265 lines
11 KiB
Markdown
265 lines
11 KiB
Markdown
# Stable Diffusion web UI
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A browser interface based on Gradio library for Stable Diffusion.
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Original script with Gradio UI was written by a kind anonymous user. This is a modification.
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## Installing and running
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### Stable Diffusion
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This script assumes that you already have main Stable Diffusion sutff installed, assumed to be in directory `/sd`.
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If you don't have it installed, follow the guide:
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- https://rentry.org/kretard
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This repository's `webgui.py` is a replacement for `kdiff.py` from the guide.
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Particularly, following files must exist:
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- `/sd/configs/stable-diffusion/v1-inference.yaml`
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- `/sd/models/ldm/stable-diffusion-v1/model.ckpt`
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- `/sd/ldm/util.py`
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- `/sd/k_diffusion/__init__.py`
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### GFPGAN
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If you want to use GFPGAN to improve generated faces, you need to install it separately.
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Follow instructions from https://github.com/TencentARC/GFPGAN, but when cloning it, do so into Stable Diffusion main directory, `/sd`.
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After that download [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) and put it
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into the `/sd/GFPGAN/experiments/pretrained_models` directory. If you're getting troubles with GFPGAN support, follow instructions
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from the GFPGAN's repository until `inference_gfpgan.py` script works.
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The following files must exist:
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- `/sd/GFPGAN/inference_gfpgan.py`
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- `/sd/GFPGAN/experiments/pretrained_models/GFPGANv1.3.pth`
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If the GFPGAN directory does not exist, you will not get the option to use GFPGAN in the UI. If it does exist, you will either be able
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to use it, or there will be a message in console with an error related to GFPGAN.
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### Web UI
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Run the script as:
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`python webui.py`
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When running the script, you must be in the main Stable Diffusion directory, `/sd`. If you cloned this repository into a subdirectory
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of `/sd`, say, the `stable-diffusion-webui` directory, you will run it as:
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`python stable-diffusion-webui/webui.py`
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When launching, you may get a very long warning message related to some weights not being used. You may freely ignore it.
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After a while, you will get a message like this:
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```
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Running on local URL: http://127.0.0.1:7860/
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```
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Open the URL in browser, and you are good to go.
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## Features
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The script creates a web UI for Stable Diffusion's txt2img and img2img scripts. Following are features added
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that are not in original script.
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### Extras tab
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Additional neural network image improvement methods unrelated to stable diffusion.
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#### GFPGAN
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Lets you improve faces in pictures using the GFPGAN model. There is a checkbox in every tab to use GFPGAN at 100%, and
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also a separate tab that just allows you to use GFPGAN on any picture, with a slider that controls how strongthe effect is.
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#### Real-ESRGAN
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Image upscaler. You can choose from multiple models by original author, and specify by how much the image should be upscaled.
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Requires `realesrgan` librarty:
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```commandline
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pip install realesrgan
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```
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### Sampling method selection
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Pick out of multiple sampling methods for txt2img:
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### Prompt matrix
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Separate multiple prompts using the `|` character, and the system will produce an image for every combination of them.
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For example, if you use `a busy city street in a modern city|illustration|cinematic lighting` prompt, there are four combinations possible (first part of prompt is always kept):
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- `a busy city street in a modern city`
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- `a busy city street in a modern city, illustration`
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- `a busy city street in a modern city, cinematic lighting`
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- `a busy city street in a modern city, illustration, cinematic lighting`
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Four images will be produced, in this order, all with same seed and each with corresponding prompt:
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Another example, this time with 5 prompts and 16 variations:
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If you use this feature, batch count will be ignored, because the number of pictures to produce
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depends on your prompts, but batch size will still work (generating multiple pictures at the
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same time for a small speed boost).
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### Flagging
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Click the Flag button under the output section, and generated images will be saved to `log/images` directory, and generation parameters
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will be appended to a csv file `log/log.csv` in the `/sd` directory.
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> but every image is saved, why would I need this?
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If you're like me, you experiment a lot with prompts and settings, and only few images are worth saving. You can
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just save them using right click in browser, but then you won't be able to reproduce them later because you will not
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know what exact prompt created the image. If you use the flag button, generation paramerters will be written to csv file,
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and you can easily find parameters for an image by searching for its filename.
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### Copy-paste generation parameters
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A text output provides generation parameters in an easy to copy-paste form for easy sharing.
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If you generate multiple pictures, the displayed seed will be the seed of the first one.
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### Correct seeds for batches
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If you use a seed of 1000 to generate two batches of two images each, four generated images will have seeds: `1000, 1001, 1002, 1003`.
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Previous versions of the UI would produce `1000, x, 1001, x`, where x is an iamge that can't be generated by any seed.
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### Resizing
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There are three options for resizing input images in img2img mode:
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- Just resize - simply resizes source image to target resolution, resulting in incorrect aspect ratio
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- Crop and resize - resize source image preserving aspect ratio so that entirety of target resolution is occupied by it, and crop parts that stick out
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- Resize and fill - resize source image preserving aspect ratio so that it entirely fits target resolution, and fill empty space by rows/columns from source image
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Example:
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### Loading
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Gradio's loading graphic has a very negative effect on the processing speed of the neural network.
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My RTX 3090 makes images about 10% faster when the tab with gradio is not active. By default, the UI
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now hides loading progress animation and replaces it with static "Loading..." text, which achieves
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the same effect. Use the `--no-progressbar-hiding` commandline option to revert this and show loading animations.
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### Prompt validation
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Stable Diffusion has a limit for input text length. If your prompt is too long, you will get a
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warning in the text output field, showing which parts of your text were truncated and ignored by the model.
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### Loopback
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A checkbox for img2img allowing to automatically feed output image as input for the next batch. Equivalent to
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saving output image, and replacing input image with it. Batch count setting controls how many iterations of
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this you get.
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Usually, when doing this, you would choose one of many images for the next iteration yourself, so the usefulness
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of this feature may be questionable, but I've managed to get some very nice outputs with it that I wasn't abble
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to get otherwise.
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Example: (cherrypicked result; original picture by anon)
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### Png info
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Adds information about generation parameters to PNG as a text chunk. You
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can view this information later using any software that supports viewing
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PNG chunk info, for example: https://www.nayuki.io/page/png-file-chunk-inspector
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### Textual Inversion
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Allows you to use pretrained textual inversion embeddings.
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See originial site for details: https://textual-inversion.github.io/.
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I used lstein's repo for training embdedding: https://github.com/lstein/stable-diffusion; if
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you want to train your own, I recommend following the guide on his site.
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No additional libraries/repositories are required to use pretrained embeddings.
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To make use of pretrained embeddings, create `embeddings` directory in the root dir of Stable
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Diffusion and put your embeddings into it. They must be .pt files about 5Kb in size, each with only
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one trained embedding, and the filename (without .pt) will be the term you'd use in prompt
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to get that embedding.
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As an example, I trained one for about 5000 steps: https://files.catbox.moe/e2ui6r.pt; it does
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not produce very good results, but it does work. Download and rename it to `Usada Pekora.pt`,
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and put it into `embeddings` dir and use Usada Pekora in prompt.
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### Settings
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A tab with settings, allowing you to use UI to edit more than half of parameters that previously
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were commandline. Settings are saved to config.js file. Settings that remain as commandline
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options are ones that are required at startup.
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### Attention
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Using `()` in prompt decreases model's attention to enclosed words, and `[]` increases it. You can combine
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multiple modifiers:
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### SD upscale
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Upscale image using RealESRGAN and then go through tiles of the result, improving them with img2img.
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Original idea by: https://github.com/jquesnelle/txt2imghd. This is an independent implementation.
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To use this feature, tick a checkbox in the img2img interface. Original
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image will be upscaled to twice the original width and height, while width and height sliders
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will specify the size of individual tiles. At the moment this method does not support batch size.
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### User scripts
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If the program is launched with `--allow-code` option, an extra text input field for script code
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is available in txt2img interface. It allows you to input python code that will do the work with
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image. If this field is not empty, the processing that would happen normally is skipped.
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In code, access parameters from web UI using the `p` variable, and provide outputs for web UI
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using the `display(images, seed, info)` function. All globals from script are also accessible.
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As an example, here is a script that draws a chart seen below (and also saves it as `test/gnomeplot/gnome5.png`):
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```python
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steps = [4, 8,12,16, 20]
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cfg_scales = [5.0,10.0,15.0]
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def cell(x, y, p=p):
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p.steps = x
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p.cfg_scale = y
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return process_images(p).images[0]
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images = [draw_xy_grid(
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xs = steps,
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ys = cfg_scales,
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x_label = lambda x: f'Steps = {x}',
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y_label = lambda y: f'CFG = {y}',
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cell = cell
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)]
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save_image(images[0], 'test/gnomeplot', 'gnome5')
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display(images)
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```
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A more simple script that would just process the image and output it normally:
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```python
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processed = process_images(p)
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print("Seed was: " + str(processed.seed))
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display(processed.images, processed.seed, processed.info)
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```
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### `--lowvram`
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Optimizations for GPUs with low VRAM. This should make it possible to generate 512x512 images on videocards with 4GB memory.
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The original idea of those ideas is by basujindal: https://github.com/basujindal/stable-diffusion. Model is separated into modules,
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and only one module is kept in GPU memory; when another module needs to run, the previous is removed from GPU memory.
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It should be obvious but the nature of those optimizations makes the processing run slower -- about 10 times slower
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compared to normal operation on my RTX 3090.
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This is an independent implementation that does not require any modification to original Stable Diffusion code, and
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with all code concenrated in one place rather than scattered around the program.
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