Stable Diffusion 3.5

Inference-only tiny reference implementation of SD3 and SD3.5 - everything you need for simple inference using SD3/SD3.5, excluding the weights files.

Contains code for the text encoders (OpenAI CLIP-L/14, OpenCLIP bigG, Google T5-XXL) (these models are all public), the VAE Decoder (similar to previous SD models, but 16-channels and no postquantconv step), and the core MM-DiT (entirely new).

Note: this repo is a reference library meant to assist partner organizations in implementing SD3. For alternate inference, use Comfy.

Download

Download the following models from HuggingFace into models directory:

1. Stability AI SD3.5 Large or Stability AI SD3.5 Large Turbo
2. OpenAI CLIP-L
3. OpenCLIP bigG
4. Google T5-XXL

This code also works for SD3 Medium.

Install

# Note: on windows use "python" not "python3"
python3 -s -m venv .sd3.5
source .sd3.5/bin/activate
# or on windows: venv/scripts/activate
python3 -s -m pip install -r requirements.txt

Run

# Generate a cat using SD3.5 Large model (at models/sd3.5_large.safetensors) with its default settings
python3 sd3_infer.py --prompt "cute wallpaper art of a cat"
# Or use a text file with a list of prompts
python3 sd3_infer.py --prompt path/to/my_prompts.txt
# Generate a cat using SD3.5 Large Turbo with its default settings
python3 sd3_infer.py --prompt path/to/my_prompts.txt --model models/sd3.5_large_turbo.safetensors
# Generate a cat using SD3 Medium with its default settings
python3 sd3_infer.py --prompt path/to/my_prompts.txt --model models/sd3_medium.safetensors

Images will be output to outputs/<MODEL>/<PROMPT>_<DATETIME>_<POSTFIX> by default. To add a postfix to the output directory, add --postfix <my_postfix>. For example,

python3 sd3_infer.py --prompt path/to/my_prompts.txt --postfix "steps100" --steps 100

To change the resolution of the generated image, add --width <WIDTH> --height <HEIGHT>.

To generate images using SD3 Medium, download the model and use --model models/sd3_medium.safetensors.

File Guide

• sd3_infer.py - entry point, review this for basic usage of diffusion model
• sd3_impls.py - contains the wrapper around the MMDiTX and the VAE
• other_impls.py - contains the CLIP models, the T5 model, and some utilities
• mmditx.py - contains the core of the MMDiT-X itself
• folder models with the following files (download separately):
  • clip_l.safetensors (OpenAI CLIP-L, same as SDXL/SD3, can grab a public copy)
  • clip_g.safetensors (openclip bigG, same as SDXL/SD3, can grab a public copy)
  • t5xxl.safetensors (google T5-v1.1-XXL, can grab a public copy)
  • sd3.5_large.safetensors (or sd3_medium.safetensors)

Code Origin

The code included here originates from:

• Stability AI internal research code repository (MM-DiT)
• Public Stability AI repositories (eg VAE)
• Some unique code for this reference repo written by Alex Goodwin and Vikram Voleti for Stability AI
• Some code from ComfyUI internal Stability implementation of SD3 (for some code corrections and handlers)
• HuggingFace and upstream providers (for sections of CLIP/T5 code)

Legal

Stability AI’s Stable Diffusion 3.5 model, including its code and weights, are licensed subject to the Stability AI Community License Agreement (https://stability.ai/community-license-agreement), as well as our accompanying Acceptable Use Policy (https://stability.ai/use-policy).

Stability AI Community License, Copyright © Stability AI, Ltd. All Rights Reserved.

Note

Some code in other_impls originates from HuggingFace and is subject to the HuggingFace Transformers Apache2 License