From c94fbae291f22f7c44303c2c7c9f6341c5be5825 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Sat, 11 Mar 2023 07:56:26 -0500 Subject: [PATCH] pre merge cleanup --- automatic.sh | 4 +- home/vlado/branches/automatic/README.md | 161 +++++ .../Lora/ui_extra_networks_lora.py | 37 ++ .../automatic/javascript/progressbar.js | 243 ++++++++ .../branches/automatic/modules/api/api.py | 551 ++++++++++++++++++ .../branches/automatic/modules/api/models.py | 269 +++++++++ .../branches/automatic/modules/sd_hijack.py | 264 +++++++++ .../modules/ui_extra_networks_checkpoints.py | 39 ++ .../modules/ui_extra_networks_hypernets.py | 36 ++ .../ui_extra_networks_textual_inversion.py | 34 ++ .../test/basic_features/txt2img_test.py | 80 +++ q.sh | 16 + 12 files changed, 1732 insertions(+), 2 deletions(-) create mode 100644 home/vlado/branches/automatic/README.md create mode 100644 home/vlado/branches/automatic/extensions-builtin/Lora/ui_extra_networks_lora.py create mode 100644 home/vlado/branches/automatic/javascript/progressbar.js create mode 100644 home/vlado/branches/automatic/modules/api/api.py create mode 100644 home/vlado/branches/automatic/modules/api/models.py create mode 100644 home/vlado/branches/automatic/modules/sd_hijack.py create mode 100644 home/vlado/branches/automatic/modules/ui_extra_networks_checkpoints.py create mode 100644 home/vlado/branches/automatic/modules/ui_extra_networks_hypernets.py create mode 100644 home/vlado/branches/automatic/modules/ui_extra_networks_textual_inversion.py create mode 100644 home/vlado/branches/automatic/test/basic_features/txt2img_test.py create mode 100644 q.sh diff --git a/automatic.sh b/automatic.sh index 2e82949ad..2636081b1 100755 --- a/automatic.sh +++ b/automatic.sh @@ -71,7 +71,7 @@ echo "- nVIDIA: $SMI" git-version () { pushd $1 >/dev/null - BRANCH=$(git branch | tail -1 | awk '{print $NF}') + BRANCH=$(git branch | grep -E 'main|master' | tail -1 | awk '{print $NF}') VER=$(git log -1 --pretty=format:"%h %ad") URL=$(git remote get-url origin) popd >/dev/null @@ -80,7 +80,7 @@ git-version () { git-update () { pushd $1 >/dev/null - BRANCH=$(git branch | tail -1 | awk '{print $NF}') + BRANCH=$(git branch | grep -E 'main|master' | tail -1 | awk '{print $NF}') git checkout --quiet $BRANCH git pull --quiet --rebase --autostash popd >/dev/null diff --git a/home/vlado/branches/automatic/README.md b/home/vlado/branches/automatic/README.md new file mode 100644 index 000000000..2ceb4d2db --- /dev/null +++ b/home/vlado/branches/automatic/README.md @@ -0,0 +1,161 @@ +# Stable Diffusion web UI +A browser interface based on Gradio library for Stable Diffusion. + +![](screenshot.png) + +## Features +[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features): +- Original txt2img and img2img modes +- One click install and run script (but you still must install python and git) +- Outpainting +- Inpainting +- Color Sketch +- Prompt Matrix +- Stable Diffusion Upscale +- Attention, specify parts of text that the model should pay more attention to + - a man in a ((tuxedo)) - will pay more attention to tuxedo + - a man in a (tuxedo:1.21) - alternative syntax + - select text and press ctrl+up or ctrl+down to automatically adjust attention to selected text (code contributed by anonymous user) +- Loopback, run img2img processing multiple times +- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters +- Textual Inversion + - have as many embeddings as you want and use any names you like for them + - use multiple embeddings with different numbers of vectors per token + - works with half precision floating point numbers + - train embeddings on 8GB (also reports of 6GB working) +- Extras tab with: + - GFPGAN, neural network that fixes faces + - CodeFormer, face restoration tool as an alternative to GFPGAN + - RealESRGAN, neural network upscaler + - ESRGAN, neural network upscaler with a lot of third party models + - SwinIR and Swin2SR([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers + - LDSR, Latent diffusion super resolution upscaling +- Resizing aspect ratio options +- Sampling method selection + - Adjust sampler eta values (noise multiplier) + - More advanced noise setting options +- Interrupt processing at any time +- 4GB video card support (also reports of 2GB working) +- Correct seeds for batches +- Live prompt token length validation +- Generation parameters + - parameters you used to generate images are saved with that image + - in PNG chunks for PNG, in EXIF for JPEG + - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI + - can be disabled in settings + - drag and drop an image/text-parameters to promptbox +- Read Generation Parameters Button, loads parameters in promptbox to UI +- Settings page +- Running arbitrary python code from UI (must run with --allow-code to enable) +- Mouseover hints for most UI elements +- Possible to change defaults/mix/max/step values for UI elements via text config +- Tiling support, a checkbox to create images that can be tiled like textures +- Progress bar and live image generation preview + - Can use a separate neural network to produce previews with almost none VRAM or compute requirement +- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image +- Styles, a way to save part of prompt and easily apply them via dropdown later +- Variations, a way to generate same image but with tiny differences +- Seed resizing, a way to generate same image but at slightly different resolution +- CLIP interrogator, a button that tries to guess prompt from an image +- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway +- Batch Processing, process a group of files using img2img +- Img2img Alternative, reverse Euler method of cross attention control +- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions +- Reloading checkpoints on the fly +- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one +- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community +- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once + - separate prompts using uppercase `AND` + - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2` +- No token limit for prompts (original stable diffusion lets you use up to 75 tokens) +- DeepDanbooru integration, creates danbooru style tags for anime prompts +- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) +- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI +- Generate forever option +- Training tab + - hypernetworks and embeddings options + - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) +- Clip skip +- Hypernetworks +- Loras (same as Hypernetworks but more pretty) +- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt. +- Can select to load a different VAE from settings screen +- Estimated completion time in progress bar +- API +- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. +- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) +- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions +- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions +- Now without any bad letters! +- Load checkpoints in safetensors format +- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64 +- Now with a license! +- Reorder elements in the UI from settings screen +- + +## Installation and Running +Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. + +Alternatively, use online services (like Google Colab): + +- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) + +### Automatic Installation on Windows +1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH" +2. Install [git](https://git-scm.com/download/win). +3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`. +4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user. + +### Automatic Installation on Linux +1. Install the dependencies: +```bash +# Debian-based: +sudo apt install wget git python3 python3-venv +# Red Hat-based: +sudo dnf install wget git python3 +# Arch-based: +sudo pacman -S wget git python3 +``` +2. To install in `/home/$(whoami)/stable-diffusion-webui/`, run: +```bash +bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh) +``` +3. Run `webui.sh`. +### Installation on Apple Silicon + +Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon). + +## Contributing +Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) + +## Documentation +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 +- CodeFormer - https://github.com/sczhou/CodeFormer +- ESRGAN - https://github.com/xinntao/ESRGAN +- SwinIR - https://github.com/JingyunLiang/SwinIR +- Swin2SR - https://github.com/mv-lab/swin2sr +- LDSR - https://github.com/Hafiidz/latent-diffusion +- MiDaS - https://github.com/isl-org/MiDaS +- Ideas for optimizations - https://github.com/basujindal/stable-diffusion +- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing. +- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion) +- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention) +- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas). +- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd +- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot +- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator +- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch +- xformers - https://github.com/facebookresearch/xformers +- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru +- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6) +- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix +- Security advice - RyotaK +- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user. +- (You) diff --git a/home/vlado/branches/automatic/extensions-builtin/Lora/ui_extra_networks_lora.py b/home/vlado/branches/automatic/extensions-builtin/Lora/ui_extra_networks_lora.py new file mode 100644 index 000000000..22cabcb0f --- /dev/null +++ b/home/vlado/branches/automatic/extensions-builtin/Lora/ui_extra_networks_lora.py @@ -0,0 +1,37 @@ +import json +import os +import lora + +from modules import shared, ui_extra_networks + + +class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Lora') + + def refresh(self): + lora.list_available_loras() + + def list_items(self): + for name, lora_on_disk in lora.available_loras.items(): + path, ext = os.path.splitext(lora_on_disk.filename) + previews = [path + ".png", path + ".preview.png"] + + preview = None + for file in previews: + if os.path.isfile(file): + preview = self.link_preview(file) + break + + yield { + "name": name, + "filename": path, + "preview": preview, + "search_term": self.search_terms_from_path(lora_on_disk.filename), + "prompt": json.dumps(f""), + "local_preview": path + ".png", + } + + def allowed_directories_for_previews(self): + return [shared.cmd_opts.lora_dir] + diff --git a/home/vlado/branches/automatic/javascript/progressbar.js b/home/vlado/branches/automatic/javascript/progressbar.js new file mode 100644 index 000000000..ff6d757ba --- /dev/null +++ b/home/vlado/branches/automatic/javascript/progressbar.js @@ -0,0 +1,243 @@ +// code related to showing and updating progressbar shown as the image is being made + + +galleries = {} +storedGallerySelections = {} +galleryObservers = {} + +function rememberGallerySelection(id_gallery){ + storedGallerySelections[id_gallery] = getGallerySelectedIndex(id_gallery) +} + +function getGallerySelectedIndex(id_gallery){ + let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') + let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') + + let currentlySelectedIndex = -1 + galleryButtons.forEach(function(v, i){ if(v==galleryBtnSelected) { currentlySelectedIndex = i } }) + + return currentlySelectedIndex +} + +// this is a workaround for https://github.com/gradio-app/gradio/issues/2984 +function check_gallery(id_gallery){ + let gallery = gradioApp().getElementById(id_gallery) + // if gallery has no change, no need to setting up observer again. + if (gallery && galleries[id_gallery] !== gallery){ + galleries[id_gallery] = gallery; + if(galleryObservers[id_gallery]){ + galleryObservers[id_gallery].disconnect(); + } + + storedGallerySelections[id_gallery] = -1 + + galleryObservers[id_gallery] = new MutationObserver(function (){ + let galleryButtons = gradioApp().querySelectorAll('#'+id_gallery+' .gallery-item') + let galleryBtnSelected = gradioApp().querySelector('#'+id_gallery+' .gallery-item.\\!ring-2') + let currentlySelectedIndex = getGallerySelectedIndex(id_gallery) + prevSelectedIndex = storedGallerySelections[id_gallery] + storedGallerySelections[id_gallery] = -1 + + if (prevSelectedIndex !== -1 && galleryButtons.length>prevSelectedIndex && !galleryBtnSelected) { + // automatically re-open previously selected index (if exists) + activeElement = gradioApp().activeElement; + let scrollX = window.scrollX; + let scrollY = window.scrollY; + + galleryButtons[prevSelectedIndex].click(); + showGalleryImage(); + + // When the gallery button is clicked, it gains focus and scrolls itself into view + // We need to scroll back to the previous position + setTimeout(function (){ + window.scrollTo(scrollX, scrollY); + }, 50); + + if(activeElement){ + // i fought this for about an hour; i don't know why the focus is lost or why this helps recover it + // if someone has a better solution please by all means + setTimeout(function (){ + activeElement.focus({ + preventScroll: true // Refocus the element that was focused before the gallery was opened without scrolling to it + }) + }, 1); + } + } + }) + galleryObservers[id_gallery].observe( gallery, { childList:true, subtree:false }) + } +} + +onUiUpdate(function(){ + check_gallery('txt2img_gallery') + check_gallery('img2img_gallery') +}) + +function request(url, data, handler, errorHandler){ + var xhr = new XMLHttpRequest(); + var url = url; + xhr.open("POST", url, true); + xhr.setRequestHeader("Content-Type", "application/json"); + xhr.onreadystatechange = function () { + if (xhr.readyState === 4) { + if (xhr.status === 200) { + try { + var js = JSON.parse(xhr.responseText); + handler(js) + } catch (error) { + console.error(error); + errorHandler() + } + } else{ + errorHandler() + } + } + }; + var js = JSON.stringify(data); + xhr.send(js); +} + +function pad2(x){ + return x<10 ? '0'+x : x +} + +function formatTime(secs){ + if(secs > 3600){ + return pad2(Math.floor(secs/60/60)) + ":" + pad2(Math.floor(secs/60)%60) + ":" + pad2(Math.floor(secs)%60) + } else if(secs > 60){ + return pad2(Math.floor(secs/60)) + ":" + pad2(Math.floor(secs)%60) + } else{ + return Math.floor(secs) + "s" + } +} + +function setTitle(progress){ + var title = 'Stable Diffusion' + + if(opts.show_progress_in_title && progress){ + title = '[' + progress.trim() + '] ' + title; + } + + if(document.title != title){ + document.title = title; + } +} + + +function randomId(){ + return "task(" + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7) + Math.random().toString(36).slice(2, 7)+")" +} + +// starts sending progress requests to "/internal/progress" uri, creating progressbar above progressbarContainer element and +// preview inside gallery element. Cleans up all created stuff when the task is over and calls atEnd. +// calls onProgress every time there is a progress update +function requestProgress(id_task, progressbarContainer, gallery, atEnd, onProgress){ + var dateStart = new Date() + var wasEverActive = false + var parentProgressbar = progressbarContainer.parentNode + var parentGallery = gallery ? gallery.parentNode : null + + var divProgress = document.createElement('div') + divProgress.className='progressDiv' + divProgress.style.display = opts.show_progressbar ? "" : "none" + var divInner = document.createElement('div') + divInner.className='progress' + + divProgress.appendChild(divInner) + parentProgressbar.insertBefore(divProgress, progressbarContainer) + + if(parentGallery){ + var livePreview = document.createElement('div') + livePreview.className='livePreview' + parentGallery.insertBefore(livePreview, gallery) + } + + var removeProgressBar = function(){ + setTitle("") + parentProgressbar.removeChild(divProgress) + if(parentGallery) parentGallery.removeChild(livePreview) + atEnd() + } + + var fun = function(id_task, id_live_preview){ + request("./internal/progress", {"id_task": id_task, "id_live_preview": id_live_preview}, function(res){ + if(res.completed){ + removeProgressBar() + return + } + + var rect = progressbarContainer.getBoundingClientRect() + + if(rect.width){ + divProgress.style.width = rect.width + "px"; + } + + progressText = "" + + divInner.style.width = ((res.progress || 0) * 100.0) + '%' + divInner.style.background = res.progress ? "" : "transparent" + + if(res.progress > 0){ + progressText = ((res.progress || 0) * 100.0).toFixed(0) + '%' + } + + if(res.eta){ + progressText += " ETA: " + formatTime(res.eta) + } + + + setTitle(progressText) + + if(res.textinfo && res.textinfo.indexOf("\n") == -1){ + progressText = res.textinfo + " " + progressText + } + + divInner.textContent = progressText + + var elapsedFromStart = (new Date() - dateStart) / 1000 + + if(res.active) wasEverActive = true; + + if(! res.active && wasEverActive){ + removeProgressBar() + return + } + + if(elapsedFromStart > 5 && !res.queued && !res.active){ + removeProgressBar() + return + } + + + if(res.live_preview && gallery){ + var rect = gallery.getBoundingClientRect() + if(rect.width){ + livePreview.style.width = rect.width + "px" + livePreview.style.height = rect.height + "px" + } + + var img = new Image(); + img.onload = function() { + livePreview.appendChild(img) + if(livePreview.childElementCount > 2){ + livePreview.removeChild(livePreview.firstElementChild) + } + } + img.src = res.live_preview; + } + + + if(onProgress){ + onProgress(res) + } + + setTimeout(() => { + fun(id_task, res.id_live_preview); + }, opts.live_preview_refresh_period || 500) + }, function(){ + removeProgressBar() + }) + } + + fun(id_task, 0) +} diff --git a/home/vlado/branches/automatic/modules/api/api.py b/home/vlado/branches/automatic/modules/api/api.py new file mode 100644 index 000000000..5a9ac5f1a --- /dev/null +++ b/home/vlado/branches/automatic/modules/api/api.py @@ -0,0 +1,551 @@ +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, Request, Response +from fastapi.security import HTTPBasic, HTTPBasicCredentials +from secrets import compare_digest + +import modules.shared as shared +from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing +from modules.api.models import * +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images +from modules.textual_inversion.textual_inversion import create_embedding, train_embedding +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_config import find_checkpoint_config_near_filename +from modules.realesrgan_model import get_realesrgan_models +from modules import devices +from typing import List +import piexif +import piexif.helper + +def upscaler_to_index(name: str): + try: + return [x.name.lower() for x in shared.sd_upscalers].index(name.lower()) + except: + raise HTTPException(status_code=400, detail=f"Invalid upscaler, needs to be one of these: {' , '.join([x.name for x in sd_upscalers])}") + +def script_name_to_index(name, scripts): + try: + return [script.title().lower() for script in scripts].index(name.lower()) + except: + raise HTTPException(status_code=422, detail=f"Script '{name}' not found") + +def validate_sampler_name(name): + config = sd_samplers.all_samplers_map.get(name, None) + if config is None: + raise HTTPException(status_code=404, detail="Sampler not found") + + return name + +def setUpscalers(req: dict): + reqDict = vars(req) + reqDict['extras_upscaler_1'] = reqDict.pop('upscaler_1', None) + reqDict['extras_upscaler_2'] = reqDict.pop('upscaler_2', None) + return reqDict + +def decode_base64_to_image(encoding): + if encoding.startswith("data:image/"): + encoding = encoding.split(";")[1].split(",")[1] + try: + image = Image.open(BytesIO(base64.b64decode(encoding))) + return image + except Exception as err: + raise HTTPException(status_code=500, detail="Invalid encoded image") + +def encode_pil_to_base64(image): + with io.BytesIO() as output_bytes: + + if opts.samples_format.lower() == 'png': + use_metadata = False + metadata = PngImagePlugin.PngInfo() + for key, value in image.info.items(): + if isinstance(key, str) and isinstance(value, str): + metadata.add_text(key, value) + use_metadata = True + image.save(output_bytes, format="PNG", pnginfo=(metadata if use_metadata else None), quality=opts.jpeg_quality) + + elif opts.samples_format.lower() in ("jpg", "jpeg", "webp"): + parameters = image.info.get('parameters', None) + exif_bytes = piexif.dump({ + "Exif": { piexif.ExifIFD.UserComment: piexif.helper.UserComment.dump(parameters or "", encoding="unicode") } + }) + if opts.samples_format.lower() in ("jpg", "jpeg"): + image.save(output_bytes, format="JPEG", exif = exif_bytes, quality=opts.jpeg_quality) + else: + image.save(output_bytes, format="WEBP", exif = exif_bytes, quality=opts.jpeg_quality) + + else: + raise HTTPException(status_code=500, detail="Invalid image format") + + 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): + if shared.cmd_opts.api_auth: + self.credentials = dict() + for auth in shared.cmd_opts.api_auth.split(","): + user, password = auth.split(":") + self.credentials[user] = password + + 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) + self.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse) + self.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse) + self.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse) + self.add_api_route("/sdapi/v1/interrogate", self.interrogateapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"]) + self.add_api_route("/sdapi/v1/skip", self.skip, methods=["POST"]) + self.add_api_route("/sdapi/v1/options", self.get_config, methods=["GET"], response_model=OptionsModel) + self.add_api_route("/sdapi/v1/options", self.set_config, methods=["POST"]) + self.add_api_route("/sdapi/v1/cmd-flags", self.get_cmd_flags, methods=["GET"], response_model=FlagsModel) + self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[SamplerItem]) + self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[UpscalerItem]) + self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[SDModelItem]) + self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[HypernetworkItem]) + self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[FaceRestorerItem]) + self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[RealesrganItem]) + self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem]) + 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) + self.add_api_route("/sdapi/v1/preprocess", self.preprocess, methods=["POST"], response_model=PreprocessResponse) + self.add_api_route("/sdapi/v1/train/embedding", self.train_embedding, methods=["POST"], response_model=TrainResponse) + self.add_api_route("/sdapi/v1/train/hypernetwork", self.train_hypernetwork, methods=["POST"], response_model=TrainResponse) + self.add_api_route("/sdapi/v1/memory", self.get_memory, methods=["GET"], response_model=MemoryResponse) + + def add_api_route(self, path: str, endpoint, **kwargs): + if shared.cmd_opts.api_auth: + return self.app.add_api_route(path, endpoint, dependencies=[Depends(self.auth)], **kwargs) + return self.app.add_api_route(path, endpoint, **kwargs) + + def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())): + if credentials.username in self.credentials: + if compare_digest(credentials.password, self.credentials[credentials.username]): + return True + + raise HTTPException(status_code=401, detail="Incorrect username or password", headers={"WWW-Authenticate": "Basic"}) + + def get_script(self, script_name, script_runner): + if script_name is None: + return None, None + + if not script_runner.scripts: + script_runner.initialize_scripts(False) + ui.create_ui() + + script_idx = script_name_to_index(script_name, script_runner.selectable_scripts) + script = script_runner.selectable_scripts[script_idx] + return script, script_idx + + def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): + script, script_idx = self.get_script(txt2imgreq.script_name, scripts.scripts_txt2img) + + populate = txt2imgreq.copy(update={ # Override __init__ params + "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index), + "do_not_save_samples": True, + "do_not_save_grid": True + } + ) + if populate.sampler_name: + populate.sampler_index = None # prevent a warning later on + + args = vars(populate) + args.pop('script_name', None) + + with self.queue_lock: + p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **args) + + shared.state.begin() + if script is not None: + p.outpath_grids = opts.outdir_txt2img_grids + p.outpath_samples = opts.outdir_txt2img_samples + p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args + processed = scripts.scripts_txt2img.run(p, *p.script_args) + else: + processed = process_images(p) + shared.state.end() + + b64images = list(map(encode_pil_to_base64, processed.images)) + + return TextToImageResponse(images=b64images, parameters=vars(txt2imgreq), info=processed.js()) + + def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): + init_images = img2imgreq.init_images + if init_images is None: + raise HTTPException(status_code=404, detail="Init image not found") + + script, script_idx = self.get_script(img2imgreq.script_name, scripts.scripts_img2img) + + mask = img2imgreq.mask + if mask: + mask = decode_base64_to_image(mask) + + populate = img2imgreq.copy(update={ # Override __init__ params + "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index), + "do_not_save_samples": True, + "do_not_save_grid": True, + "mask": mask + } + ) + if populate.sampler_name: + populate.sampler_index = None # prevent a warning later on + + args = vars(populate) + args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine. + args.pop('script_name', None) + + with self.queue_lock: + p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) + p.init_images = [decode_base64_to_image(x) for x in init_images] + + shared.state.begin() + if script is not None: + p.outpath_grids = opts.outdir_img2img_grids + p.outpath_samples = opts.outdir_img2img_samples + p.script_args = [script_idx + 1] + [None] * (script.args_from - 1) + p.script_args + processed = scripts.scripts_img2img.run(p, *p.script_args) + else: + processed = process_images(p) + shared.state.end() + + b64images = list(map(encode_pil_to_base64, processed.images)) + + if not img2imgreq.include_init_images: + img2imgreq.init_images = None + img2imgreq.mask = None + + return ImageToImageResponse(images=b64images, parameters=vars(img2imgreq), info=processed.js()) + + def extras_single_image_api(self, req: ExtrasSingleImageRequest): + reqDict = setUpscalers(req) + + reqDict['image'] = decode_base64_to_image(reqDict['image']) + + with self.queue_lock: + result = postprocessing.run_extras(extras_mode=0, image_folder="", input_dir="", output_dir="", save_output=False, **reqDict) + + return ExtrasSingleImageResponse(image=encode_pil_to_base64(result[0][0]), html_info=result[1]) + + def extras_batch_images_api(self, req: ExtrasBatchImagesRequest): + reqDict = setUpscalers(req) + + def prepareFiles(file): + file = decode_base64_to_file(file.data, file_path=file.name) + file.orig_name = file.name + return file + + reqDict['image_folder'] = list(map(prepareFiles, reqDict['imageList'])) + reqDict.pop('imageList') + + with self.queue_lock: + result = postprocessing.run_extras(extras_mode=1, image="", input_dir="", output_dir="", save_output=False, **reqDict) + + return ExtrasBatchImagesResponse(images=list(map(encode_pil_to_base64, result[0])), html_info=result[1]) + + def pnginfoapi(self, req: PNGInfoRequest): + if(not req.image.strip()): + return PNGInfoResponse(info="") + + image = decode_base64_to_image(req.image.strip()) + if image is None: + return PNGInfoResponse(info="") + + geninfo, items = images.read_info_from_image(image) + if geninfo is None: + geninfo = "" + + items = {**{'parameters': geninfo}, **items} + + return PNGInfoResponse(info=geninfo, items=items) + + def progressapi(self, req: ProgressRequest = Depends()): + # copy from check_progress_call of ui.py + + if shared.state.job_count == 0: + return ProgressResponse(progress=0, eta_relative=0, state=shared.state.dict(), textinfo=shared.state.textinfo) + + # avoid dividing zero + progress = 0.01 + + if shared.state.job_count > 0: + progress += shared.state.job_no / shared.state.job_count + if shared.state.sampling_steps > 0: + progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps + + time_since_start = time.time() - shared.state.time_start + eta = (time_since_start/progress) + eta_relative = eta-time_since_start + + progress = min(progress, 1) + + shared.state.set_current_image() + + current_image = None + if shared.state.current_image and not req.skip_current_image: + current_image = encode_pil_to_base64(shared.state.current_image) + + return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image, textinfo=shared.state.textinfo) + + def interrogateapi(self, interrogatereq: InterrogateRequest): + image_b64 = interrogatereq.image + if image_b64 is None: + raise HTTPException(status_code=404, detail="Image not found") + + img = decode_base64_to_image(image_b64) + img = img.convert('RGB') + + # Override object param + with self.queue_lock: + if interrogatereq.model == "clip": + processed = shared.interrogator.interrogate(img) + elif interrogatereq.model == "deepdanbooru": + processed = deepbooru.model.tag(img) + else: + raise HTTPException(status_code=404, detail="Model not found") + + return InterrogateResponse(caption=processed) + + def interruptapi(self): + shared.state.interrupt() + + return {} + + def skip(self): + shared.state.skip() + + def get_config(self): + options = {} + for key in shared.opts.data.keys(): + metadata = shared.opts.data_labels.get(key) + if(metadata is not None): + options.update({key: shared.opts.data.get(key, shared.opts.data_labels.get(key).default)}) + else: + options.update({key: shared.opts.data.get(key, None)}) + + return options + + def set_config(self, req: Dict[str, Any]): + for k, v in req.items(): + shared.opts.set(k, v) + + shared.opts.save(shared.config_filename) + return + + def get_cmd_flags(self): + return vars(shared.cmd_opts) + + def get_samplers(self): + return [{"name": sampler[0], "aliases":sampler[2], "options":sampler[3]} for sampler in sd_samplers.all_samplers] + + def get_upscalers(self): + return [ + { + "name": upscaler.name, + "model_name": upscaler.scaler.model_name, + "model_path": upscaler.data_path, + "model_url": None, + "scale": upscaler.scale, + } + for upscaler in shared.sd_upscalers + ] + + def get_sd_models(self): + return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()] + + def get_hypernetworks(self): + return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks] + + def get_face_restorers(self): + return [{"name":x.name(), "cmd_dir": getattr(x, "cmd_dir", None)} for x in shared.face_restorers] + + def get_realesrgan_models(self): + return [{"name":x.name,"path":x.data_path, "scale":x.scale} for x in get_realesrgan_models(None)] + + def get_prompt_styles(self): + styleList = [] + for k in shared.prompt_styles.styles: + style = shared.prompt_styles.styles[k] + styleList.append({"name":style[0], "prompt": style[1], "negative_prompt": style[2]}) + + return styleList + + 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() + + def create_embedding(self, args: dict): + try: + shared.state.begin() + filename = create_embedding(**args) # create empty embedding + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() # reload embeddings so new one can be immediately used + shared.state.end() + return CreateResponse(info = "create embedding filename: {filename}".format(filename = filename)) + except AssertionError as e: + shared.state.end() + return TrainResponse(info = "create embedding error: {error}".format(error = e)) + + def create_hypernetwork(self, args: dict): + try: + shared.state.begin() + filename = create_hypernetwork(**args) # create empty embedding + shared.state.end() + return CreateResponse(info = "create hypernetwork filename: {filename}".format(filename = filename)) + except AssertionError as e: + shared.state.end() + return TrainResponse(info = "create hypernetwork error: {error}".format(error = e)) + + def preprocess(self, args: dict): + try: + shared.state.begin() + preprocess(**args) # quick operation unless blip/booru interrogation is enabled + shared.state.end() + return PreprocessResponse(info = 'preprocess complete') + except KeyError as e: + shared.state.end() + return PreprocessResponse(info = "preprocess error: invalid token: {error}".format(error = e)) + except AssertionError as e: + shared.state.end() + return PreprocessResponse(info = "preprocess error: {error}".format(error = e)) + except FileNotFoundError as e: + shared.state.end() + return PreprocessResponse(info = 'preprocess error: {error}'.format(error = e)) + + def train_embedding(self, args: dict): + try: + shared.state.begin() + apply_optimizations = shared.opts.training_xattention_optimizations + error = None + filename = '' + if not apply_optimizations: + sd_hijack.undo_optimizations() + try: + embedding, filename = train_embedding(**args) # can take a long time to complete + except Exception as e: + error = e + finally: + if not apply_optimizations: + sd_hijack.apply_optimizations() + shared.state.end() + return TrainResponse(info = "train embedding complete: filename: {filename} error: {error}".format(filename = filename, error = error)) + except AssertionError as msg: + shared.state.end() + return TrainResponse(info = "train embedding error: {msg}".format(msg = msg)) + + def train_hypernetwork(self, args: dict): + try: + shared.state.begin() + shared.loaded_hypernetworks = [] + apply_optimizations = shared.opts.training_xattention_optimizations + error = None + filename = '' + if not apply_optimizations: + sd_hijack.undo_optimizations() + try: + hypernetwork, filename = train_hypernetwork(**args) + except Exception as e: + error = e + finally: + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) + if not apply_optimizations: + sd_hijack.apply_optimizations() + shared.state.end() + return TrainResponse(info="train embedding complete: filename: {filename} error: {error}".format(filename=filename, error=error)) + except AssertionError as msg: + shared.state.end() + return TrainResponse(info="train embedding error: {error}".format(error=error)) + + def get_memory(self): + try: + import os, psutil + process = psutil.Process(os.getpid()) + res = process.memory_info() # only rss is cross-platform guaranteed so we dont rely on other values + ram_total = 100 * res.rss / process.memory_percent() # and total memory is calculated as actual value is not cross-platform safe + ram = { 'free': ram_total - res.rss, 'used': res.rss, 'total': ram_total } + except Exception as err: + ram = { 'error': f'{err}' } + try: + import torch + if torch.cuda.is_available(): + s = torch.cuda.mem_get_info() + system = { 'free': s[0], 'used': s[1] - s[0], 'total': s[1] } + s = dict(torch.cuda.memory_stats(shared.device)) + allocated = { 'current': s['allocated_bytes.all.current'], 'peak': s['allocated_bytes.all.peak'] } + reserved = { 'current': s['reserved_bytes.all.current'], 'peak': s['reserved_bytes.all.peak'] } + active = { 'current': s['active_bytes.all.current'], 'peak': s['active_bytes.all.peak'] } + inactive = { 'current': s['inactive_split_bytes.all.current'], 'peak': s['inactive_split_bytes.all.peak'] } + warnings = { 'retries': s['num_alloc_retries'], 'oom': s['num_ooms'] } + cuda = { + 'system': system, + 'active': active, + 'allocated': allocated, + 'reserved': reserved, + 'inactive': inactive, + 'events': warnings, + } + else: + cuda = { 'error': 'unavailable' } + except Exception as err: + cuda = { 'error': f'{err}' } + return MemoryResponse(ram = ram, cuda = cuda) + + def launch(self, server_name, port): + self.app.include_router(self.router) + uvicorn.run(self.app, host=server_name, port=port) diff --git a/home/vlado/branches/automatic/modules/api/models.py b/home/vlado/branches/automatic/modules/api/models.py new file mode 100644 index 000000000..cba43d3b1 --- /dev/null +++ b/home/vlado/branches/automatic/modules/api/models.py @@ -0,0 +1,269 @@ +import inspect +from pydantic import BaseModel, Field, create_model +from typing import Any, Optional +from typing_extensions import Literal +from inflection import underscore +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img +from modules.shared import sd_upscalers, opts, parser +from typing import Dict, List + +API_NOT_ALLOWED = [ + "self", + "kwargs", + "sd_model", + "outpath_samples", + "outpath_grids", + "sampler_index", + "do_not_save_samples", + "do_not_save_grid", + "extra_generation_params", + "overlay_images", + "do_not_reload_embeddings", + "seed_enable_extras", + "prompt_for_display", + "sampler_noise_scheduler_override", + "ddim_discretize" +] + +class ModelDef(BaseModel): + """Assistance Class for Pydantic Dynamic Model Generation""" + + field: str + field_alias: str + field_type: Any + field_value: Any + field_exclude: bool = False + + +class PydanticModelGenerator: + """ + Takes in created classes and stubs them out in a way FastAPI/Pydantic is happy about: + source_data is a snapshot of the default values produced by the class + params are the names of the actual keys required by __init__ + """ + + def __init__( + self, + model_name: str = None, + class_instance = None, + additional_fields = None, + ): + def field_type_generator(k, v): + # field_type = str if not overrides.get(k) else overrides[k]["type"] + # print(k, v.annotation, v.default) + field_type = v.annotation + + return Optional[field_type] + + def merge_class_params(class_): + all_classes = list(filter(lambda x: x is not object, inspect.getmro(class_))) + parameters = {} + for classes in all_classes: + parameters = {**parameters, **inspect.signature(classes.__init__).parameters} + return parameters + + + self._model_name = model_name + self._class_data = merge_class_params(class_instance) + + self._model_def = [ + ModelDef( + field=underscore(k), + field_alias=k, + field_type=field_type_generator(k, v), + field_value=v.default + ) + for (k,v) in self._class_data.items() if k not in API_NOT_ALLOWED + ] + + for fields in additional_fields: + self._model_def.append(ModelDef( + field=underscore(fields["key"]), + field_alias=fields["key"], + field_type=fields["type"], + field_value=fields["default"], + field_exclude=fields["exclude"] if "exclude" in fields else False)) + + def generate_model(self): + """ + Creates a pydantic BaseModel + from the json and overrides provided at initialization + """ + fields = { + d.field: (d.field_type, Field(default=d.field_value, alias=d.field_alias, exclude=d.field_exclude)) for d in self._model_def + } + DynamicModel = create_model(self._model_name, **fields) + DynamicModel.__config__.allow_population_by_field_name = True + DynamicModel.__config__.allow_mutation = True + return DynamicModel + +StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingTxt2Img", + StableDiffusionProcessingTxt2Img, + [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}] +).generate_model() + +StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingImg2Img", + StableDiffusionProcessingImg2Img, + [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}, {"key": "include_init_images", "type": bool, "default": False, "exclude" : True}, {"key": "script_name", "type": str, "default": None}, {"key": "script_args", "type": list, "default": []}] +).generate_model() + +class TextToImageResponse(BaseModel): + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ImageToImageResponse(BaseModel): + images: List[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: dict + info: str + +class ExtrasBaseRequest(BaseModel): + resize_mode: Literal[0, 1] = Field(default=0, title="Resize Mode", description="Sets the resize mode: 0 to upscale by upscaling_resize amount, 1 to upscale up to upscaling_resize_h x upscaling_resize_w.") + show_extras_results: bool = Field(default=True, title="Show results", description="Should the backend return the generated image?") + gfpgan_visibility: float = Field(default=0, title="GFPGAN Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of GFPGAN, values should be between 0 and 1.") + codeformer_visibility: float = Field(default=0, title="CodeFormer Visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of CodeFormer, values should be between 0 and 1.") + codeformer_weight: float = Field(default=0, title="CodeFormer Weight", ge=0, le=1, allow_inf_nan=False, description="Sets the weight of CodeFormer, values should be between 0 and 1.") + upscaling_resize: float = Field(default=2, title="Upscaling Factor", ge=1, le=8, description="By how much to upscale the image, only used when resize_mode=0.") + upscaling_resize_w: int = Field(default=512, title="Target Width", ge=1, description="Target width for the upscaler to hit. Only used when resize_mode=1.") + upscaling_resize_h: int = Field(default=512, title="Target Height", ge=1, description="Target height for the upscaler to hit. Only used when resize_mode=1.") + upscaling_crop: bool = Field(default=True, title="Crop to fit", description="Should the upscaler crop the image to fit in the chosen size?") + upscaler_1: str = Field(default="None", title="Main upscaler", description=f"The name of the main upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + upscaler_2: str = Field(default="None", title="Secondary upscaler", description=f"The name of the secondary upscaler to use, it has to be one of this list: {' , '.join([x.name for x in sd_upscalers])}") + extras_upscaler_2_visibility: float = Field(default=0, title="Secondary upscaler visibility", ge=0, le=1, allow_inf_nan=False, description="Sets the visibility of secondary upscaler, values should be between 0 and 1.") + upscale_first: bool = Field(default=False, title="Upscale first", description="Should the upscaler run before restoring faces?") + +class ExtraBaseResponse(BaseModel): + html_info: str = Field(title="HTML info", description="A series of HTML tags containing the process info.") + +class ExtrasSingleImageRequest(ExtrasBaseRequest): + image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") + +class ExtrasSingleImageResponse(ExtraBaseResponse): + image: str = Field(default=None, title="Image", description="The generated image in base64 format.") + +class FileData(BaseModel): + data: str = Field(title="File data", description="Base64 representation of the file") + name: str = Field(title="File name") + +class ExtrasBatchImagesRequest(ExtrasBaseRequest): + imageList: List[FileData] = Field(title="Images", description="List of images to work on. Must be Base64 strings") + +class ExtrasBatchImagesResponse(ExtraBaseResponse): + images: List[str] = Field(title="Images", description="The generated images in base64 format.") + +class PNGInfoRequest(BaseModel): + image: str = Field(title="Image", description="The base64 encoded PNG image") + +class PNGInfoResponse(BaseModel): + info: str = Field(title="Image info", description="A string with the parameters used to generate the image") + items: dict = Field(title="Items", description="An object containing all the info the image had") + +class ProgressRequest(BaseModel): + skip_current_image: bool = Field(default=False, title="Skip current image", description="Skip current image serialization") + +class ProgressResponse(BaseModel): + progress: float = Field(title="Progress", description="The progress with a range of 0 to 1") + eta_relative: float = Field(title="ETA in secs") + state: dict = Field(title="State", description="The current state snapshot") + current_image: str = Field(default=None, title="Current image", description="The current image in base64 format. opts.show_progress_every_n_steps is required for this to work.") + textinfo: str = Field(default=None, title="Info text", description="Info text used by WebUI.") + +class InterrogateRequest(BaseModel): + image: str = Field(default="", title="Image", description="Image to work on, must be a Base64 string containing the image's data.") + model: str = Field(default="clip", title="Model", description="The interrogate model used.") + +class InterrogateResponse(BaseModel): + caption: str = Field(default=None, title="Caption", description="The generated caption for the image.") + +class TrainResponse(BaseModel): + info: str = Field(title="Train info", description="Response string from train embedding or hypernetwork task.") + +class CreateResponse(BaseModel): + info: str = Field(title="Create info", description="Response string from create embedding or hypernetwork task.") + +class PreprocessResponse(BaseModel): + info: str = Field(title="Preprocess info", description="Response string from preprocessing task.") + +fields = {} +for key, metadata in opts.data_labels.items(): + value = opts.data.get(key) + optType = opts.typemap.get(type(metadata.default), type(value)) + + if (metadata is not None): + fields.update({key: (Optional[optType], Field( + default=metadata.default ,description=metadata.label))}) + else: + fields.update({key: (Optional[optType], Field())}) + +OptionsModel = create_model("Options", **fields) + +flags = {} +_options = vars(parser)['_option_string_actions'] +for key in _options: + if(_options[key].dest != 'help'): + flag = _options[key] + _type = str + if _options[key].default is not None: _type = type(_options[key].default) + flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))}) + +FlagsModel = create_model("Flags", **flags) + +class SamplerItem(BaseModel): + name: str = Field(title="Name") + aliases: List[str] = Field(title="Aliases") + options: Dict[str, str] = Field(title="Options") + +class UpscalerItem(BaseModel): + name: str = Field(title="Name") + model_name: Optional[str] = Field(title="Model Name") + model_path: Optional[str] = Field(title="Path") + model_url: Optional[str] = Field(title="URL") + scale: Optional[float] = Field(title="Scale") + +class SDModelItem(BaseModel): + title: str = Field(title="Title") + model_name: str = Field(title="Model Name") + hash: Optional[str] = Field(title="Short hash") + sha256: Optional[str] = Field(title="sha256 hash") + filename: str = Field(title="Filename") + config: Optional[str] = Field(title="Config file") + +class HypernetworkItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + +class FaceRestorerItem(BaseModel): + name: str = Field(title="Name") + cmd_dir: Optional[str] = Field(title="Path") + +class RealesrganItem(BaseModel): + name: str = Field(title="Name") + path: Optional[str] = Field(title="Path") + scale: Optional[int] = Field(title="Scale") + +class PromptStyleItem(BaseModel): + name: str = Field(title="Name") + prompt: Optional[str] = Field(title="Prompt") + negative_prompt: Optional[str] = Field(title="Negative Prompt") + +class ArtistItem(BaseModel): + name: str = Field(title="Name") + 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)") + +class MemoryResponse(BaseModel): + ram: dict = Field(title="RAM", description="System memory stats") + cuda: dict = Field(title="CUDA", description="nVidia CUDA memory stats") diff --git a/home/vlado/branches/automatic/modules/sd_hijack.py b/home/vlado/branches/automatic/modules/sd_hijack.py new file mode 100644 index 000000000..794767831 --- /dev/null +++ b/home/vlado/branches/automatic/modules/sd_hijack.py @@ -0,0 +1,264 @@ +import torch +from torch.nn.functional import silu +from types import MethodType + +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, sd_hijack_xlmr, xlmr + +import ldm.modules.attention +import ldm.modules.diffusionmodules.model +import ldm.modules.diffusionmodules.openaimodel +import ldm.models.diffusion.ddim +import ldm.models.diffusion.plms +import ldm.modules.encoders.modules + +attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward +diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity +diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward + +# new memory efficient cross attention blocks do not support hypernets and we already +# have memory efficient cross attention anyway, so this disables SD2.0's memory efficient cross attention +ldm.modules.attention.MemoryEfficientCrossAttention = ldm.modules.attention.CrossAttention +ldm.modules.attention.BasicTransformerBlock.ATTENTION_MODES["softmax-xformers"] = ldm.modules.attention.CrossAttention + +# silence new console spam from SD2 +ldm.modules.attention.print = lambda *args: None +ldm.modules.diffusionmodules.model.print = lambda *args: None + + +def apply_optimizations(): + undo_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_sub_quad_attention: + print("Applying sub-quadratic cross attention optimization.") + ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward + ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_attnblock_forward + optimization_method = 'sub-quadratic' + 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 cmd_opts.opt_split_attention and not torch.cuda.is_available()): + 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(): + ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward + ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity + ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward + + +def fix_checkpoint(): + """checkpoints are now added and removed in embedding/hypernet code, since torch doesn't want + checkpoints to be added when not training (there's a warning)""" + + pass + + +def weighted_loss(sd_model, pred, target, mean=True): + #Calculate the weight normally, but ignore the mean + loss = sd_model._old_get_loss(pred, target, mean=False) + + #Check if we have weights available + weight = getattr(sd_model, '_custom_loss_weight', None) + if weight is not None: + loss *= weight + + #Return the loss, as mean if specified + return loss.mean() if mean else loss + +def weighted_forward(sd_model, x, c, w, *args, **kwargs): + try: + #Temporarily append weights to a place accessible during loss calc + sd_model._custom_loss_weight = w + + #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely + #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set + if not hasattr(sd_model, '_old_get_loss'): + sd_model._old_get_loss = sd_model.get_loss + sd_model.get_loss = MethodType(weighted_loss, sd_model) + + #Run the standard forward function, but with the patched 'get_loss' + return sd_model.forward(x, c, *args, **kwargs) + finally: + try: + #Delete temporary weights if appended + del sd_model._custom_loss_weight + except AttributeError as e: + pass + + #If we have an old loss function, reset the loss function to the original one + if hasattr(sd_model, '_old_get_loss'): + sd_model.get_loss = sd_model._old_get_loss + del sd_model._old_get_loss + +def apply_weighted_forward(sd_model): + #Add new function 'weighted_forward' that can be called to calc weighted loss + sd_model.weighted_forward = MethodType(weighted_forward, sd_model) + +def undo_weighted_forward(sd_model): + try: + del sd_model.weighted_forward + except AttributeError as e: + pass + + +class StableDiffusionModelHijack: + fixes = None + comments = [] + layers = None + circular_enabled = False + clip = None + optimization_method = None + + embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase() + + def __init__(self): + self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir) + + def hijack(self, m): + if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: + model_embeddings = m.cond_stage_model.roberta.embeddings + model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self) + 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) + + apply_weighted_forward(m) + if m.cond_stage_key == "edit": + sd_hijack_unet.hijack_ddpm_edit() + + self.optimization_method = apply_optimizations() + + self.clip = m.cond_stage_model + + def flatten(el): + flattened = [flatten(children) for children in el.children()] + res = [el] + for c in flattened: + res += c + return res + + self.layers = flatten(m) + + def undo_hijack(self, m): + if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: + m.cond_stage_model = m.cond_stage_model.wrapped + + 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 + if type(model_embeddings.token_embedding) == EmbeddingsWithFixes: + model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped + elif type(m.cond_stage_model) == sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords: + m.cond_stage_model.wrapped.model.token_embedding = m.cond_stage_model.wrapped.model.token_embedding.wrapped + m.cond_stage_model = m.cond_stage_model.wrapped + + undo_optimizations() + undo_weighted_forward(m) + + self.apply_circular(False) + self.layers = None + self.clip = None + + def apply_circular(self, enable): + if self.circular_enabled == enable: + return + + self.circular_enabled = enable + + for layer in [layer for layer in self.layers if type(layer) == torch.nn.Conv2d]: + layer.padding_mode = 'circular' if enable else 'zeros' + + def clear_comments(self): + self.comments = [] + + def get_prompt_lengths(self, text): + _, token_count = self.clip.process_texts([text]) + + return token_count, self.clip.get_target_prompt_token_count(token_count) + + +class EmbeddingsWithFixes(torch.nn.Module): + def __init__(self, wrapped, embeddings): + super().__init__() + self.wrapped = wrapped + self.embeddings = embeddings + + def forward(self, input_ids): + batch_fixes = self.embeddings.fixes + self.embeddings.fixes = None + + inputs_embeds = self.wrapped(input_ids) + + if batch_fixes is None or len(batch_fixes) == 0 or max([len(x) for x in batch_fixes]) == 0: + return inputs_embeds + + vecs = [] + for fixes, tensor in zip(batch_fixes, inputs_embeds): + for offset, embedding in fixes: + emb = devices.cond_cast_unet(embedding.vec) + emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0]) + tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]) + + vecs.append(tensor) + + return torch.stack(vecs) + + +def add_circular_option_to_conv_2d(): + conv2d_constructor = torch.nn.Conv2d.__init__ + + def conv2d_constructor_circular(self, *args, **kwargs): + return conv2d_constructor(self, *args, padding_mode='circular', **kwargs) + + torch.nn.Conv2d.__init__ = conv2d_constructor_circular + + +model_hijack = StableDiffusionModelHijack() + + +def register_buffer(self, name, attr): + """ + Fix register buffer bug for Mac OS. + """ + + if type(attr) == torch.Tensor: + if attr.device != devices.device: + attr = attr.to(device=devices.device, dtype=(torch.float32 if devices.device.type == 'mps' else None)) + + setattr(self, name, attr) + + +ldm.models.diffusion.ddim.DDIMSampler.register_buffer = register_buffer +ldm.models.diffusion.plms.PLMSSampler.register_buffer = register_buffer diff --git a/home/vlado/branches/automatic/modules/ui_extra_networks_checkpoints.py b/home/vlado/branches/automatic/modules/ui_extra_networks_checkpoints.py new file mode 100644 index 000000000..04097a794 --- /dev/null +++ b/home/vlado/branches/automatic/modules/ui_extra_networks_checkpoints.py @@ -0,0 +1,39 @@ +import html +import json +import os +import urllib.parse + +from modules import shared, ui_extra_networks, sd_models + + +class ExtraNetworksPageCheckpoints(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Checkpoints') + + def refresh(self): + shared.refresh_checkpoints() + + def list_items(self): + checkpoint: sd_models.CheckpointInfo + for name, checkpoint in sd_models.checkpoints_list.items(): + path, ext = os.path.splitext(checkpoint.filename) + previews = [path + ".png", path + ".preview.png"] + + preview = None + for file in previews: + if os.path.isfile(file): + preview = self.link_preview(file) + break + + yield { + "name": checkpoint.name_for_extra, + "filename": path, + "preview": preview, + "search_term": self.search_terms_from_path(checkpoint.filename) + " " + (checkpoint.sha256 or ""), + "onclick": '"' + html.escape(f"""return selectCheckpoint({json.dumps(name)})""") + '"', + "local_preview": path + ".png", + } + + def allowed_directories_for_previews(self): + return [v for v in [shared.cmd_opts.ckpt_dir, sd_models.model_path] if v is not None] + diff --git a/home/vlado/branches/automatic/modules/ui_extra_networks_hypernets.py b/home/vlado/branches/automatic/modules/ui_extra_networks_hypernets.py new file mode 100644 index 000000000..578510887 --- /dev/null +++ b/home/vlado/branches/automatic/modules/ui_extra_networks_hypernets.py @@ -0,0 +1,36 @@ +import json +import os + +from modules import shared, ui_extra_networks + + +class ExtraNetworksPageHypernetworks(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Hypernetworks') + + def refresh(self): + shared.reload_hypernetworks() + + def list_items(self): + for name, path in shared.hypernetworks.items(): + path, ext = os.path.splitext(path) + previews = [path + ".png", path + ".preview.png"] + + preview = None + for file in previews: + if os.path.isfile(file): + preview = self.link_preview(file) + break + + yield { + "name": name, + "filename": path, + "preview": preview, + "search_term": self.search_terms_from_path(path), + "prompt": json.dumps(f""), + "local_preview": path + ".png", + } + + def allowed_directories_for_previews(self): + return [shared.cmd_opts.hypernetwork_dir] + diff --git a/home/vlado/branches/automatic/modules/ui_extra_networks_textual_inversion.py b/home/vlado/branches/automatic/modules/ui_extra_networks_textual_inversion.py new file mode 100644 index 000000000..bb64eb81e --- /dev/null +++ b/home/vlado/branches/automatic/modules/ui_extra_networks_textual_inversion.py @@ -0,0 +1,34 @@ +import json +import os + +from modules import ui_extra_networks, sd_hijack + + +class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage): + def __init__(self): + super().__init__('Textual Inversion') + self.allow_negative_prompt = True + + def refresh(self): + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) + + def list_items(self): + for embedding in sd_hijack.model_hijack.embedding_db.word_embeddings.values(): + path, ext = os.path.splitext(embedding.filename) + preview_file = path + ".preview.png" + + preview = None + if os.path.isfile(preview_file): + preview = self.link_preview(preview_file) + + yield { + "name": embedding.name, + "filename": embedding.filename, + "preview": preview, + "search_term": self.search_terms_from_path(embedding.filename), + "prompt": json.dumps(embedding.name), + "local_preview": path + ".preview.png", + } + + def allowed_directories_for_previews(self): + return list(sd_hijack.model_hijack.embedding_db.embedding_dirs) diff --git a/home/vlado/branches/automatic/test/basic_features/txt2img_test.py b/home/vlado/branches/automatic/test/basic_features/txt2img_test.py new file mode 100644 index 000000000..5aa43a44a --- /dev/null +++ b/home/vlado/branches/automatic/test/basic_features/txt2img_test.py @@ -0,0 +1,80 @@ +import unittest +import requests + + +class TestTxt2ImgWorking(unittest.TestCase): + def setUp(self): + self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img" + self.simple_txt2img = { + "enable_hr": False, + "denoising_strength": 0, + "firstphase_width": 0, + "firstphase_height": 0, + "prompt": "example prompt", + "styles": [], + "seed": -1, + "subseed": -1, + "subseed_strength": 0, + "seed_resize_from_h": -1, + "seed_resize_from_w": -1, + "batch_size": 1, + "n_iter": 1, + "steps": 3, + "cfg_scale": 7, + "width": 64, + "height": 64, + "restore_faces": False, + "tiling": False, + "negative_prompt": "", + "eta": 0, + "s_churn": 0, + "s_tmax": 0, + "s_tmin": 0, + "s_noise": 1, + "sampler_index": "Euler a" + } + + def test_txt2img_simple_performed(self): + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_with_negative_prompt_performed(self): + self.simple_txt2img["negative_prompt"] = "example negative prompt" + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_with_complex_prompt_performed(self): + self.simple_txt2img["prompt"] = "((emphasis)), (emphasis1:1.1), [to:1], [from::2], [from:to:0.3], [alt|alt1]" + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_not_square_image_performed(self): + self.simple_txt2img["height"] = 128 + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_with_hrfix_performed(self): + self.simple_txt2img["enable_hr"] = True + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_with_tiling_performed(self): + self.simple_txt2img["tiling"] = True + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_with_restore_faces_performed(self): + self.simple_txt2img["restore_faces"] = True + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_with_vanilla_sampler_performed(self): + self.simple_txt2img["sampler_index"] = "PLMS" + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + self.simple_txt2img["sampler_index"] = "DDIM" + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_multiple_batches_performed(self): + self.simple_txt2img["n_iter"] = 2 + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + def test_txt2img_batch_performed(self): + self.simple_txt2img["batch_size"] = 2 + self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200) + + +if __name__ == "__main__": + unittest.main() diff --git a/q.sh b/q.sh new file mode 100644 index 000000000..da5f21240 --- /dev/null +++ b/q.sh @@ -0,0 +1,16 @@ +echo 'README.md +extensions-builtin/Lora/ui_extra_networks_lora.py +javascript/progressbar.js +modules/api/api.py +modules/api/models.py +modules/models/diffusion/uni_pc/sampler.py +modules/sd_hijack.py +modules/ui_extra_networks_checkpoints.py +modules/ui_extra_networks_hypernets.py +modules/ui_extra_networks_textual_inversion.py +test/basic_features/txt2img_test.py' | while read F; do + echo "Creating backup" $F + echo $F | cpio -pvd /tmp/backup + echo "Copying original" $F + echo ~/branches/automatic/$F | cpio -pvd . +done