Instructions to use jdopensource/JoyAI-Image-Edit-ComfyUI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jdopensource/JoyAI-Image-Edit-ComfyUI with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jdopensource/JoyAI-Image-Edit-ComfyUI", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
JoyAI-Image-Edit (ComfyUI weights)
Single-file .safetensors checkpoints of JoyAI-Image-Edit, repackaged for the standard ComfyUI model folder layout.
Use these with the joyai_image_comfyui custom node from the JoyAI-Image GitHub repo.
Files
| File | Size | Goes into | Component |
|---|---|---|---|
diffusion_models/joy_image_edit_bf16.safetensors |
~32 GB | ComfyUI/models/diffusion_models/ |
JoyImageEditTransformer3DModel (bf16) |
text_encoders/qwen3vl_joyimage_bf16.safetensors |
~17 GB | ComfyUI/models/text_encoders/ |
Qwen3VL text encoder (bf16) |
vae/joy_image_edit_vae.safetensors |
~254 MB | ComfyUI/models/vae/ |
AutoencoderKLWan |
The repo's directory layout already matches ComfyUI/models/, so a single hf download into your models root drops every file where it needs to go.
Quick start
# 1) Install the custom node
cd ComfyUI/custom_nodes
git clone https://github.com/jd-opensource/JoyAI-Image.git
cp -r JoyAI-Image/joyai_image_comfyui ./
rm -rf JoyAI-Image
# 2) Download the weights straight into ComfyUI/models/
hf download jdopensource/JoyAI-Image-Edit-ComfyUI \
--local-dir /path/to/ComfyUI/models
# 3) Restart ComfyUI โ new nodes appear under `loaders/joyai` and `image/joyai`.
Requirements
diffusers >= 0.39.0.dev0(install from source:pip install git+https://github.com/huggingface/diffusers.git)transformers >= 4.57.0
GGUF quantizations
Lower-bit GGUF quants of the transformer and text encoder are available at huangfeice/JoyAI-Image-Edit-Diffusers-GGUF (community contribution). The VAE here is the only VAE you need โ GGUF doesn't quantize VAE.
Links
- Source code, node implementation, and full documentation: github.com/jd-opensource/JoyAI-Image
- Original Diffusers-format weights: jdopensource/JoyAI-Image-Edit-Diffusers
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