Merge branch 'AUTOMATIC1111:master' into fix-sd-arch-switch-in-override-settings

README.md

@@ -127,6 +127,8 @@ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC
 The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
 
 ## Credits
+Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
+
 - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
 - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
 - GFPGAN - https://github.com/TencentARC/GFPGAN.git

configs/alt-diffusion-inference.yaml

@@ -0,0 +1,72 @@
+model:
+  base_learning_rate: 1.0e-04
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False
+
+    scheduler_config: # 10000 warmup steps
+      target: ldm.lr_scheduler.LambdaLinearScheduler
+      params:
+        warm_up_steps: [ 10000 ]
+        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+        f_start: [ 1.e-6 ]
+        f_max: [ 1. ]
+        f_min: [ 1. ]
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_heads: 8
+        use_spatial_transformer: True
+        transformer_depth: 1
+        context_dim: 768
+        use_checkpoint: True
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: modules.xlmr.BertSeriesModelWithTransformation
+      params:
+        name: "XLMR-Large"

extensions-builtin/roll-artist/scripts/roll-artist.py

@@ -0,0 +1,50 @@
+import random
+
+from modules import script_callbacks, shared
+import gradio as gr
+
+art_symbol = '\U0001f3a8'  # 🎨
+global_prompt = None
+related_ids = {"txt2img_prompt", "txt2img_clear_prompt", "img2img_prompt", "img2img_clear_prompt" }
+
+
+def roll_artist(prompt):
+    allowed_cats = set([x for x in shared.artist_db.categories() if len(shared.opts.random_artist_categories)==0 or x in shared.opts.random_artist_categories])
+    artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
+
+    return prompt + ", " + artist.name if prompt != '' else artist.name
+
+
+def add_roll_button(prompt):
+    roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
+
+    roll.click(
+        fn=roll_artist,
+        _js="update_txt2img_tokens",
+        inputs=[
+            prompt,
+        ],
+        outputs=[
+            prompt,
+        ]
+    )
+
+
+def after_component(component, **kwargs):
+    global global_prompt
+
+    elem_id = kwargs.get('elem_id', None)
+    if elem_id not in related_ids:
+        return
+
+    if elem_id == "txt2img_prompt":
+        global_prompt = component
+    elif elem_id == "txt2img_clear_prompt":
+        add_roll_button(global_prompt)
+    elif elem_id == "img2img_prompt":
+        global_prompt = component
+    elif elem_id == "img2img_clear_prompt":
+        add_roll_button(global_prompt)
+
+
+script_callbacks.on_after_component(after_component)

html/footer.html

@@ -0,0 +1,9 @@
+<div>
+        <a href="/docs">API</a>
+         • 
+        <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
+         • 
+        <a href="https://gradio.app">Gradio</a>
+         • 
+        <a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
+</div>

html/licenses.html

@@ -0,0 +1,392 @@
+<style>
+    #licenses h2 {font-size: 1.2em; font-weight: bold; margin-bottom: 0.2em;}
+    #licenses small {font-size: 0.95em; opacity: 0.85;}
+    #licenses pre { margin: 1em 0 2em 0;}
+</style>
+
+<h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
+<small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
+<pre>
+S-Lab License 1.0
+
+Copyright 2022 S-Lab
+
+Redistribution and use for non-commercial purpose in source and
+binary forms, with or without modification, are permitted provided
+that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in
+   the documentation and/or other materials provided with the
+   distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived
+   from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+In the event that redistribution and/or use for commercial purpose in
+source or binary forms, with or without modification is required,
+please contact the contributor(s) of the work.
+</pre>
+
+
+<h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
+<small>Code for architecture and reading models copied.</small>
+<pre>
+MIT License
+
+Copyright (c) 2021 victorca25
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
+<small>Some code is copied to support ESRGAN models.</small>
+<pre>
+BSD 3-Clause License
+
+Copyright (c) 2021, Xintao Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+</pre>
+
+<h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
+<small>Some code for compatibility with OSX is taken from lstein's repository.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 InvokeAI Team
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/Hafiidz/latent-diffusion/blob/main/LICENSE">LDSR</a></h2>
+<small>Code added by contirubtors, most likely copied from this repository.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/pharmapsychotic/clip-interrogator/blob/main/LICENSE">CLIP Interrogator</a></h2>
+<small>Some small amounts of code borrowed and reworked.</small>
+<pre>
+MIT License
+
+Copyright (c) 2022 pharmapsychotic
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
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+
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+copies or substantial portions of the Software.
+
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+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+</pre>
+
+<h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
+<small>Code added by contirubtors, most likely copied from this repository.</small>
+
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+

javascript/dragdrop.js

@@ -9,11 +9,19 @@ function dropReplaceImage( imgWrap, files ) {
         return;
     }
 
+    const tmpFile = files[0];
+
     imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
     const callback = () => {
         const fileInput = imgWrap.querySelector('input[type="file"]');
         if ( fileInput ) {
-            fileInput.files = files;
+            if ( files.length === 0 ) {
+                files = new DataTransfer();
+                files.items.add(tmpFile);
+                fileInput.files = files.files;
+            } else {
+                fileInput.files = files;
+            }
             fileInput.dispatchEvent(new Event('change'));   
         }
     };

javascript/hints.js

@@ -81,9 +81,6 @@ titles = {
 
     "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).",
 
-    "Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
-    "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.",
 
@@ -100,7 +97,13 @@ titles = {
     "Clip skip": "Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.",
 
     "Approx NN": "Cheap neural network approximation. Very fast compared to VAE, but produces pictures with 4 times smaller horizontal/vertical resoluton and lower quality.",
-    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality."
+    "Approx cheap": "Very cheap approximation. Very fast compared to VAE, but produces pictures with 8 times smaller horizontal/vertical resoluton and extremely low quality.",
+
+    "Hires. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
+    "Hires steps": "Number of sampling steps for upscaled picture. If 0, uses same as for original.",
+    "Upscale by": "Adjusts the size of the image by multiplying the original width and height by the selected value. Ignored if either Resize width to or Resize height to are non-zero.",
+    "Resize width to": "Resizes image to this width. If 0, width is inferred from either of two nearby sliders.",
+    "Resize height to": "Resizes image to this height. If 0, height is inferred from either of two nearby sliders."
 }
 
 

javascript/imageviewer.js

@@ -148,8 +148,8 @@ function showGalleryImage() {
                 if(e && e.parentElement.tagName == 'DIV'){
                     e.style.cursor='pointer'
                     e.style.userSelect='none'
-                    e.addEventListener('click', function (evt) {
-                        if(!opts.js_modal_lightbox) return;
+                    e.addEventListener('mousedown', function (evt) {
+                        if(!opts.js_modal_lightbox || evt.button != 0) return;
                         modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed)
                         showModal(evt)
                     }, true);

javascript/ui.js

@@ -1,4 +1,4 @@
-// various functions for interation with ui.py not large enough to warrant putting them in separate files
+// various functions for interaction with ui.py not large enough to warrant putting them in separate files
 
 function set_theme(theme){
     gradioURL = window.location.href
@@ -19,7 +19,7 @@ function selected_gallery_index(){
 
 function extract_image_from_gallery(gallery){
     if(gallery.length == 1){
-        return gallery[0]
+        return [gallery[0]]
     }
 
     index = selected_gallery_index()
@@ -28,7 +28,7 @@ function extract_image_from_gallery(gallery){
         return [null]
     }
 
-    return gallery[index];
+    return [gallery[index]];
 }
 
 function args_to_array(args){
@@ -188,6 +188,17 @@ onUiUpdate(function(){
 		img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea");
 		img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button"));
 	}
+
+    show_all_pages = gradioApp().getElementById('settings_show_all_pages')
+    settings_tabs = gradioApp().querySelector('#settings div')
+    if(show_all_pages && settings_tabs){
+        settings_tabs.appendChild(show_all_pages)
+        show_all_pages.onclick = function(){
+            gradioApp().querySelectorAll('#settings > div').forEach(function(elem){
+                elem.style.display = "block";
+            })
+        }
+    }
 })
 
 let txt2img_textarea, img2img_textarea = undefined;

modules/api/api.py

@@ -1,11 +1,12 @@
 import base64
 import io
 import time
+import datetime
 import uvicorn
 from threading import Lock
 from io import BytesIO
 from gradio.processing_utils import decode_base64_to_file
-from fastapi import APIRouter, Depends, FastAPI, HTTPException
+from fastapi import APIRouter, Depends, FastAPI, HTTPException, Request, Response
 from fastapi.security import HTTPBasic, HTTPBasicCredentials
 from secrets import compare_digest
 
@@ -18,7 +19,7 @@ from modules.textual_inversion.textual_inversion import create_embedding, train_
 from modules.textual_inversion.preprocess import preprocess
 from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
 from PIL import PngImagePlugin,Image
-from modules.sd_models import checkpoints_list
+from modules.sd_models import checkpoints_list, find_checkpoint_config
 from modules.realesrgan_model import get_realesrgan_models
 from modules import devices
 from typing import List
@@ -67,6 +68,27 @@ def encode_pil_to_base64(image):
         bytes_data = output_bytes.getvalue()
     return base64.b64encode(bytes_data)
 
+def api_middleware(app: FastAPI):
+    @app.middleware("http")
+    async def log_and_time(req: Request, call_next):
+        ts = time.time()
+        res: Response = await call_next(req)
+        duration = str(round(time.time() - ts, 4))
+        res.headers["X-Process-Time"] = duration
+        endpoint = req.scope.get('path', 'err')
+        if shared.cmd_opts.api_log and endpoint.startswith('/sdapi'):
+            print('API {t} {code} {prot}/{ver} {method} {endpoint} {cli} {duration}'.format(
+                t = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f"),
+                code = res.status_code,
+                ver = req.scope.get('http_version', '0.0'),
+                cli = req.scope.get('client', ('0:0.0.0', 0))[0],
+                prot = req.scope.get('scheme', 'err'),
+                method = req.scope.get('method', 'err'),
+                endpoint = endpoint,
+                duration = duration,
+            ))
+        return res
+
 
 class Api:
     def __init__(self, app: FastAPI, queue_lock: Lock):
@@ -79,6 +101,7 @@ class Api:
         self.router = APIRouter()
         self.app = app
         self.queue_lock = queue_lock
+        api_middleware(self.app)
         self.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"], response_model=TextToImageResponse)
         self.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"], response_model=ImageToImageResponse)
         self.add_api_route("/sdapi/v1/extra-single-image", self.extras_single_image_api, methods=["POST"], response_model=ExtrasSingleImageResponse)
@@ -100,6 +123,7 @@ class Api:
         self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem])
         self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str])
         self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem])
+        self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse)
         self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"])
         self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse)
         self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse)
@@ -128,15 +152,14 @@ class Api:
         )
         if populate.sampler_name:
             populate.sampler_index = None  # prevent a warning later on
-        p = StableDiffusionProcessingTxt2Img(**vars(populate))
-        # Override object param
-
-        shared.state.begin()
 
         with self.queue_lock:
-            processed = process_images(p)
+            p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate))
+
+            shared.state.begin()
+            processed = process_images(p)
+            shared.state.end()
 
-        shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
@@ -163,16 +186,14 @@ class Api:
 
         args = vars(populate)
         args.pop('include_init_images', None)  # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine.
-        p = StableDiffusionProcessingImg2Img(**args)
-
-        p.init_images = [decode_base64_to_image(x) for x in init_images]
-
-        shared.state.begin()
 
         with self.queue_lock:
-            processed = process_images(p)
+            p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args)
+            p.init_images = [decode_base64_to_image(x) for x in init_images]
 
-        shared.state.end()
+            shared.state.begin()
+            processed = process_images(p)
+            shared.state.end()
 
         b64images = list(map(encode_pil_to_base64, processed.images))
 
@@ -305,7 +326,7 @@ class Api:
         return upscalers
 
     def get_sd_models(self):
-        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": x.config} for x in checkpoints_list.values()]
+        return [{"title":x.title, "model_name":x.model_name, "hash":x.hash, "filename": x.filename, "config": find_checkpoint_config(x)} for x in checkpoints_list.values()]
 
     def get_hypernetworks(self):
         return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
@@ -330,6 +351,26 @@ class Api:
     def get_artists(self):
         return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists]
 
+    def get_embeddings(self):
+        db = sd_hijack.model_hijack.embedding_db
+
+        def convert_embedding(embedding):
+            return {
+                "step": embedding.step,
+                "sd_checkpoint": embedding.sd_checkpoint,
+                "sd_checkpoint_name": embedding.sd_checkpoint_name,
+                "shape": embedding.shape,
+                "vectors": embedding.vectors,
+            }
+
+        def convert_embeddings(embeddings):
+            return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()}
+
+        return {
+            "loaded": convert_embeddings(db.word_embeddings),
+            "skipped": convert_embeddings(db.skipped_embeddings),
+        }
+
     def refresh_checkpoints(self):
         shared.refresh_checkpoints()
 

modules/api/models.py

@@ -249,3 +249,13 @@ class ArtistItem(BaseModel):
     score: float = Field(title="Score")
     category: str = Field(title="Category")
 
+class EmbeddingItem(BaseModel):
+    step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available")
+    sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available")
+    sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead")
+    shape: int = Field(title="Shape", description="The length of each individual vector in the embedding")
+    vectors: int = Field(title="Vectors", description="The number of vectors in the embedding")
+
+class EmbeddingsResponse(BaseModel):
+    loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model")
+    skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)")

modules/errors.py

@@ -2,9 +2,30 @@ import sys
 import traceback
 
 
+def print_error_explanation(message):
+    lines = message.strip().split("\n")
+    max_len = max([len(x) for x in lines])
+
+    print('=' * max_len, file=sys.stderr)
+    for line in lines:
+        print(line, file=sys.stderr)
+    print('=' * max_len, file=sys.stderr)
+
+
+def display(e: Exception, task):
+    print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr)
+    print(traceback.format_exc(), file=sys.stderr)
+
+    message = str(e)
+    if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
+        print_error_explanation("""
+The most likely cause of this is you are trying to load Stable Diffusion 2.0 model without specifying its connfig file.
+See https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20 for how to solve this.
+        """)
+
+
 def run(code, task):
     try:
         code()
     except Exception as e:
-        print(f"{task}: {type(e).__name__}", file=sys.stderr)
-        print(traceback.format_exc(), file=sys.stderr)
+        display(task, e)

modules/extras.py

@@ -19,8 +19,6 @@ from modules.shared import opts
 import modules.gfpgan_model
 from modules.ui import plaintext_to_html
 import modules.codeformer_model
-import piexif
-import piexif.helper
 import gradio as gr
 import safetensors.torch
 
@@ -58,6 +56,9 @@ cached_images: LruCache = LruCache(max_size=5)
 def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_dir, show_extras_results, gfpgan_visibility, codeformer_visibility, codeformer_weight, upscaling_resize, upscaling_resize_w, upscaling_resize_h, upscaling_crop, extras_upscaler_1, extras_upscaler_2, extras_upscaler_2_visibility, upscale_first: bool, save_output: bool = True):
     devices.torch_gc()
 
+    shared.state.begin()
+    shared.state.job = 'extras'
+
     imageArr = []
     # Also keep track of original file names
     imageNameArr = []
@@ -94,6 +95,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     # Extra operation definitions
 
     def run_gfpgan(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-gfpgan'
         restored_img = modules.gfpgan_model.gfpgan_fix_faces(np.array(image, dtype=np.uint8))
         res = Image.fromarray(restored_img)
 
@@ -104,6 +106,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def run_codeformer(image: Image.Image, info: str) -> Tuple[Image.Image, str]:
+        shared.state.job = 'extras-codeformer'
         restored_img = modules.codeformer_model.codeformer.restore(np.array(image, dtype=np.uint8), w=codeformer_weight)
         res = Image.fromarray(restored_img)
 
@@ -114,6 +117,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         return (res, info)
 
     def upscale(image, scaler_index, resize, mode, resize_w, resize_h, crop):
+        shared.state.job = 'extras-upscale'
         upscaler = shared.sd_upscalers[scaler_index]
         res = upscaler.scaler.upscale(image, resize, upscaler.data_path)
         if mode == 1 and crop:
@@ -180,6 +184,9 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
     for image, image_name in zip(imageArr, imageNameArr):
         if image is None:
             return outputs, "Please select an input image.", ''
+
+        shared.state.textinfo = f'Processing image {image_name}'
+        
         existing_pnginfo = image.info or {}
 
         image = image.convert("RGB")
@@ -193,6 +200,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
         else:
             basename = ''
 
+        if opts.enable_pnginfo: # append info before save
+            image.info = existing_pnginfo
+            image.info["extras"] = info
+
         if save_output:
             # Add upscaler name as a suffix.
             suffix = f"-{shared.sd_upscalers[extras_upscaler_1].name}" if shared.opts.use_upscaler_name_as_suffix else ""
@@ -203,10 +214,6 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
             images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True,
                             no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None, suffix=suffix)
 
-        if opts.enable_pnginfo:
-            image.info = existing_pnginfo
-            image.info["extras"] = info
-
         if extras_mode != 2 or show_extras_results :
             outputs.append(image)
 
@@ -242,6 +249,9 @@ def run_pnginfo(image):
 
 
 def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format):
+    shared.state.begin()
+    shared.state.job = 'model-merge'
+
     def weighted_sum(theta0, theta1, alpha):
         return ((1 - alpha) * theta0) + (alpha * theta1)
 
@@ -263,8 +273,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     theta_func1, theta_func2 = theta_funcs[interp_method]
 
     if theta_func1 and not tertiary_model_info:
+        shared.state.textinfo = "Failed: Interpolation method requires a tertiary model."
+        shared.state.end()
         return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
 
+    shared.state.textinfo = f"Loading {secondary_model_info.filename}..."
     print(f"Loading {secondary_model_info.filename}...")
     theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
 
@@ -281,6 +294,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
                     theta_1[key] = torch.zeros_like(theta_1[key])
         del theta_2
 
+    shared.state.textinfo = f"Loading {primary_model_info.filename}..."
     print(f"Loading {primary_model_info.filename}...")
     theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
 
@@ -291,6 +305,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
             a = theta_0[key]
             b = theta_1[key]
 
+            shared.state.textinfo = f'Merging layer {key}'
             # this enables merging an inpainting model (A) with another one (B);
             # where normal model would have 4 channels, for latenst space, inpainting model would
             # have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
@@ -330,6 +345,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
 
     output_modelname = os.path.join(ckpt_dir, filename)
 
+    shared.state.textinfo = f"Saving to {output_modelname}..."
     print(f"Saving to {output_modelname}...")
 
     _, extension = os.path.splitext(output_modelname)
@@ -341,4 +357,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam
     sd_models.list_models()
 
     print("Checkpoint saved.")
+    shared.state.textinfo = "Checkpoint saved to " + output_modelname
+    shared.state.end()
+
     return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]

modules/generation_parameters_copypaste.py

@@ -1,12 +1,13 @@
 import base64
 import io
+import math
 import os
 import re
 from pathlib import Path
 
 import gradio as gr
 from modules.shared import script_path
-from modules import shared
+from modules import shared, ui_tempdir
 import tempfile
 from PIL import Image
 
@@ -36,9 +37,12 @@ def quote(text):
 
 
 def image_from_url_text(filedata):
-    if type(filedata) == dict and filedata["is_file"]:
+    if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False):
+        filedata = filedata[0]
+
+    if type(filedata) == dict and filedata.get("is_file", False):
         filename = filedata["name"]
-        is_in_right_dir = any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in shared.demo.temp_dirs)
+        is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename)
         assert is_in_right_dir, 'trying to open image file outside of allowed directories'
 
         return Image.open(filename)
@@ -93,7 +97,7 @@ def integrate_settings_paste_fields(component_dict):
 def create_buttons(tabs_list):
     buttons = {}
     for tab in tabs_list:
-        buttons[tab] = gr.Button(f"Send to {tab}")
+        buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab")
     return buttons
 
 
@@ -102,35 +106,57 @@ def bind_buttons(buttons, send_image, send_generate_info):
     bind_list.append([buttons, send_image, send_generate_info])
 
 
+def send_image_and_dimensions(x):
+    if isinstance(x, Image.Image):
+        img = x
+    else:
+        img = image_from_url_text(x)
+
+    if shared.opts.send_size and isinstance(img, Image.Image):
+        w = img.width
+        h = img.height
+    else:
+        w = gr.update()
+        h = gr.update()
+
+    return img, w, h
+
+
 def run_bind():
-    for buttons, send_image, send_generate_info in bind_list:
+    for buttons, source_image_component, send_generate_info in bind_list:
         for tab in buttons:
             button = buttons[tab]
-            if send_image and paste_fields[tab]["init_img"]:
-                if type(send_image) == gr.Gallery:
-                    button.click(
-                        fn=lambda x: image_from_url_text(x),
-                        _js="extract_image_from_gallery",
-                        inputs=[send_image],
-                        outputs=[paste_fields[tab]["init_img"]],
-                    )
-                else:
-                    button.click(
-                        fn=lambda x: x,
-                        inputs=[send_image],
-                        outputs=[paste_fields[tab]["init_img"]],
-                    )
+            destination_image_component = paste_fields[tab]["init_img"]
+            fields = paste_fields[tab]["fields"]
 
-            if send_generate_info and paste_fields[tab]["fields"] is not None:
+            destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None)
+            destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None)
+
+            if source_image_component and destination_image_component:
+                if isinstance(source_image_component, gr.Gallery):
+                    func = send_image_and_dimensions if destination_width_component else image_from_url_text
+                    jsfunc = "extract_image_from_gallery"
+                else:
+                    func = send_image_and_dimensions if destination_width_component else lambda x: x
+                    jsfunc = None
+
+                button.click(
+                    fn=func,
+                    _js=jsfunc,
+                    inputs=[source_image_component],
+                    outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component],
+                )
+
+            if send_generate_info and fields is not None:
                 if send_generate_info in paste_fields:
-                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] +  (['Size-1', 'Size-2'] if shared.opts.send_size else []) + (["Seed"] if shared.opts.send_seed else [])
+                    paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else [])
                     button.click(
                         fn=lambda *x: x,
                         inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names],
-                        outputs=[field for field, name in paste_fields[tab]["fields"] if name in paste_field_names],
+                        outputs=[field for field, name in fields if name in paste_field_names],
                     )
                 else:
-                    connect_paste(button, paste_fields[tab]["fields"], send_generate_info)
+                    connect_paste(button, fields, send_generate_info)
 
             button.click(
                 fn=None,
@@ -164,6 +190,34 @@ def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
     return None
 
 
+def restore_old_hires_fix_params(res):
+    """for infotexts that specify old First pass size parameter, convert it into
+    width, height, and hr scale"""
+
+    firstpass_width = res.get('First pass size-1', None)
+    firstpass_height = res.get('First pass size-2', None)
+
+    if firstpass_width is None or firstpass_height is None:
+        return
+
+    firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
+    width = int(res.get("Size-1", 512))
+    height = int(res.get("Size-2", 512))
+
+    if firstpass_width == 0 or firstpass_height == 0:
+        # old algorithm for auto-calculating first pass size
+        desired_pixel_count = 512 * 512
+        actual_pixel_count = width * height
+        scale = math.sqrt(desired_pixel_count / actual_pixel_count)
+        firstpass_width = math.ceil(scale * width / 64) * 64
+        firstpass_height = math.ceil(scale * height / 64) * 64
+
+    res['Size-1'] = firstpass_width
+    res['Size-2'] = firstpass_height
+    res['Hires resize-1'] = width
+    res['Hires resize-2'] = height
+
+
 def parse_generation_parameters(x: str):
     """parses generation parameters string, the one you see in text field under the picture in UI:
 ```
@@ -221,6 +275,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         hypernet_hash = res.get("Hypernet hash", None)
         res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash)
 
+    if "Hires resize-1" not in res:
+        res["Hires resize-1"] = 0
+        res["Hires resize-2"] = 0
+
+    restore_old_hires_fix_params(res)
+
     return res
 
 

modules/hypernetworks/hypernetwork.py

@@ -402,10 +402,8 @@ def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None,
 
     shared.reload_hypernetworks()
 
-    return fn
 
-
-def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
     from modules import images
 
@@ -417,6 +415,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     shared.loaded_hypernetwork = Hypernetwork()
     shared.loaded_hypernetwork.load(path)
 
+    shared.state.job = "train-hypernetwork"
     shared.state.textinfo = "Initializing hypernetwork training..."
     shared.state.job_count = steps
 
@@ -447,6 +446,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
         return hypernetwork, filename
 
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
+    
+    clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else None
+    if clip_grad:
+        clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)
 
     # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
@@ -465,7 +468,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
         shared.parallel_processing_allowed = False
         shared.sd_model.cond_stage_model.to(devices.cpu)
         shared.sd_model.first_stage_model.to(devices.cpu)
-    
+
     weights = hypernetwork.weights()
     hypernetwork.train_mode()
 
@@ -524,6 +527,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                 if shared.state.interrupted:
                     break
 
+                if clip_grad:
+                    clip_grad_sched.step(hypernetwork.step)
+                
                 with devices.autocast():
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     if tag_drop_out != 0 or shuffle_tags:
@@ -538,14 +544,14 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
 
                     _loss_step += loss.item()
                 scaler.scale(loss).backward()
+                
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
-                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.7f}")
-                # scaler.unscale_(optimizer)
-                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
-                # torch.nn.utils.clip_grad_norm_(weights, max_norm=1.0)
-                # print(f"grad:{weights[0].grad.detach().cpu().abs().mean().item():.15f}")
+
+                if clip_grad:
+                    clip_grad(weights, clip_grad_sched.learn_rate)
+                
                 scaler.step(optimizer)
                 scaler.update()
                 hypernetwork.step += 1

modules/images.py

@@ -39,11 +39,14 @@ def image_grid(imgs, batch_size=1, rows=None):
 
     cols = math.ceil(len(imgs) / rows)
 
-    w, h = imgs[0].size
-    grid = Image.new('RGB', size=(cols * w, rows * h), color='black')
+    params = script_callbacks.ImageGridLoopParams(imgs, cols, rows)
+    script_callbacks.image_grid_callback(params)
 
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i % cols * w, i // cols * h))
+    w, h = imgs[0].size
+    grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black')
+
+    for i, img in enumerate(params.imgs):
+        grid.paste(img, box=(i % params.cols * w, i // params.cols * h))
 
     return grid
 
@@ -227,16 +230,32 @@ def draw_prompt_matrix(im, width, height, all_prompts):
     return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
 
 
-def resize_image(resize_mode, im, width, height):
+def resize_image(resize_mode, im, width, height, upscaler_name=None):
+    """
+    Resizes an image with the specified resize_mode, width, and height.
+
+    Args:
+        resize_mode: The mode to use when resizing the image.
+            0: Resize the image to the specified width and height.
+            1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
+            2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
+        im: The image to resize.
+        width: The width to resize the image to.
+        height: The height to resize the image to.
+        upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img.
+    """
+
+    upscaler_name = upscaler_name or opts.upscaler_for_img2img
+
     def resize(im, w, h):
-        if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L':
+        if upscaler_name is None or upscaler_name == "None" or im.mode == 'L':
             return im.resize((w, h), resample=LANCZOS)
 
         scale = max(w / im.width, h / im.height)
 
         if scale > 1.0:
-            upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img]
-            assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}"
+            upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
+            assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
 
             upscaler = upscalers[0]
             im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
@@ -525,6 +544,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
             image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
 
         elif extension.lower() in (".jpg", ".jpeg", ".webp"):
+            if image_to_save.mode == 'RGBA':
+                image_to_save = image_to_save.convert("RGB")
+
             image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality)
 
             if opts.enable_pnginfo and info is not None:

modules/img2img.py

@@ -162,4 +162,4 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro
     if opts.do_not_show_images:
         processed.images = []
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
+    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)

modules/interrogate.py

@@ -135,8 +135,9 @@ class InterrogateModels:
         return caption[0]
 
     def interrogate(self, pil_image):
-        res = None
-
+        res = ""
+        shared.state.begin()
+        shared.state.job = 'interrogate'
         try:
 
             if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -177,5 +178,6 @@ class InterrogateModels:
             res += "<error>"
 
         self.unload()
+        shared.state.end()
 
         return res

modules/memmon.py

@@ -71,10 +71,13 @@ class MemUsageMonitor(threading.Thread):
     def read(self):
         if not self.disabled:
             free, total = torch.cuda.mem_get_info()
+            self.data["free"] = free
             self.data["total"] = total
 
             torch_stats = torch.cuda.memory_stats(self.device)
+            self.data["active"] = torch_stats["active.all.current"]
             self.data["active_peak"] = torch_stats["active_bytes.all.peak"]
+            self.data["reserved"] = torch_stats["reserved_bytes.all.current"]
             self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"]
             self.data["system_peak"] = total - self.data["min_free"]
 

modules/modelloader.py

@@ -123,6 +123,23 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
         pass
 
 
+builtin_upscaler_classes = []
+forbidden_upscaler_classes = set()
+
+
+def list_builtin_upscalers():
+    load_upscalers()
+
+    builtin_upscaler_classes.clear()
+    builtin_upscaler_classes.extend(Upscaler.__subclasses__())
+
+
+def forbid_loaded_nonbuiltin_upscalers():
+    for cls in Upscaler.__subclasses__():
+        if cls not in builtin_upscaler_classes:
+            forbidden_upscaler_classes.add(cls)
+
+
 def load_upscalers():
     # We can only do this 'magic' method to dynamically load upscalers if they are referenced,
     # so we'll try to import any _model.py files before looking in __subclasses__
@@ -139,6 +156,9 @@ def load_upscalers():
     datas = []
     commandline_options = vars(shared.cmd_opts)
     for cls in Upscaler.__subclasses__():
+        if cls in forbidden_upscaler_classes:
+            continue
+
         name = cls.__name__
         cmd_name = f"{name.lower().replace('upscaler', '')}_models_path"
         scaler = cls(commandline_options.get(cmd_name, None))

modules/processing.py

@@ -76,6 +76,24 @@ def apply_overlay(image, paste_loc, index, overlays):
     return image
 
 
+def txt2img_image_conditioning(sd_model, x, width, height):
+    if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
+        # Dummy zero conditioning if we're not using inpainting model.
+        # Still takes up a bit of memory, but no encoder call.
+        # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
+        return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
+
+    # The "masked-image" in this case will just be all zeros since the entire image is masked.
+    image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
+    image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
+
+    # Add the fake full 1s mask to the first dimension.
+    image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
+    image_conditioning = image_conditioning.to(x.dtype)
+
+    return image_conditioning
+
+
 class StableDiffusionProcessing():
     """
     The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
@@ -136,28 +154,12 @@ class StableDiffusionProcessing():
         self.all_negative_prompts = None
         self.all_seeds = None
         self.all_subseeds = None
+        self.iteration = 0
 
     def txt2img_image_conditioning(self, x, width=None, height=None):
-        if self.sampler.conditioning_key not in {'hybrid', 'concat'}:
-            # Dummy zero conditioning if we're not using inpainting model.
-            # Still takes up a bit of memory, but no encoder call.
-            # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
-            return x.new_zeros(x.shape[0], 5, 1, 1)
+        self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
 
-        self.is_using_inpainting_conditioning = True
-
-        height = height or self.height
-        width = width or self.width
-
-        # The "masked-image" in this case will just be all zeros since the entire image is masked.
-        image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
-        image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning))
-
-        # Add the fake full 1s mask to the first dimension.
-        image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
-        image_conditioning = image_conditioning.to(x.dtype)
-
-        return image_conditioning
+        return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
 
     def depth2img_image_conditioning(self, source_image):
         # Use the AddMiDaS helper to Format our source image to suit the MiDaS model
@@ -239,7 +241,7 @@ class StableDiffusionProcessing():
 
 
 class Processed:
-    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
+    def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
         self.images = images_list
         self.prompt = p.prompt
         self.negative_prompt = p.negative_prompt
@@ -247,6 +249,7 @@ class Processed:
         self.subseed = subseed
         self.subseed_strength = p.subseed_strength
         self.info = info
+        self.comments = comments
         self.width = p.width
         self.height = p.height
         self.sampler_name = p.sampler_name
@@ -338,13 +341,14 @@ def slerp(val, low, high):
 
 
 def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
+    eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
     xs = []
 
     # if we have multiple seeds, this means we are working with batch size>1; this then
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
@@ -384,8 +388,8 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
         if sampler_noises is not None:
             cnt = p.sampler.number_of_needed_noises(p)
 
-            if opts.eta_noise_seed_delta > 0:
-                torch.manual_seed(seed + opts.eta_noise_seed_delta)
+            if eta_noise_seed_delta > 0:
+                torch.manual_seed(seed + eta_noise_seed_delta)
 
             for j in range(cnt):
                 sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
@@ -418,7 +422,7 @@ def fix_seed(p):
     p.subseed = get_fixed_seed(p.subseed)
 
 
-def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration=0, position_in_batch=0):
+def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
     index = position_in_batch + iteration * p.batch_size
 
     clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
@@ -544,6 +548,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             state.job_count = p.n_iter
 
         for n in range(p.n_iter):
+            p.iteration = n
+
             if state.skipped:
                 state.skipped = False
 
@@ -647,7 +653,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
     devices.torch_gc()
 
-    res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
+    res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
 
     if p.scripts is not None:
         p.scripts.postprocess(p, res)
@@ -658,76 +664,114 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
     sampler = None
 
-    def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
+    def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs):
         super().__init__(**kwargs)
         self.enable_hr = enable_hr
         self.denoising_strength = denoising_strength
-        self.firstphase_width = firstphase_width
-        self.firstphase_height = firstphase_height
+        self.hr_scale = hr_scale
+        self.hr_upscaler = hr_upscaler
+        self.hr_second_pass_steps = hr_second_pass_steps
+        self.hr_resize_x = hr_resize_x
+        self.hr_resize_y = hr_resize_y
+        self.hr_upscale_to_x = hr_resize_x
+        self.hr_upscale_to_y = hr_resize_y
+
+        if firstphase_width != 0 or firstphase_height != 0:
+            print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr)
+            self.hr_scale = self.width / firstphase_width
+            self.width = firstphase_width
+            self.height = firstphase_height
+
         self.truncate_x = 0
         self.truncate_y = 0
 
+
     def init(self, all_prompts, all_seeds, all_subseeds):
         if self.enable_hr:
-            if state.job_count == -1:
-                state.job_count = self.n_iter * 2
+            if self.hr_resize_x == 0 and self.hr_resize_y == 0:
+                self.extra_generation_params["Hires upscale"] = self.hr_scale
+                self.hr_upscale_to_x = int(self.width * self.hr_scale)
+                self.hr_upscale_to_y = int(self.height * self.hr_scale)
             else:
-                state.job_count = state.job_count * 2
+                self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
 
-            self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
-
-            if self.firstphase_width == 0 or self.firstphase_height == 0:
-                desired_pixel_count = 512 * 512
-                actual_pixel_count = self.width * self.height
-                scale = math.sqrt(desired_pixel_count / actual_pixel_count)
-                self.firstphase_width = math.ceil(scale * self.width / 64) * 64
-                self.firstphase_height = math.ceil(scale * self.height / 64) * 64
-                firstphase_width_truncated = int(scale * self.width)
-                firstphase_height_truncated = int(scale * self.height)
-
-            else:
-
-                width_ratio = self.width / self.firstphase_width
-                height_ratio = self.height / self.firstphase_height
-
-                if width_ratio > height_ratio:
-                    firstphase_width_truncated = self.firstphase_width
-                    firstphase_height_truncated = self.firstphase_width * self.height / self.width
+                if self.hr_resize_y == 0:
+                    self.hr_upscale_to_x = self.hr_resize_x
+                    self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+                elif self.hr_resize_x == 0:
+                    self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+                    self.hr_upscale_to_y = self.hr_resize_y
                 else:
-                    firstphase_width_truncated = self.firstphase_height * self.width / self.height
-                    firstphase_height_truncated = self.firstphase_height
+                    target_w = self.hr_resize_x
+                    target_h = self.hr_resize_y
+                    src_ratio = self.width / self.height
+                    dst_ratio = self.hr_resize_x / self.hr_resize_y
 
-            self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
-            self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
+                    if src_ratio < dst_ratio:
+                        self.hr_upscale_to_x = self.hr_resize_x
+                        self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
+                    else:
+                        self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
+                        self.hr_upscale_to_y = self.hr_resize_y
+
+                    self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
+                    self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
+
+            # special case: the user has chosen to do nothing
+            if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
+                self.enable_hr = False
+                self.denoising_strength = None
+                self.extra_generation_params.pop("Hires upscale", None)
+                self.extra_generation_params.pop("Hires resize", None)
+                return
+
+            if not state.processing_has_refined_job_count:
+                if state.job_count == -1:
+                    state.job_count = self.n_iter
+
+                shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
+                state.job_count = state.job_count * 2
+                state.processing_has_refined_job_count = True
+
+            if self.hr_second_pass_steps:
+                self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps
+
+            if self.hr_upscaler is not None:
+                self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
 
     def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
+        latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest")
+        if self.enable_hr and latent_scale_mode is None:
+            assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
+
+        x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
+
         if not self.enable_hr:
-            x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-            samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
             return samples
 
-        x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
-        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
+        target_width = self.hr_upscale_to_x
+        target_height = self.hr_upscale_to_y
 
-        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
-
-        """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
         def save_intermediate(image, index):
+            """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
+
             if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
                 return
 
             if not isinstance(image, Image.Image):
-                image = sd_samplers.sample_to_image(image, index)
+                image = sd_samplers.sample_to_image(image, index, approximation=0)
 
-            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
+            info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
+            images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
 
-        if opts.use_scale_latent_for_hires_fix:
+        if latent_scale_mode is not None:
             for i in range(samples.shape[0]):
                 save_intermediate(samples, i)
 
-            samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
+            samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
 
             # Avoid making the inpainting conditioning unless necessary as
             # this does need some extra compute to decode / encode the image again.
@@ -747,7 +791,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
                 save_intermediate(image, i)
 
-                image = images.resize_image(0, image, self.width, self.height)
+                image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
                 image = np.array(image).astype(np.float32) / 255.0
                 image = np.moveaxis(image, 2, 0)
                 batch_images.append(image)
@@ -764,13 +808,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
 
         self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
 
-        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
+        samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
+
+        noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
 
         # GC now before running the next img2img to prevent running out of memory
         x = None
         devices.torch_gc()
 
-        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning)
+        samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
 
         return samples
 

modules/script_callbacks.py

@@ -51,6 +51,13 @@ class UiTrainTabParams:
         self.txt2img_preview_params = txt2img_preview_params
 
 
+class ImageGridLoopParams:
+    def __init__(self, imgs, cols, rows):
+        self.imgs = imgs
+        self.cols = cols
+        self.rows = rows
+
+
 ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
 callback_map = dict(
     callbacks_app_started=[],
@@ -63,6 +70,7 @@ callback_map = dict(
     callbacks_cfg_denoiser=[],
     callbacks_before_component=[],
     callbacks_after_component=[],
+    callbacks_image_grid=[],
 )
 
 
@@ -155,6 +163,14 @@ def after_component_callback(component, **kwargs):
             report_exception(c, 'after_component_callback')
 
 
+def image_grid_callback(params: ImageGridLoopParams):
+    for c in callback_map['callbacks_image_grid']:
+        try:
+            c.callback(params)
+        except Exception:
+            report_exception(c, 'image_grid')
+
+
 def add_callback(callbacks, fun):
     stack = [x for x in inspect.stack() if x.filename != __file__]
     filename = stack[0].filename if len(stack) > 0 else 'unknown file'
@@ -255,3 +271,11 @@ def on_before_component(callback):
 def on_after_component(callback):
     """register a function to be called after a component is created. See on_before_component for more."""
     add_callback(callback_map['callbacks_after_component'], callback)
+
+
+def on_image_grid(callback):
+    """register a function to be called before making an image grid.
+    The callback is called with one argument:
+       - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified.
+    """
+    add_callback(callback_map['callbacks_image_grid'], callback)

modules/sd_hijack.py

@@ -5,7 +5,7 @@ import modules.textual_inversion.textual_inversion
 from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
 
 from modules.sd_hijack_optimizations import invokeAI_mps_available
 
@@ -35,26 +35,35 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
+    
+    optimization_method = None
 
     if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)):
         print("Applying xformers cross attention optimization.")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
         ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
+        optimization_method = 'xformers'
     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
+        optimization_method = '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
+            optimization_method = 'V1'
         else:
             print("Applying cross attention optimization (InvokeAI).")
             ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
+            optimization_method = '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).")
         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
+        optimization_method = 'Doggettx'
+
+    return optimization_method
 
 
 def undo_optimizations():
@@ -68,27 +77,37 @@ def fix_checkpoint():
     ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward
     ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward
 
+
 class StableDiffusionModelHijack:
     fixes = None
     comments = []
     layers = None
     circular_enabled = False
     clip = None
+    optimization_method = None
 
     embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
 
     def hijack(self, m):
-        if type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+            model_embeddings = m.cond_stage_model.roberta.embeddings
+            model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+            m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
+
+        elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
             m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+
         elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
             m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
             m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
 
-        self.clip = m.cond_stage_model
+        self.optimization_method = apply_optimizations()
 
-        apply_optimizations()
+        self.clip = m.cond_stage_model
+        
         fix_checkpoint()
 
         def flatten(el):
@@ -101,7 +120,11 @@ class StableDiffusionModelHijack:
         self.layers = flatten(m)
 
     def undo_hijack(self, m):
-        if type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
+
+        if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
+            m.cond_stage_model = m.cond_stage_model.wrapped 
+
+        elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
@@ -129,8 +152,8 @@ class StableDiffusionModelHijack:
 
     def tokenize(self, text):
         _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
-        return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
 
+        return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
 
 
 class EmbeddingsWithFixes(torch.nn.Module):

modules/sd_hijack_clip.py

@@ -5,7 +5,6 @@ import torch
 from modules import prompt_parser, devices
 from modules.shared import opts
 
-
 def get_target_prompt_token_count(token_count):
     return math.ceil(max(token_count, 1) / 75) * 75
 
@@ -254,10 +253,13 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def __init__(self, wrapped, hijack):
         super().__init__(wrapped, hijack)
         self.tokenizer = wrapped.tokenizer
-        self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+
+        vocab = self.tokenizer.get_vocab()
+
+        self.comma_token = vocab.get(',</w>', None)
 
         self.token_mults = {}
-        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]
+        tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k]
         for text, ident in tokens_with_parens:
             mult = 1.0
             for c in text:
@@ -296,6 +298,6 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
     def encode_embedding_init_text(self, init_text, nvpt):
         embedding_layer = self.wrapped.transformer.text_model.embeddings
         ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
-        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
 
         return embedded

modules/sd_hijack_inpainting.py

@@ -12,191 +12,6 @@ from ldm.models.diffusion.ddpm import LatentDiffusion
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ddim import DDIMSampler, noise_like
 
-# =================================================================================================
-# Monkey patch DDIMSampler methods from RunwayML repo directly.
-# Adapted from:
-# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py
-# =================================================================================================
-@torch.no_grad()
-def sample_ddim(self,
-            S,
-            batch_size,
-            shape,
-            conditioning=None,
-            callback=None,
-            normals_sequence=None,
-            img_callback=None,
-            quantize_x0=False,
-            eta=0.,
-            mask=None,
-            x0=None,
-            temperature=1.,
-            noise_dropout=0.,
-            score_corrector=None,
-            corrector_kwargs=None,
-            verbose=True,
-            x_T=None,
-            log_every_t=100,
-            unconditional_guidance_scale=1.,
-            unconditional_conditioning=None,
-            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-            **kwargs
-            ):
-    if conditioning is not None:
-        if isinstance(conditioning, dict):
-            ctmp = conditioning[list(conditioning.keys())[0]]
-            while isinstance(ctmp, list):
-                ctmp = ctmp[0]
-            cbs = ctmp.shape[0]
-            if cbs != batch_size:
-                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-        else:
-            if conditioning.shape[0] != batch_size:
-                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-    # sampling
-    C, H, W = shape
-    size = (batch_size, C, H, W)
-    print(f'Data shape for DDIM sampling is {size}, eta {eta}')
-
-    samples, intermediates = self.ddim_sampling(conditioning, size,
-                                                callback=callback,
-                                                img_callback=img_callback,
-                                                quantize_denoised=quantize_x0,
-                                                mask=mask, x0=x0,
-                                                ddim_use_original_steps=False,
-                                                noise_dropout=noise_dropout,
-                                                temperature=temperature,
-                                                score_corrector=score_corrector,
-                                                corrector_kwargs=corrector_kwargs,
-                                                x_T=x_T,
-                                                log_every_t=log_every_t,
-                                                unconditional_guidance_scale=unconditional_guidance_scale,
-                                                unconditional_conditioning=unconditional_conditioning,
-                                                )
-    return samples, intermediates
-
-@torch.no_grad()
-def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
-                    temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
-                    unconditional_guidance_scale=1., unconditional_conditioning=None):
-    b, *_, device = *x.shape, x.device
-
-    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
-        e_t = self.model.apply_model(x, t, c)
-    else:
-        x_in = torch.cat([x] * 2)
-        t_in = torch.cat([t] * 2)
-        if isinstance(c, dict):
-            assert isinstance(unconditional_conditioning, dict)
-            c_in = dict()
-            for k in c:
-                if isinstance(c[k], list):
-                    c_in[k] = [
-                        torch.cat([unconditional_conditioning[k][i], c[k][i]])
-                        for i in range(len(c[k]))
-                    ]
-                else:
-                    c_in[k] = torch.cat([unconditional_conditioning[k], c[k]])
-        else:
-            c_in = torch.cat([unconditional_conditioning, c])
-        e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
-        e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
-
-    if score_corrector is not None:
-        assert self.model.parameterization == "eps"
-        e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
-
-    alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
-    alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
-    sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
-    sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
-    # select parameters corresponding to the currently considered timestep
-    a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
-    a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
-    sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
-    sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
-
-    # current prediction for x_0
-    pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
-    if quantize_denoised:
-        pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
-    # direction pointing to x_t
-    dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
-    noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
-    if noise_dropout > 0.:
-        noise = torch.nn.functional.dropout(noise, p=noise_dropout)
-    x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
-    return x_prev, pred_x0
-
-
-# =================================================================================================
-# Monkey patch PLMSSampler methods.
-# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes.
-# Adapted from:
-# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py
-# =================================================================================================
-@torch.no_grad()
-def sample_plms(self,
-            S,
-            batch_size,
-            shape,
-            conditioning=None,
-            callback=None,
-            normals_sequence=None,
-            img_callback=None,
-            quantize_x0=False,
-            eta=0.,
-            mask=None,
-            x0=None,
-            temperature=1.,
-            noise_dropout=0.,
-            score_corrector=None,
-            corrector_kwargs=None,
-            verbose=True,
-            x_T=None,
-            log_every_t=100,
-            unconditional_guidance_scale=1.,
-            unconditional_conditioning=None,
-            # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
-            **kwargs
-            ):
-    if conditioning is not None:
-        if isinstance(conditioning, dict):
-            ctmp = conditioning[list(conditioning.keys())[0]]
-            while isinstance(ctmp, list):
-                ctmp = ctmp[0]
-            cbs = ctmp.shape[0]
-            if cbs != batch_size:
-                print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
-        else:
-            if conditioning.shape[0] != batch_size:
-                print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
-
-    self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
-    # sampling
-    C, H, W = shape
-    size = (batch_size, C, H, W)
-    print(f'Data shape for PLMS sampling is {size}')
-
-    samples, intermediates = self.plms_sampling(conditioning, size,
-                                                callback=callback,
-                                                img_callback=img_callback,
-                                                quantize_denoised=quantize_x0,
-                                                mask=mask, x0=x0,
-                                                ddim_use_original_steps=False,
-                                                noise_dropout=noise_dropout,
-                                                temperature=temperature,
-                                                score_corrector=score_corrector,
-                                                corrector_kwargs=corrector_kwargs,
-                                                x_T=x_T,
-                                                log_every_t=log_every_t,
-                                                unconditional_guidance_scale=unconditional_guidance_scale,
-                                                unconditional_conditioning=unconditional_conditioning,
-                                                )
-    return samples, intermediates
-
 
 @torch.no_grad()
 def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
@@ -280,61 +95,17 @@ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=F
 
     return x_prev, pred_x0, e_t
 
-# =================================================================================================
-# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config.
-# Adapted from:
-# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py
-# =================================================================================================
-
-@torch.no_grad()
-def get_unconditional_conditioning(self, batch_size, null_label=None):
-    if null_label is not None:
-        xc = null_label
-        if isinstance(xc, ListConfig):
-            xc = list(xc)
-        if isinstance(xc, dict) or isinstance(xc, list):
-            c = self.get_learned_conditioning(xc)
-        else:
-            if hasattr(xc, "to"):
-                xc = xc.to(self.device)
-            c = self.get_learned_conditioning(xc)
-    else:
-        # todo: get null label from cond_stage_model
-        raise NotImplementedError()
-    c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device)
-    return c
-
-
-class LatentInpaintDiffusion(LatentDiffusion):
-    def __init__(
-        self,
-        concat_keys=("mask", "masked_image"),
-        masked_image_key="masked_image",
-        *args,
-        **kwargs,
-    ):
-        super().__init__(*args, **kwargs)
-        self.masked_image_key = masked_image_key
-        assert self.masked_image_key in concat_keys
-        self.concat_keys = concat_keys
-
 
 def should_hijack_inpainting(checkpoint_info):
+    from modules import sd_models
+
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
-    cfg_basename = os.path.basename(checkpoint_info.config).lower()
+    cfg_basename = os.path.basename(sd_models.find_checkpoint_config(checkpoint_info)).lower()
+
     return "inpainting" in ckpt_basename and not "inpainting" in cfg_basename
 
 
 def do_inpainting_hijack():
-    # most of this stuff seems to no longer be needed because it is already included into SD2.0
     # p_sample_plms is needed because PLMS can't work with dicts as conditionings
-    # this file should be cleaned up later if everything turns out to work fine
-
-    # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning
-    # ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion
-
-    # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim
-    # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim
 
     ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms
-    # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms

modules/sd_hijack_xlmr.py

@@ -0,0 +1,34 @@
+import open_clip.tokenizer
+import torch
+
+from modules import sd_hijack_clip, devices
+from modules.shared import opts
+
+
+class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
+    def __init__(self, wrapped, hijack):
+        super().__init__(wrapped, hijack)
+
+        self.id_start = wrapped.config.bos_token_id
+        self.id_end = wrapped.config.eos_token_id
+        self.id_pad = wrapped.config.pad_token_id
+
+        self.comma_token = self.tokenizer.get_vocab().get(',', None)  # alt diffusion doesn't have </w> bits for comma
+
+    def encode_with_transformers(self, tokens):
+        # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
+        # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
+        # layer to work with - you have to use the last
+
+        attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
+        features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
+        z = features['projection_state']
+
+        return z
+
+    def encode_embedding_init_text(self, init_text, nvpt):
+        embedding_layer = self.wrapped.roberta.embeddings
+        ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
+        embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
+
+        return embedded

modules/sd_models.py

@@ -20,7 +20,7 @@ from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inp
 model_dir = "Stable-diffusion"
 model_path = os.path.abspath(os.path.join(models_path, model_dir))
 
-CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name', 'config'])
+CheckpointInfo = namedtuple("CheckpointInfo", ['filename', 'title', 'hash', 'model_name'])
 checkpoints_list = {}
 checkpoints_loaded = collections.OrderedDict()
 
@@ -48,6 +48,14 @@ def checkpoint_tiles():
     return sorted([x.title for x in checkpoints_list.values()], key = alphanumeric_key)
 
 
+def find_checkpoint_config(info):
+    config = os.path.splitext(info.filename)[0] + ".yaml"
+    if os.path.exists(config):
+        return config
+
+    return shared.cmd_opts.config
+
+
 def list_models():
     checkpoints_list.clear()
     model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
@@ -73,7 +81,7 @@ def list_models():
     if os.path.exists(cmd_ckpt):
         h = model_hash(cmd_ckpt)
         title, short_model_name = modeltitle(cmd_ckpt, h)
-        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name, shared.cmd_opts.config)
+        checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, short_model_name)
         shared.opts.data['sd_model_checkpoint'] = title
     elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
         print(f"Checkpoint in --ckpt argument not found (Possible it was moved to {model_path}: {cmd_ckpt}", file=sys.stderr)
@@ -81,12 +89,7 @@ def list_models():
         h = model_hash(filename)
         title, short_model_name = modeltitle(filename, h)
 
-        basename, _ = os.path.splitext(filename)
-        config = basename + ".yaml"
-        if not os.path.exists(config):
-            config = shared.cmd_opts.config
-
-        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name, config)
+        checkpoints_list[title] = CheckpointInfo(filename, title, h, short_model_name)
 
 
 def get_closet_checkpoint_match(searchString):
@@ -168,7 +171,10 @@ def get_state_dict_from_checkpoint(pl_sd):
 def read_state_dict(checkpoint_file, print_global_state=False, map_location=None):
     _, extension = os.path.splitext(checkpoint_file)
     if extension.lower() == ".safetensors":
-        pl_sd = safetensors.torch.load_file(checkpoint_file, device=map_location or shared.weight_load_location)
+        device = map_location or shared.weight_load_location
+        if device is None:
+            device = devices.get_cuda_device_string() if torch.cuda.is_available() else "cpu"
+        pl_sd = safetensors.torch.load_file(checkpoint_file, device=device)
     else:
         pl_sd = torch.load(checkpoint_file, map_location=map_location or shared.weight_load_location)
 
@@ -228,6 +234,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"):
     model.sd_model_checkpoint = checkpoint_file
     model.sd_checkpoint_info = checkpoint_info
 
+    model.logvar = model.logvar.to(devices.device)  # fix for training
+
     sd_vae.delete_base_vae()
     sd_vae.clear_loaded_vae()
     vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file)
@@ -276,12 +284,14 @@ def enable_midas_autodownload():
 
     midas.api.load_model = load_model_wrapper
 
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
+    checkpoint_config = find_checkpoint_config(checkpoint_info)
 
-    if checkpoint_info.config != shared.cmd_opts.config:
-        print(f"Loading config from: {checkpoint_info.config}")
+    if checkpoint_config != shared.cmd_opts.config:
+        print(f"Loading config from: {checkpoint_config}")
 
     if shared.sd_model:
         sd_hijack.model_hijack.undo_hijack(shared.sd_model)
@@ -289,7 +299,7 @@ def load_model(checkpoint_info=None):
         gc.collect()
         devices.torch_gc()
 
-    sd_config = OmegaConf.load(checkpoint_info.config)
+    sd_config = OmegaConf.load(checkpoint_config)
     
     if should_hijack_inpainting(checkpoint_info):
         # Hardcoded config for now...
@@ -298,9 +308,6 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.in_channels = 9
         sd_config.model.params.finetune_keys = None
 
-        # Create a "fake" config with a different name so that we know to unload it when switching models.
-        checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
-
     if not hasattr(sd_config.model.params, "use_ema"):
         sd_config.model.params.use_ema = False
 
@@ -310,6 +317,7 @@ def load_model(checkpoint_info=None):
         sd_config.model.params.unet_config.params.use_fp16 = False
 
     sd_model = instantiate_from_config(sd_config.model)
+
     load_model_weights(sd_model, checkpoint_info)
 
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
@@ -322,23 +330,29 @@ def load_model(checkpoint_info=None):
     sd_model.eval()
     shared.sd_model = sd_model
 
+    sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)  # Reload embeddings after model load as they may or may not fit the model
+
     script_callbacks.model_loaded_callback(sd_model)
 
     print("Model loaded.")
+
     return sd_model
 
 
 def reload_model_weights(sd_model=None, info=None):
     from modules import lowvram, devices, sd_hijack
     checkpoint_info = info or select_checkpoint()
- 
+
     if not sd_model:
         sd_model = shared.sd_model
 
+    current_checkpoint_info = sd_model.sd_checkpoint_info
+    checkpoint_config = find_checkpoint_config(current_checkpoint_info)
+
     if sd_model.sd_model_checkpoint == checkpoint_info.filename:
         return
 
-    if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
+    if checkpoint_config != find_checkpoint_config(checkpoint_info) or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
         del sd_model
         checkpoints_loaded.clear()
         load_model(checkpoint_info)
@@ -351,13 +365,19 @@ def reload_model_weights(sd_model=None, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
-    load_model_weights(sd_model, checkpoint_info)
+    try:
+        load_model_weights(sd_model, checkpoint_info)
+    except Exception as e:
+        print("Failed to load checkpoint, restoring previous")
+        load_model_weights(sd_model, current_checkpoint_info)
+        raise
+    finally:
+        sd_hijack.model_hijack.hijack(sd_model)
+        script_callbacks.model_loaded_callback(sd_model)
 
-    sd_hijack.model_hijack.hijack(sd_model)
-    script_callbacks.model_loaded_callback(sd_model)
-
-    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
-        sd_model.to(devices.device)
+        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
+            sd_model.to(devices.device)
 
     print("Weights loaded.")
+
     return sd_model

modules/sd_samplers.py

@@ -97,8 +97,9 @@ sampler_extra_params = {
 
 def setup_img2img_steps(p, steps=None):
     if opts.img2img_fix_steps or steps is not None:
-        steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
-        t_enc = p.steps - 1
+        requested_steps = (steps or p.steps)
+        steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
+        t_enc = requested_steps - 1
     else:
         steps = p.steps
         t_enc = int(min(p.denoising_strength, 0.999) * steps)
@@ -465,7 +466,9 @@ class KDiffusionSampler:
         if p.sampler_noise_scheduler_override:
             sigmas = p.sampler_noise_scheduler_override(steps)
         elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
-            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
+            sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
+
+            sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
         else:
             sigmas = self.model_wrap.get_sigmas(steps)
 

modules/sd_vae.py

@@ -1,5 +1,6 @@
 import torch
 import os
+import collections
 from collections import namedtuple
 from modules import shared, devices, script_callbacks
 from modules.paths import models_path
@@ -30,6 +31,7 @@ base_vae = None
 loaded_vae_file = None
 checkpoint_info = None
 
+checkpoints_loaded = collections.OrderedDict()
 
 def get_base_vae(model):
     if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
@@ -149,13 +151,30 @@ def load_vae(model, vae_file=None):
     global first_load, vae_dict, vae_list, loaded_vae_file
     # save_settings = False
 
+    cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
+
     if vae_file:
-        assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
-        print(f"Loading VAE weights from: {vae_file}")
-        store_base_vae(model)
-        vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
-        vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-        _load_vae_dict(model, vae_dict_1)
+        if cache_enabled and vae_file in checkpoints_loaded:
+            # use vae checkpoint cache
+            print(f"Loading VAE weights [{get_filename(vae_file)}] from cache")
+            store_base_vae(model)
+            _load_vae_dict(model, checkpoints_loaded[vae_file])
+        else:
+            assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}"
+            print(f"Loading VAE weights from: {vae_file}")
+            store_base_vae(model)
+            vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location)
+            vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys}
+            _load_vae_dict(model, vae_dict_1)
+
+            if cache_enabled:
+                # cache newly loaded vae
+                checkpoints_loaded[vae_file] = vae_dict_1.copy()
+
+        # clean up cache if limit is reached
+        if cache_enabled:
+            while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
+                checkpoints_loaded.popitem(last=False)  # LRU
 
         # If vae used is not in dict, update it
         # It will be removed on refresh though

modules/shared.py

@@ -14,7 +14,7 @@ import modules.interrogate
 import modules.memmon
 import modules.styles
 import modules.devices as devices
-from modules import localization, sd_vae, extensions, script_loading
+from modules import localization, sd_vae, extensions, script_loading, errors
 from modules.paths import models_path, script_path, sd_path
 
 
@@ -23,7 +23,7 @@ demo = None
 sd_model_file = os.path.join(script_path, 'model.ckpt')
 default_sd_model_file = sd_model_file
 parser = argparse.ArgumentParser()
-parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",)
+parser.add_argument("--config", type=str, default=os.path.join(script_path, "configs/v1-inference.yaml"), help="path to config which constructs model",)
 parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
 parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
 parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
@@ -82,6 +82,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode
 parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False)
 parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)")
 parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
+parser.add_argument("--api-log", action='store_true', help="use api-log=True to enable logging of all API requests")
 parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui")
 parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI")
 parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None)
@@ -109,6 +110,17 @@ restricted_opts = {
     "outdir_save",
 }
 
+ui_reorder_categories = [
+    "sampler",
+    "dimensions",
+    "cfg",
+    "seed",
+    "checkboxes",
+    "hires_fix",
+    "batch",
+    "scripts",
+]
+
 cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
 
 devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
@@ -141,6 +153,7 @@ class State:
     job = ""
     job_no = 0
     job_count = 0
+    processing_has_refined_job_count = False
     job_timestamp = '0'
     sampling_step = 0
     sampling_steps = 0
@@ -168,9 +181,10 @@ class State:
     def dict(self):
         obj = {
             "skipped": self.skipped,
-            "interrupted": self.skipped,
+            "interrupted": self.interrupted,
             "job": self.job,
             "job_count": self.job_count,
+            "job_timestamp": self.job_timestamp,
             "job_no": self.job_no,
             "sampling_step": self.sampling_step,
             "sampling_steps": self.sampling_steps,
@@ -181,6 +195,7 @@ class State:
     def begin(self):
         self.sampling_step = 0
         self.job_count = -1
+        self.processing_has_refined_job_count = False
         self.job_no = 0
         self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
         self.current_latent = None
@@ -201,12 +216,13 @@ class State:
 
     """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
     def set_current_image(self):
+        if not parallel_processing_allowed:
+            return
+
         if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0:
             self.do_set_current_image()
 
     def do_set_current_image(self):
-        if not parallel_processing_allowed:
-            return
         if self.current_latent is None:
             return
 
@@ -218,6 +234,7 @@ class State:
 
         self.current_image_sampling_step = self.sampling_step
 
+
 state = State()
 
 artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv'))
@@ -327,7 +344,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
     "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
 }))
 
 options_templates.update(options_section(('face-restoration', "Face restoration"), {
@@ -345,7 +361,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
@@ -356,6 +372,7 @@ options_templates.update(options_section(('training', "Training"), {
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
     "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
     "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list),
     "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
     "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
@@ -367,13 +384,17 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", gr.ColorPicker, {}),
     "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."),
     "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 }),
     'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", 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()}),
 }))
 
+options_templates.update(options_section(('compatibility', "Compatibility"), {
+    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
+    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
+}))
+
 options_templates.update(options_section(('interrogate', "Interrogate Options"), {
     "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
     "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
@@ -405,7 +426,10 @@ options_templates.update(options_section(('ui', "User interface"), {
     "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
     "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
     "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
+    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"),
+    "dimensions_and_batch_together": OptionInfo(True, "Show Witdth/Height and Batch sliders in same row"),
     'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
+    'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/ing2img UI item order"),
     'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)),
 }))
 
@@ -476,7 +500,12 @@ class Options:
             return False
 
         if self.data_labels[key].onchange is not None:
-            self.data_labels[key].onchange()
+            try:
+                self.data_labels[key].onchange()
+            except Exception as e:
+                errors.display(e, f"changing setting {key} to {value}")
+                setattr(self, key, oldval)
+                return False
 
         return True
 
@@ -539,6 +568,15 @@ opts = Options()
 if os.path.exists(config_filename):
     opts.load(config_filename)
 
+latent_upscale_default_mode = "Latent"
+latent_upscale_modes = {
+    "Latent": {"mode": "bilinear", "antialias": False},
+    "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
+    "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
+    "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
+    "Latent (nearest)": {"mode": "nearest", "antialias": False},
+}
+
 sd_upscalers = []
 
 sd_model = None
@@ -572,7 +610,7 @@ class TotalTQDM:
             return
         if self._tqdm is None:
             self.reset()
-        self._tqdm.total=new_total
+        self._tqdm.total = new_total
 
     def clear(self):
         if self._tqdm is not None:

modules/textual_inversion/learn_schedule.py

@@ -58,14 +58,19 @@ class LearnRateScheduler:
 
         self.finished = False
 
-    def apply(self, optimizer, step_number):
+    def step(self, step_number):
         if step_number < self.end_step:
-            return
+            return False
 
         try:
             (self.learn_rate, self.end_step) = next(self.schedules)
-        except Exception:
+        except StopIteration:
             self.finished = True
+            return False
+        return True
+
+    def apply(self, optimizer, step_number):
+        if not self.step(step_number):
             return
 
         if self.verbose:

modules/textual_inversion/preprocess.py

@@ -124,6 +124,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre
 
     files = listfiles(src)
 
+    shared.state.job = "preprocess"
     shared.state.textinfo = "Preprocessing..."
     shared.state.job_count = len(files)
 

modules/textual_inversion/textual_inversion.py

@@ -23,9 +23,12 @@ class Embedding:
         self.vec = vec
         self.name = name
         self.step = step
+        self.shape = None
+        self.vectors = 0
         self.cached_checksum = None
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
+        self.optimizer_state_dict = None
 
     def save(self, filename):
         embedding_data = {
@@ -39,6 +42,13 @@ class Embedding:
 
         torch.save(embedding_data, filename)
 
+        if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
+            optimizer_saved_dict = {
+                'hash': self.checksum(),
+                'optimizer_state_dict': self.optimizer_state_dict,
+            }
+            torch.save(optimizer_saved_dict, filename + '.optim')
+
     def checksum(self):
         if self.cached_checksum is not None:
             return self.cached_checksum
@@ -57,8 +67,10 @@ class EmbeddingDatabase:
     def __init__(self, embeddings_dir):
         self.ids_lookup = {}
         self.word_embeddings = {}
+        self.skipped_embeddings = {}
         self.dir_mtime = None
         self.embeddings_dir = embeddings_dir
+        self.expected_shape = -1
 
     def register_embedding(self, embedding, model):
 
@@ -75,21 +87,26 @@ class EmbeddingDatabase:
 
         return embedding
 
-    def load_textual_inversion_embeddings(self):
+    def get_expected_shape(self):
+        vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
+        return vec.shape[1]
+
+    def load_textual_inversion_embeddings(self, force_reload = False):
         mt = os.path.getmtime(self.embeddings_dir)
-        if self.dir_mtime is not None and mt <= self.dir_mtime:
+        if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime:
             return
 
         self.dir_mtime = mt
         self.ids_lookup.clear()
         self.word_embeddings.clear()
+        self.skipped_embeddings.clear()
+        self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
-            name = os.path.splitext(filename)[0]
+            name, ext = os.path.splitext(filename)
+            ext = ext.upper()
 
-            data = []
-
-            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
+            if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 embed_image = Image.open(path)
                 if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
                     data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
@@ -97,8 +114,10 @@ class EmbeddingDatabase:
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
-            else:
+            elif ext in ['.BIN', '.PT']:
                 data = torch.load(path, map_location="cpu")
+            else:
+                return
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -122,7 +141,13 @@ class EmbeddingDatabase:
             embedding.step = data.get('step', None)
             embedding.sd_checkpoint = data.get('sd_checkpoint', None)
             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
-            self.register_embedding(embedding, shared.sd_model)
+            embedding.vectors = vec.shape[0]
+            embedding.shape = vec.shape[-1]
+
+            if self.expected_shape == -1 or self.expected_shape == embedding.shape:
+                self.register_embedding(embedding, shared.sd_model)
+            else:
+                self.skipped_embeddings[name] = embedding
 
         for fn in os.listdir(self.embeddings_dir):
             try:
@@ -137,8 +162,9 @@ class EmbeddingDatabase:
                 print(traceback.format_exc(), file=sys.stderr)
                 continue
 
-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
-        print("Embeddings:", ', '.join(self.word_embeddings.keys()))
+        print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
+        if len(self.skipped_embeddings) > 0:
+            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
 
     def find_embedding_at_position(self, tokens, offset):
         token = tokens[offset]
@@ -225,11 +251,12 @@ def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, dat
     if save_model_every or create_image_every:
         assert log_directory, "Log directory is empty"
 
-def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
     save_embedding_every = save_embedding_every or 0
     create_image_every = create_image_every or 0
     validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
 
+    shared.state.job = "train-embedding"
     shared.state.textinfo = "Initializing textual inversion training..."
     shared.state.job_count = steps
 
@@ -267,7 +294,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
         return embedding, filename
     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)
 
-   # dataset loading may take a while, so input validations and early returns should be done before this
+    clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \
+        torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \
+        None
+    if clip_grad:
+        clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)
+    # dataset loading may take a while, so input validations and early returns should be done before this
     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
     old_parallel_processing_allowed = shared.parallel_processing_allowed
 
@@ -285,6 +317,19 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
+    if shared.opts.save_optimizer_state:
+        optimizer_state_dict = None
+        if os.path.exists(filename + '.optim'):
+            optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')
+            if embedding.checksum() == optimizer_saved_dict.get('hash', None):
+                optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+    
+        if optimizer_state_dict is not None:
+            optimizer.load_state_dict(optimizer_state_dict)
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
+
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
@@ -295,12 +340,14 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     loss_step = 0
     _loss_step = 0 #internal
 
-
     last_saved_file = "<none>"
     last_saved_image = "<none>"
     forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
+    is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}
+    img_c = None
+
     pbar = tqdm.tqdm(total=steps - initial_step)
     try:
         for i in range((steps-initial_step) * gradient_step):
@@ -318,14 +365,22 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 if shared.state.interrupted:
                     break
 
+                if clip_grad:
+                    clip_grad_sched.step(embedding.step)
+            
                 with devices.autocast():
-                    # c = stack_conds(batch.cond).to(devices.device)
-                    # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)
-                    # print(mask)
-                    # c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory)
                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                     c = shared.sd_model.cond_stage_model(batch.cond_text)
-                    loss = shared.sd_model(x, c)[0] / gradient_step
+
+                    if is_training_inpainting_model:
+                        if img_c is None:
+                            img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)
+
+                        cond = {"c_concat": [img_c], "c_crossattn": [c]}
+                    else:
+                        cond = c
+
+                    loss = shared.sd_model(x, cond)[0] / gradient_step
                     del x
 
                     _loss_step += loss.item()
@@ -334,6 +389,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
+                
+                if clip_grad:
+                    clip_grad(embedding.vec, clip_grad_sched.learn_rate)
+
                 scaler.step(optimizer)
                 scaler.update()
                 embedding.step += 1
@@ -352,9 +411,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     # Before saving, change name to match current checkpoint.
                     embedding_name_every = f'{embedding_name}-{steps_done}'
                     last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
-                    #if shared.opts.save_optimizer_state:
-                        #embedding.optimizer_state_dict = optimizer.state_dict()
-                    save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
+                    save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
                     embedding_yet_to_be_embedded = True
 
                 write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
@@ -444,7 +501,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
         filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
-        save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+        save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     except Exception:
         print(traceback.format_exc(), file=sys.stderr)
         pass
@@ -456,7 +513,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return embedding, filename
 
-def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
     old_embedding_name = embedding.name
     old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
     old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
@@ -467,6 +524,7 @@ def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cache
         if remove_cached_checksum:
             embedding.cached_checksum = None
         embedding.name = embedding_name
+        embedding.optimizer_state_dict = optimizer.state_dict()
         embedding.save(filename)
     except:
         embedding.sd_checkpoint = old_sd_checkpoint

modules/txt2img.py

@@ -8,7 +8,7 @@ import modules.processing as processing
 from modules.ui import plaintext_to_html
 
 
-def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
+def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, hr_second_pass_steps: int, hr_resize_x: int, hr_resize_y: int, *args):
     p = StableDiffusionProcessingTxt2Img(
         sd_model=shared.sd_model,
         outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
@@ -33,8 +33,11 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
         tiling=tiling,
         enable_hr=enable_hr,
         denoising_strength=denoising_strength if enable_hr else None,
-        firstphase_width=firstphase_width if enable_hr else None,
-        firstphase_height=firstphase_height if enable_hr else None,
+        hr_scale=hr_scale,
+        hr_upscaler=hr_upscaler,
+        hr_second_pass_steps=hr_second_pass_steps,
+        hr_resize_x=hr_resize_x,
+        hr_resize_y=hr_resize_y,
     )
 
     p.scripts = modules.scripts.scripts_txt2img
@@ -59,4 +62,4 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
     if opts.do_not_show_images:
         processed.images = []
 
-    return processed.images, generation_info_js, plaintext_to_html(processed.info)
+    return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments)

modules/ui_components.py

@@ -0,0 +1,25 @@
+import gradio as gr
+
+
+class ToolButton(gr.Button, gr.components.FormComponent):
+    """Small button with single emoji as text, fits inside gradio forms"""
+
+    def __init__(self, **kwargs):
+        super().__init__(variant="tool", **kwargs)
+
+    def get_block_name(self):
+        return "button"
+
+
+class FormRow(gr.Row, gr.components.FormComponent):
+    """Same as gr.Row but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "row"
+
+
+class FormGroup(gr.Group, gr.components.FormComponent):
+    """Same as gr.Row but fits inside gradio forms"""
+
+    def get_block_name(self):
+        return "group"

modules/ui_tempdir.py

@@ -1,6 +1,7 @@
 import os
 import tempfile
 from collections import namedtuple
+from pathlib import Path
 
 import gradio as gr
 
@@ -12,10 +13,29 @@ from modules import shared
 Savedfile = namedtuple("Savedfile", ["name"])
 
 
+def register_tmp_file(gradio, filename):
+    if hasattr(gradio, 'temp_file_sets'):  # gradio 3.15
+        gradio.temp_file_sets[0] = gradio.temp_file_sets[0] | {os.path.abspath(filename)}
+
+    if hasattr(gradio, 'temp_dirs'):  # gradio 3.9
+        gradio.temp_dirs = gradio.temp_dirs | {os.path.abspath(os.path.dirname(filename))}
+
+
+def check_tmp_file(gradio, filename):
+    if hasattr(gradio, 'temp_file_sets'):
+        return any([filename in fileset for fileset in gradio.temp_file_sets])
+
+    if hasattr(gradio, 'temp_dirs'):
+        return any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in gradio.temp_dirs)
+
+    return False
+
+
 def save_pil_to_file(pil_image, dir=None):
     already_saved_as = getattr(pil_image, 'already_saved_as', None)
     if already_saved_as and os.path.isfile(already_saved_as):
-        shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
+        register_tmp_file(shared.demo, already_saved_as)
+
         file_obj = Savedfile(already_saved_as)
         return file_obj
 
@@ -44,7 +64,7 @@ def on_tmpdir_changed():
 
     os.makedirs(shared.opts.temp_dir, exist_ok=True)
 
-    shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(shared.opts.temp_dir)}
+    register_tmp_file(shared.demo, os.path.join(shared.opts.temp_dir, "x"))
 
 
 def cleanup_tmpdr():

modules/upscaler.py

@@ -53,10 +53,10 @@ class Upscaler:
     def do_upscale(self, img: PIL.Image, selected_model: str):
         return img
 
-    def upscale(self, img: PIL.Image, scale: int, selected_model: str = None):
+    def upscale(self, img: PIL.Image, scale, selected_model: str = None):
         self.scale = scale
-        dest_w = img.width * scale
-        dest_h = img.height * scale
+        dest_w = int(img.width * scale)
+        dest_h = int(img.height * scale)
 
         for i in range(3):
             shape = (img.width, img.height)

modules/xlmr.py

@@ -0,0 +1,137 @@
+from transformers import BertPreTrainedModel,BertModel,BertConfig
+import torch.nn as nn
+import torch
+from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
+from transformers import XLMRobertaModel,XLMRobertaTokenizer
+from typing import Optional
+
+class BertSeriesConfig(BertConfig):
+    def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
+
+        super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+class RobertaSeriesConfig(XLMRobertaConfig):
+    def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
+        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+        self.project_dim = project_dim
+        self.pooler_fn = pooler_fn
+        self.learn_encoder = learn_encoder
+
+
+class BertSeriesModelWithTransformation(BertPreTrainedModel):
+
+    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+    config_class = BertSeriesConfig
+
+    def __init__(self, config=None, **kargs):
+        # modify initialization for autoloading 
+        if config is None:
+            config = XLMRobertaConfig()
+            config.attention_probs_dropout_prob= 0.1
+            config.bos_token_id=0
+            config.eos_token_id=2
+            config.hidden_act='gelu'
+            config.hidden_dropout_prob=0.1
+            config.hidden_size=1024
+            config.initializer_range=0.02
+            config.intermediate_size=4096
+            config.layer_norm_eps=1e-05
+            config.max_position_embeddings=514
+
+            config.num_attention_heads=16
+            config.num_hidden_layers=24
+            config.output_past=True
+            config.pad_token_id=1
+            config.position_embedding_type= "absolute"
+
+            config.type_vocab_size= 1
+            config.use_cache=True
+            config.vocab_size= 250002
+            config.project_dim = 768
+            config.learn_encoder = False
+        super().__init__(config)
+        self.roberta = XLMRobertaModel(config)
+        self.transformation = nn.Linear(config.hidden_size,config.project_dim)
+        self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+        self.pooler = lambda x: x[:,0]
+        self.post_init()
+
+    def encode(self,c):
+        device = next(self.parameters()).device
+        text = self.tokenizer(c,
+                        truncation=True,
+                        max_length=77,
+                        return_length=False,
+                        return_overflowing_tokens=False,
+                        padding="max_length",
+                        return_tensors="pt")
+        text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
+        text["attention_mask"] = torch.tensor(
+            text['attention_mask']).to(device)
+        features = self(**text)
+        return features['projection_state'] 
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+    ) :
+        r"""
+        """
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+        outputs = self.roberta(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=True,
+            return_dict=return_dict,
+        )
+
+        # last module outputs
+        sequence_output = outputs[0]
+
+
+        # project every module
+        sequence_output_ln = self.pre_LN(sequence_output)
+
+        # pooler
+        pooler_output = self.pooler(sequence_output_ln)
+        pooler_output = self.transformation(pooler_output)
+        projection_state = self.transformation(outputs.last_hidden_state)
+
+        return {
+            'pooler_output':pooler_output,
+            'last_hidden_state':outputs.last_hidden_state,
+            'hidden_states':outputs.hidden_states,
+            'attentions':outputs.attentions,
+            'projection_state':projection_state,
+            'sequence_out': sequence_output
+        }
+
+
+class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
+    base_model_prefix = 'roberta'
+    config_class= RobertaSeriesConfig

requirements.txt

@@ -5,11 +5,11 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.9
+gradio==3.15.0
 invisible-watermark
 numpy
 omegaconf
-opencv-python
+opencv-contrib-python
 requests
 piexif
 Pillow

requirements_versions.txt

@@ -3,9 +3,9 @@ transformers==4.19.2
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.9
+gradio==3.15.0
 numpy==1.23.3
-Pillow==9.2.0
+Pillow==9.4.0
 realesrgan==0.3.0
 torch
 omegaconf==2.2.3
@@ -26,5 +26,5 @@ lark==1.1.2
 inflection==0.5.1
 GitPython==3.1.27
 torchsde==0.2.5
-safetensors==0.2.5
+safetensors==0.2.7
 httpcore<=0.15

script.js

@@ -4,7 +4,7 @@ function gradioApp() {
 }
 
 function get_uiCurrentTab() {
-    return gradioApp().querySelector('.tabs button:not(.border-transparent)')
+    return gradioApp().querySelector('#tabs button:not(.border-transparent)')
 }
 
 function get_uiCurrentTabContent() {

scripts/sd_upscale.py

@@ -19,7 +19,7 @@ class Script(scripts.Script):
     def ui(self, is_img2img):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
         overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64)
-        scale_factor = gr.Slider(minimum=1, maximum=4, step=1, label='Scale Factor', value=2)
+        scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0)
         upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
 
         return [info, overlap, upscaler_index, scale_factor]

scripts/xy_grid.py

@@ -10,7 +10,7 @@ import numpy as np
 import modules.scripts as scripts
 import gradio as gr
 
-from modules import images, paths, sd_samplers
+from modules import images, paths, sd_samplers, processing
 from modules.hypernetworks import hypernetwork
 from modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img
 from modules.shared import opts, cmd_opts, state
@@ -202,7 +202,7 @@ axis_options = [
     AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
     AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
     AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
-    AxisOption("Upscale latent space for hires.", str, apply_upscale_latent_space, format_value_add_label, None),
+    AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), format_value_add_label, None),
     AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
     AxisOption("VAE", str, apply_vae, format_value_add_label, None),
     AxisOption("Styles", str, apply_styles, format_value_add_label, None),
@@ -267,7 +267,6 @@ class SharedSettingsStackHelper(object):
         self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
         self.hypernetwork = opts.sd_hypernetwork
         self.model = shared.sd_model
-        self.use_scale_latent_for_hires_fix = opts.use_scale_latent_for_hires_fix
         self.vae = opts.sd_vae
   
     def __exit__(self, exc_type, exc_value, tb):
@@ -278,7 +277,6 @@ class SharedSettingsStackHelper(object):
         hypernetwork.apply_strength()
 
         opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
-        opts.data["use_scale_latent_for_hires_fix"] = self.use_scale_latent_for_hires_fix
 
 
 re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@@ -287,6 +285,7 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
 re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
 re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
 
+
 class Script(scripts.Script):
     def title(self):
         return "X/Y plot"
@@ -383,7 +382,7 @@ class Script(scripts.Script):
         ys = process_axis(y_opt, y_values)
 
         def fix_axis_seeds(axis_opt, axis_list):
-            if axis_opt.label in ['Seed','Var. seed']:
+            if axis_opt.label in ['Seed', 'Var. seed']:
                 return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]
             else:
                 return axis_list
@@ -405,12 +404,33 @@ class Script(scripts.Script):
         print(f"X/Y plot will create {len(xs) * len(ys) * p.n_iter} images on a {len(xs)}x{len(ys)} grid. (Total steps to process: {total_steps * p.n_iter})")
         shared.total_tqdm.updateTotal(total_steps * p.n_iter)
 
+        grid_infotext = [None]
+
         def cell(x, y):
             pc = copy(p)
             x_opt.apply(pc, x, xs)
             y_opt.apply(pc, y, ys)
 
-            return process_images(pc)
+            res = process_images(pc)
+
+            if grid_infotext[0] is None:
+                pc.extra_generation_params = copy(pc.extra_generation_params)
+
+                if x_opt.label != 'Nothing':
+                    pc.extra_generation_params["X Type"] = x_opt.label
+                    pc.extra_generation_params["X Values"] = x_values
+                    if x_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
+                        pc.extra_generation_params["Fixed X Values"] = ", ".join([str(x) for x in xs])
+
+                if y_opt.label != 'Nothing':
+                    pc.extra_generation_params["Y Type"] = y_opt.label
+                    pc.extra_generation_params["Y Values"] = y_values
+                    if y_opt.label in ["Seed", "Var. seed"] and not no_fixed_seeds:
+                        pc.extra_generation_params["Fixed Y Values"] = ", ".join([str(y) for y in ys])
+
+                grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)
+
+            return res
 
         with SharedSettingsStackHelper():
             processed = draw_xy_grid(
@@ -425,6 +445,6 @@ class Script(scripts.Script):
             )
 
         if opts.grid_save:
-            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
+            images.save_image(processed.images[0], p.outpath_grids, "xy_grid", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)
 
         return processed

style.css

@@ -73,8 +73,9 @@
     margin-right: auto;
 }
 
-#random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{
-    min-width: auto;
+[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{
+    min-width: 2.3em;
+    height: 2.5em;
     flex-grow: 0;
     padding-left: 0.25em;
     padding-right: 0.25em;
@@ -84,27 +85,28 @@
     display: none;
 }
 
-#seed_row, #subseed_row{
+[id$=_seed_row], [id$=_subseed_row]{
     gap: 0.5rem;
+    padding: 0.6em;
 }
 
-#subseed_show_box{
+[id$=_subseed_show_box]{
     min-width: auto;
     flex-grow: 0;
 }
 
-#subseed_show_box > div{
+[id$=_subseed_show_box] > div{
     border: 0;
     height: 100%;
 }
 
-#subseed_show{
+[id$=_subseed_show]{
     min-width: auto;
     flex-grow: 0;
     padding: 0;
 }
 
-#subseed_show label{
+[id$=_subseed_show] label{
     height: 100%;
 }
 
@@ -206,24 +208,24 @@ button{
 
 fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block span{
     position: absolute;
-    top: -0.6em;
+    top: -0.7em;
     line-height: 1.2em;
-    padding: 0 0.5em;
-    margin: 0;
+    padding: 0;
+    margin: 0 0.5em;
 
     background-color: white;
-    border-top: 1px solid #eee;
-    border-left: 1px solid #eee;
-    border-right: 1px solid #eee;
+    box-shadow:  6px 0 6px 0px white, -6px 0 6px 0px white;
 
     z-index: 300;
 }
 
 .dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
     background-color: rgb(31, 41, 55);
-    border-top: 1px solid rgb(55 65 81);
-    border-left: 1px solid rgb(55 65 81);
-    border-right: 1px solid rgb(55 65 81);
+    box-shadow: 6px 0 6px 0px rgb(31, 41, 55), -6px 0 6px 0px rgb(31, 41, 55);
+}
+
+#txt2img_column_batch, #img2img_column_batch{
+    min-width: min(13.5em, 100%) !important;
 }
 
 #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{
@@ -232,22 +234,40 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
     margin-right: 8em;
 }
 
-.gr-panel div.flex-col div.justify-between label span{
-    margin: 0;
-}
-
 #settings .gr-panel div.flex-col div.justify-between div{
     position: relative;
     z-index: 200;
 }
 
-input[type="range"]{
-    margin: 0.5em 0 -0.3em 0;
+#settings{
+    display: block;
 }
 
-#txt2img_sampling label{
-    padding-left: 0.6em;
-    padding-right: 0.6em;
+#settings > div{
+    border: none;
+    margin-left: 10em;
+}
+
+#settings > div.flex-wrap{
+    float: left;
+    display: block;
+    margin-left: 0;
+    width: 10em;
+}
+
+#settings > div.flex-wrap button{
+    display: block;
+    border: none;
+    text-align: left;
+}
+
+#settings_result{
+    height: 1.4em;
+    margin: 0 1.2em;
+}
+
+input[type="range"]{
+    margin: 0.5em 0 -0.3em 0;
 }
 
 #mask_bug_info {
@@ -501,13 +521,6 @@ input[type="range"]{
     padding: 0;
 }
 
-#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{
-    max-width: 2.5em;
-    min-width: 2.5em;
-    height: 2.4em;
-}
-
-
 canvas[key="mask"] {
     z-index: 12 !important;
     filter: invert();
@@ -521,7 +534,7 @@ canvas[key="mask"] {
     position: absolute;
     right: 0.5em;
     top: -0.6em;
-    z-index: 200;
+    z-index: 400;
     width: 8em;
 }
 #quicksettings .gr-box > div > div > input.gr-text-input {
@@ -568,6 +581,53 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
     font-size: 95%;
 }
 
+#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{
+    min-width: auto;
+    padding-left: 0.5em;
+    padding-right: 0.5em;
+}
+
+.gr-form{
+    background-color: white;
+}
+
+.dark .gr-form{
+    background-color: rgb(31 41 55 / var(--tw-bg-opacity));
+}
+
+.gr-button-tool{
+    max-width: 2.5em;
+    min-width: 2.5em !important;
+    height: 2.4em;
+    margin: 0.55em 0;
+}
+
+#quicksettings .gr-button-tool{
+    margin: 0;
+}
+
+
+#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form {
+    padding-top: 0.9em;
+}
+
+#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form, #train_tabs div.gr-form .gr-form{
+    border: none;
+    padding-bottom: 0.5em;
+}
+
+footer {
+    display: none !important;
+}
+
+#footer{
+    text-align: center;
+}
+
+#footer div{
+    display: inline-block;
+}
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running

v2-inference-v.yaml

@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"

webui.py

@@ -1,4 +1,5 @@
 import os
+import sys
 import threading
 import time
 import importlib
@@ -8,7 +9,7 @@ from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.middleware.gzip import GZipMiddleware
 
-from modules import import_hook
+from modules import import_hook, errors
 from modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call
 from modules.paths import script_path
 
@@ -55,12 +56,20 @@ def initialize():
     gfpgan.setup_model(cmd_opts.gfpgan_models_path)
     shared.face_restorers.append(modules.face_restoration.FaceRestoration())
 
+    modelloader.list_builtin_upscalers()
     modules.scripts.load_scripts()
-
     modelloader.load_upscalers()
 
     modules.sd_vae.refresh_vae_list()
-    modules.sd_models.load_model()
+
+    try:
+        modules.sd_models.load_model()
+    except Exception as e:
+        errors.display(e, "loading stable diffusion model")
+        print("", file=sys.stderr)
+        print("Stable diffusion model failed to load, exiting", file=sys.stderr)
+        exit(1)
+
     shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
     shared.opts.onchange("sd_vae", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
     shared.opts.onchange("sd_vae_as_default", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)
@@ -91,11 +100,11 @@ def initialize():
 
 def setup_cors(app):
     if cmd_opts.cors_allow_origins and cmd_opts.cors_allow_origins_regex:
-        app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'])
+        app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
     elif cmd_opts.cors_allow_origins:
-        app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'])
+        app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
     elif cmd_opts.cors_allow_origins_regex:
-        app.add_middleware(CORSMiddleware, allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'])
+        app.add_middleware(CORSMiddleware, allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])
 
 
 def create_api(app):
@@ -169,23 +178,22 @@ def webui():
         modules.script_callbacks.app_started_callback(shared.demo, app)
 
         wait_on_server(shared.demo)
+        print('Restarting UI...')
 
         sd_samplers.set_samplers()
 
-        print('Reloading extensions')
         extensions.list_extensions()
 
         localization.list_localizations(cmd_opts.localizations_dir)
 
-        print('Reloading custom scripts')
+        modelloader.forbid_loaded_nonbuiltin_upscalers()
         modules.scripts.reload_scripts()
         modelloader.load_upscalers()
 
-        print('Reloading modules: modules.ui')
-        importlib.reload(modules.ui)
-        print('Refreshing Model List')
+        for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]:
+            importlib.reload(module)
+
         modules.sd_models.list_models()
-        print('Restarting Gradio')
 
 
 if __name__ == "__main__":