(wrap_gradio_gpu_call):
                 process_flip,
                 process_split,
                 process_caption,
-                process_caption_deepbooru
             ],
             outputs=[
                 ti_output,
@@ -1172,31 +1120,10 @@ def create_ui(wrap_gradio_gpu_call):
                 training_width,
                 training_height,
                 steps,
+                num_repeats,
                 create_image_every,
                 save_embedding_every,
                 template_file,
-                save_image_with_stored_embedding,
-                preview_image_prompt,
-            ],
-            outputs=[
-                ti_output,
-                ti_outcome,
-            ]
-        )
-
-        train_hypernetwork.click(
-            fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
-            _js="start_training_textual_inversion",
-            inputs=[
-                train_hypernetwork_name,
-                learn_rate,
-                dataset_directory,
-                log_directory,
-                steps,
-                create_image_every,
-                save_embedding_every,
-                template_file,
-                preview_image_prompt,
             ],
             outputs=[
                 ti_output,
@@ -1210,7 +1137,6 @@ def create_ui(wrap_gradio_gpu_call):
             outputs=[],
         )
 
-
     def create_setting_component(key):
         def fun():
             return opts.data[key] if key in opts.data else opts.data_labels[key].default
@@ -1364,14 +1290,13 @@ Requested path was: {f}
             shared.state.interrupt()
             settings_interface.gradio_ref.do_restart = True
 
-
         restart_gradio.click(
             fn=request_restart,
             inputs=[],
             outputs=[],
             _js='function(){restart_reload()}'
         )
-
+        
         if column is not None:
             column.__exit__()
 
@@ -1381,7 +1306,7 @@ Requested path was: {f}
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (train_interface, "Train", "ti"),
+        (textual_inversion_interface, "Textual inversion", "ti"),
         (settings_interface, "Settings", "settings"),
     ]
 
@@ -1403,12 +1328,12 @@ Requested path was: {f}
                 component_dict[k] = component
 
         settings_interface.gradio_ref = demo
-
+        
         with gr.Tabs() as tabs:
             for interface, label, ifid in interfaces:
-                with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
+                with gr.TabItem(label, id=ifid):
                     interface.render()
-
+        
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
             audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
@@ -1541,10 +1466,10 @@ Requested path was: {f}
 
             if getattr(obj,'custom_script_source',None) is not None:
               key = 'customscript/' + obj.custom_script_source + '/' + key
-
+            
             if getattr(obj, 'do_not_save_to_config', False):
                 return
-
+            
             saved_value = ui_settings.get(key, None)
             if saved_value is None:
                 ui_settings[key] = getattr(obj, field)
@@ -1568,10 +1493,10 @@ Requested path was: {f}
 
         if type(x) == gr.Textbox:
             apply_field(x, 'value')
-
+        
         if type(x) == gr.Number:
             apply_field(x, 'value')
-
+        
     visit(txt2img_interface, loadsave, "txt2img")
     visit(img2img_interface, loadsave, "img2img")
     visit(extras_interface, loadsave, "extras")

requirements.txt

@@ -4,7 +4,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.4.1
+gradio==3.4b3
 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==3.4b3
 numpy==1.23.3
 Pillow==9.2.0
 realesrgan==0.3.0

script.js

@@ -6,10 +6,6 @@ function get_uiCurrentTab() {
     return gradioApp().querySelector('.tabs button:not(.border-transparent)')
 }
 
-function get_uiCurrentTabContent() {
-    return gradioApp().querySelector('.tabitem[id^=tab_]:not([style*="display: none"])')
-}
-
 uiUpdateCallbacks = []
 uiTabChangeCallbacks = []
 let uiCurrentTab = null
@@ -54,11 +50,8 @@ document.addEventListener("DOMContentLoaded", function() {
     } else if (e.keyCode !== undefined) {
         if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
     }
-    if (handled) {
-        button = get_uiCurrentTabContent().querySelector('button[id$=_generate]');
-        if (button) {
-            button.click();
-        }
+    if (handled) { 
+        gradioApp().querySelector("#txt2img_generate").click(); 
         e.preventDefault();
     }
 })

scripts/img2imgalt.py

@@ -129,6 +129,8 @@ class Script(scripts.Script):
         return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
 
     def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
+        p.batch_size = 1
+        p.batch_count = 1
 
 
         def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
@@ -152,7 +154,7 @@ class Script(scripts.Script):
                     rec_noise = find_noise_for_image(p, cond, uncond, cfg, st)
                 self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
 
-            rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
+            rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], [p.seed + x + 1 for x in range(p.init_latent.shape[0])])
             
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
             

scripts/loopback.py

@@ -38,7 +38,6 @@ class Script(scripts.Script):
 
         grids = []
         all_images = []
-        original_init_image = p.init_images
         state.job_count = loops * batch_count
 
         initial_color_corrections = [processing.setup_color_correction(p.init_images[0])]
@@ -46,9 +45,6 @@ class Script(scripts.Script):
         for n in range(batch_count):
             history = []
 
-            # Reset to original init image at the start of each batch
-            p.init_images = original_init_image
-
             for i in range(loops):
                 p.n_iter = 1
                 p.batch_size = 1

scripts/xy_grid.py

@@ -10,8 +10,7 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images
-from modules.hypernetworks import hypernetwork
+from modules import images, hypernetwork
 from modules.processing import process_images, Processed, get_correct_sampler
 from modules.shared import opts, cmd_opts, state
 import modules.shared as shared
@@ -28,9 +27,6 @@ def apply_field(field):
 
 
 def apply_prompt(p, x, xs):
-    if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:
-        raise RuntimeError(f"Prompt S/R did not find {xs[0]} in prompt or negative prompt.")
-
     p.prompt = p.prompt.replace(xs[0], x)
     p.negative_prompt = p.negative_prompt.replace(xs[0], x)
 
@@ -77,42 +73,14 @@ def apply_sampler(p, x, xs):
     p.sampler_index = sampler_index
 
 
-def confirm_samplers(p, xs):
-    samplers_dict = build_samplers_dict(p)
-    for x in xs:
-        if x.lower() not in samplers_dict.keys():
-            raise RuntimeError(f"Unknown sampler: {x}")
-
-
 def apply_checkpoint(p, x, xs):
     info = modules.sd_models.get_closet_checkpoint_match(x)
-    if info is None:
-        raise RuntimeError(f"Unknown checkpoint: {x}")
+    assert info is not None, f'Checkpoint for {x} not found'
     modules.sd_models.reload_model_weights(shared.sd_model, info)
 
 
-def confirm_checkpoints(p, xs):
-    for x in xs:
-        if modules.sd_models.get_closet_checkpoint_match(x) is None:
-            raise RuntimeError(f"Unknown checkpoint: {x}")
-
-
 def apply_hypernetwork(p, x, xs):
-    if x.lower() in ["", "none"]:
-        name = None
-    else:
-        name = hypernetwork.find_closest_hypernetwork_name(x)
-        if not name:
-            raise RuntimeError(f"Unknown hypernetwork: {x}")
-    hypernetwork.load_hypernetwork(name)
-
-
-def confirm_hypernetworks(p, xs):
-    for x in xs:
-        if x.lower() in ["", "none"]:
-            continue
-        if not hypernetwork.find_closest_hypernetwork_name(x):
-            raise RuntimeError(f"Unknown hypernetwork: {x}")
+    hypernetwork.load_hypernetwork(x)
 
 
 def apply_clip_skip(p, x, xs):
@@ -149,29 +117,29 @@ def str_permutations(x):
     return x
 
 
-AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value", "confirm"])
-AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value", "confirm"])
+AxisOption = namedtuple("AxisOption", ["label", "type", "apply", "format_value"])
+AxisOptionImg2Img = namedtuple("AxisOptionImg2Img", ["label", "type", "apply", "format_value"])
 
 
 axis_options = [
-    AxisOption("Nothing", str, do_nothing, format_nothing, None),
-    AxisOption("Seed", int, apply_field("seed"), format_value_add_label, None),
-    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label, None),
-    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label, None),
-    AxisOption("Steps", int, apply_field("steps"), format_value_add_label, None),
-    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label, None),
-    AxisOption("Prompt S/R", str, apply_prompt, format_value, None),
-    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list, None),
-    AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
-    AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
-    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
-    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
-    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
-    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
-    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label, None),
-    AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
-    AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
-    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
+    AxisOption("Nothing", str, do_nothing, format_nothing),
+    AxisOption("Seed", int, apply_field("seed"), format_value_add_label),
+    AxisOption("Var. seed", int, apply_field("subseed"), format_value_add_label),
+    AxisOption("Var. strength", float, apply_field("subseed_strength"), format_value_add_label),
+    AxisOption("Steps", int, apply_field("steps"), format_value_add_label),
+    AxisOption("CFG Scale", float, apply_field("cfg_scale"), format_value_add_label),
+    AxisOption("Prompt S/R", str, apply_prompt, format_value),
+    AxisOption("Prompt order", str_permutations, apply_order, format_value_join_list),
+    AxisOption("Sampler", str, apply_sampler, format_value),
+    AxisOption("Checkpoint name", str, apply_checkpoint, format_value),
+    AxisOption("Hypernetwork", str, apply_hypernetwork, format_value),
+    AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label),
+    AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label),
+    AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label),
+    AxisOption("Sigma noise", float, apply_field("s_noise"), format_value_add_label),
+    AxisOption("Eta", float, apply_field("eta"), format_value_add_label),
+    AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label),
+    AxisOptionImg2Img("Denoising", float, apply_field("denoising_strength"), format_value_add_label),  # as it is now all AxisOptionImg2Img items must go after AxisOption ones
 ]
 
 
@@ -225,7 +193,7 @@ class Script(scripts.Script):
             x_values = gr.Textbox(label="X values", visible=False, lines=1)
 
         with gr.Row():
-            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[0].label, visible=False, type="index", elem_id="y_type")
+            y_type = gr.Dropdown(label="Y type", choices=[x.label for x in current_axis_options], value=current_axis_options[4].label, visible=False, type="index", elem_id="y_type")
             y_values = gr.Textbox(label="Y values", visible=False, lines=1)
         
         draw_legend = gr.Checkbox(label='Draw legend', value=True)
@@ -297,10 +265,17 @@ class Script(scripts.Script):
                 valslist = list(permutations(valslist))
 
             valslist = [opt.type(x) for x in valslist]
-
+            
             # Confirm options are valid before starting
-            if opt.confirm:
-                opt.confirm(p, valslist)
+            if opt.label == "Sampler":
+                samplers_dict = build_samplers_dict(p)
+                for sampler_val in valslist:
+                    if sampler_val.lower() not in samplers_dict.keys():
+                        raise RuntimeError(f"Unknown sampler: {sampler_val}")
+            elif opt.label == "Checkpoint name":
+                for ckpt_val in valslist:
+                    if modules.sd_models.get_closet_checkpoint_match(ckpt_val) is None:
+                        raise RuntimeError(f"Checkpoint for {ckpt_val} not found")
 
             return valslist
 

style.css

@@ -2,27 +2,6 @@
     max-width: 100%;
 }
 
-#txt2img_token_counter {
-    height: 0px;
-}
-
-#img2img_token_counter {
-    height: 0px;
-}
-
-#sh{
-    min-width: 2em;
-    min-height: 2em;
-    max-width: 2em;
-    max-height: 2em;
-    flex-grow: 0;
-    padding-left: 0.25em;
-    padding-right: 0.25em;
-    margin: 0.1em 0;
-    opacity: 0%;
-    cursor: default;	
-}
-
 .output-html p {margin: 0 0.5em;}
 
 .row > *,
@@ -240,7 +219,6 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
 #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
     position: relative;
     border: none;
-    margin-right: 8em;
 }
 
 .gr-panel div.flex-col div.justify-between label span{
@@ -496,13 +474,3 @@ canvas[key="mask"] {
     mix-blend-mode: multiply;
     pointer-events: none;
 }
-
-
-/* gradio 3.4.1 stuff for editable scrollbar values */
-.gr-box > div > div > input.gr-text-input{
-    position: absolute;
-    right: 0.5em;
-    top: -0.6em;
-    z-index: 200;
-    width: 8em;
-}

textual_inversion_templates/hypernetwork.txt

@@ -1,27 +0,0 @@
-a photo of a [filewords]
-a rendering of a [filewords]
-a cropped photo of the [filewords]
-the photo of a [filewords]
-a photo of a clean [filewords]
-a photo of a dirty [filewords]
-a dark photo of the [filewords]
-a photo of my [filewords]
-a photo of the cool [filewords]
-a close-up photo of a [filewords]
-a bright photo of the [filewords]
-a cropped photo of a [filewords]
-a photo of the [filewords]
-a good photo of the [filewords]
-a photo of one [filewords]
-a close-up photo of the [filewords]
-a rendition of the [filewords]
-a photo of the clean [filewords]
-a rendition of a [filewords]
-a photo of a nice [filewords]
-a good photo of a [filewords]
-a photo of the nice [filewords]
-a photo of the small [filewords]
-a photo of the weird [filewords]
-a photo of the large [filewords]
-a photo of a cool [filewords]
-a photo of a small [filewords]

textual_inversion_templates/none.txt

@@ -1 +0,0 @@
-picture

webui.py

@@ -29,9 +29,13 @@ from modules import devices
 from modules import modelloader
 from modules.paths import script_path
 from modules.shared import cmd_opts
-import modules.hypernetworks.hypernetwork
-
 
+modelloader.cleanup_models()
+modules.sd_models.setup_model()
+codeformer.setup_model(cmd_opts.codeformer_models_path)
+gfpgan.setup_model(cmd_opts.gfpgan_models_path)
+shared.face_restorers.append(modules.face_restoration.FaceRestoration())
+modelloader.load_upscalers()
 queue_lock = threading.Lock()
 
 
@@ -73,24 +77,16 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
     return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
 
 
-def initialize():
-    modelloader.cleanup_models()
-    modules.sd_models.setup_model()
-    codeformer.setup_model(cmd_opts.codeformer_models_path)
-    gfpgan.setup_model(cmd_opts.gfpgan_models_path)
-    shared.face_restorers.append(modules.face_restoration.FaceRestoration())
-    modelloader.load_upscalers()
+modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
 
-    modules.scripts.load_scripts(os.path.join(script_path, "scripts"))
+shared.sd_model = modules.sd_models.load_model()
+shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
 
-    shared.sd_model = modules.sd_models.load_model()
-    shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
-    shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
+loaded_hypernetwork = modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)
+shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
 
 
 def webui():
-    initialize()
-    
     # make the program just exit at ctrl+c without waiting for anything
     def sigint_handler(sig, frame):
         print(f'Interrupted with signal {sig} in {frame}')
@@ -102,7 +98,7 @@ def webui():
 
         demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call)
         
-        app, local_url, share_url = demo.launch(
+        app,local_url,share_url = demo.launch(
             share=cmd_opts.share,
             server_name="0.0.0.0" if cmd_opts.listen else None,
             server_port=cmd_opts.port,
@@ -112,7 +108,7 @@ def webui():
             prevent_thread_lock=True
         )
         
-        app.add_middleware(GZipMiddleware, minimum_size=1000)
+        app.add_middleware(GZipMiddleware,minimum_size=1000)
 
         while 1:
             time.sleep(0.5)
@@ -128,10 +124,9 @@ def webui():
         modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
         print('Reloading modules: modules.ui')
         importlib.reload(modules.ui)
-        print('Refreshing Model List')
-        modules.sd_models.list_models()
         print('Restarting Gradio')
 
 
+
 if __name__ == "__main__":
     webui()