(9/9) This guide is no longer updated!

Please visit https://rentry.org/voldy for the latest guide with new features

Special thanks to all anons who contributed
Note: In active development, there may be some bugs
What does this add?

Gradio GUI: A retard-proof, fully featured frontend for both txt2img and img2img generation
No more manually typing parameters, now all you have to do is write your prompt and adjust sliders
ESRGAN Upscaling (NEW): Boosts the resolution of images with a built-in RealESRGAN option
Mask painting (NEW): Powerful tool for re-generating only specific parts of an image you want to change
Loopback (NEW): Automatically feed the last generated sample back into img2img
Prompt Weighting (NEW): Adjust the strength of different terms in your prompt
GFPGAN Face Correction: Automatically correct distorted faces with a built-in GFPGAN option, fixes them in less than half a second
Multiple K-diffusion samplers: Far greater quality outputs than the default sampler, less distortion and more accurate
CFG: Classifier free guidance scale, a feature for fine-tuning your output
Memory Monitoring: Shows Vram usage and generation time after outputting.
Word Seeds: Use words instead of seed numbers
Launcher Automatic shortcut to load the model, no more typing in Conda
Lighter on Vram: 512x512 img2img & txt2img tested working on 6gb but is also possible on 4gb (see under links/notes/tips)


(Updated 8/29) Alternate Prepack Installer available Here
^(Use Megabasterd for downloading large MEGA files without an account)^
Alternate guide for Linux users available Here
(txt2img only) CPU-only guide available Here
Japanese guide here 日本語ガイド


Step 1: Download the 1.4 AI model from huggingface (requires signup) or HERE
Torrent magnet: https://rentry.org/sdiffusionmagnet

Step 2: Git clone or download the main repo HERE and extract

Step 2a: (OPTIONAL): Git clone or download the WebUI repo HERE and extract

  • Move .py scripts from stable-diffusion-webui into stable-diffusion-main/scripts
  • Move webui.yaml to stable-diffusion-main/configs/webui
  • Move all other files fromstable-diffusion-webui into the stable-diffusion-main folder
  • replace if there are any file conflicts.
  • (Warning: This is to add experimental features which aren't merged into the main repo yet There may be bugs present, not recommended for most users)

Step 3: Navigate to stable-diffusion-main/models/ldm

  • Rename your .ckpt file to "model.ckpt", and place it in stable-diffusion-main/models/ldm/stable-diffusion-v1

Step 4: Edit environment.yaml and change 'name' in line 1 from "ldm" to "ldo"
(This is to prevent conflicts with other installations)

Step 5: Download Miniconda HERE. Download Miniconda 3

Step 6: Install Miniconda in the default location. Install for all users.
-Uncheck "Register Miniconda as the system Python 3.9" unless you want to

Step 7 Run "webui.cmd" from /stable-diffusion-main
Wait patiently while it installs dependencies and does a first time run.
It may seem "stuck" but it isn't. It may take up to 10-15 minutes.
And you're done!


  • GFPGAN (Face Correction)
    1. Run webui.cmd at least once to generate the necessary folders
    2. Download the GFPGAN pre-trained model
    place it in /src/gfpgan/experiments/pretrained_models/
    3. Run webui.cmd and GFPGAN options should be available!
  • ESRGAN (Upscaling)
    1. Run webui.cmd at least once to generate the necessary folders
    2. Download RealESRGAN_x4plus.pth and RealESRGAN_x4plus_anime_6B.pth
    place them in /src/realesrgan/experiments/pretrained_models
    3. Run webui.cmd and ESRGAN options should be available!
    Note: If you plan on running with 4gb, it is recommended to not add gfpgan and esrgan support due to mildly raised memory usage


  • Open webui.cmd and wait
  • After loading, the console should prompt you with something like 'localhost:7860'
  • Open your browser and enter the address
  • You should now be in an interface with a txt2img and img2img tab
  • Have fun!
    Note: You will get "prefix already exists:ldo" when running webui.cmd. This is not an error.
    It just means you already installed the environment when running the script the first time.



  1. Edit /scripts/relauncher.py in your preferred text editor
  2. Change line 8 in relauncher.py FROM "python scripts/webui.py" to the following:
    "python scripts/webui.py --optimized" and save
    (This tells it to optimize Vram by generating incrementally, it should be noticed that this sacrifices generation speed)
  3. Launch webui.cmd like normal
  • If you are still getting an 'Out of Memory' error:
    delete or rename the ESRGAN and GFPGAN models (so they don't load into memory) and relaunch
    (You can use them both as external programs anyway anyway)
  • If your output is a solid green square (known problem on GTX 16xx):
    Add --precision full --no-half to the launch parameters above, it should look like this:
    "python scripts/webui.py --precision full --no-half --optimized"
    Unfortunately, the full precision fix raises ram use drastically so you may may have to moderately reduce your output to 448x448 if on 4gb
  • If your previous installation is missing anything referenced in this guide, it may be best to start from scratch with the new repo
  • "I keep getting X not found!":
    You may have a different conda installation path:
    If your conda installation is somewhere that isn't \Programdata\miniconda3, adjust the path in webui.cmd accordingly
  • If you want to delete your environment for reinstallation, run "conda env remove -n ldo" from Miniconda
  • Double check that your environment.yaml file says "ldo"
  • Bug: img2img is currently not working with the optimized parameters, this will be corrected soon
  • If you're on Linux, just run python scripts/webui.py directly instead of using the .cmd
  • If your output is a jumbled rainbow mess your image resolution is set TOO LOW
  • Having too high of a CFG level will also introduce rainbow distortion, your CFG shouldn't be set above 20
  • If you are upgrading from an old environment which doesn't meet current dependencies(such as waifu-diffusion),
    Delete all folders inside /src before running webui.cmd
  • (Fixed) If your generations are unusually slow, disable hardware acceleration in the browser that is running webui
  • On older systems, you may have to change cudatoolkit=11.3 to cudatoolkit=9.0 in the environment.yaml file
  • Make sure your installation is on the C: drive, otherwise adjust webui.cmd with your drive letter.
  • This guide is designed for NVIDIA GPUs only, as stable diffusion requires cuda cores.
    AMD users should try https://rentry.org/sdamd
  • You can drag your favorite result from the output tab on the right back into img2img for further iteration
  • The k_euler_a and k_dpm_2_a samplers give vastly different, more intricate results from the same seed & prompt
  • Unlike other samplers, k_euler_a can generate high quality results from low steps. Try it with 10-25 instead of 50
  • If you have more Vram but are still forced to use the optimized parameter, you can try --optimized-turbofor a faster experience
  • The seed for each generated result is in the output filename if you want to revisit it
  • Using the same keywords as a generated image in img2img produces interesting variants
  • It's recommended to have your prompts be at least 512 pixels in one dimension, or a 384x384 square at the smallest
    Anything smaller will have heavy artifacting
  • 512x512 will always yield the most accurate results as the model was trained at that resolution
  • Try Low strength (0.3-0.4) + High CFG in img2img for interesting outputs
  • You can use Japanese Unicode characters in prompts
  • You can prune a v1.3 weight model using "python scripts/prune.py" in waifu-diffusion-main
    Pruning shrinks the file size to 2gb instead of 7. Output remains largely equivalent
    Comparison- https://i.postimg.cc/ZRKz4tJv/textprune.png
  • (Prune.py does not work on the new model, and does not matter as v1.4 is less heavy than v1.3 )
  • You can run GFPGAN and ESRGAN on your CPU instead of GPU by added the following parameters: --gfpgan-cpu --esrgan-cpu
    (This does not work for everyone and may have errors)

The original v1.3 leaked model from June can be downloaded here:
Backup Download: https://download1980.mediafire.com/3nu6nlhy92ag/wnlyj8vikn2kpzn/sd-v1-3-full-ema.ckpt
Torrent Magnet: https://rentry.co/6gocs

The original guide (replaced as of 8/25/22) is here: https://rentry.org/kretard

RENDER TIME BY GPU (50 steps) Time

Pub: 25 Aug 2022 07:33 UTC
Edit: 09 Sep 2022 05:34 UTC
Views: 162502