jdopensource / JoyAI-Image-Edit-Plus-ComfyUI

JoyAI-Image-Edit-Plus (ComfyUI weights)

Single-file .safetensors checkpoints of JoyAI-Image-Edit-Plus , repackaged for native ComfyUI support (no custom node required).

JoyAI-Image-Edit-Plus is the multi-image instruction-guided editing model of the JoyAI-Image family. It accepts 1–6 reference images and a text instruction, and generates a new image that combines elements from the references according to the instruction.

Files

The repo layout already matches ComfyUI/models/ , so a single hf download into your models root drops every file where it needs to go.

Model architecture

• Transformer : 40-layer DiT, hidden size 4096, 32 heads, in/out channels 16, patch size [1, 2, 2] , 3D RoPE ( rope_dim_list = [16, 56, 56] , theta 10000). Each reference image is patchified independently and concatenated on the sequence dimension with a per-image temporal offset in the 3D RoPE grid, so references may differ in resolution.
• Text encoder : Qwen3VLForConditionalGeneration (text dim 4096). The instruction is wrapped with one <|vision_start|><|image_pad|><|vision_end|> block per reference image.
• VAE : AutoencoderKLWan (z_dim 16, spatial downscale 8, temporal downscale 4) — the same VAE used by the single-image edit model.
• Scheduler : FlowMatch (Euler), sampling shift 1.5.

Weight names are byte-identical to the diffusers checkpoint (894 transformer keys, zero renaming); ComfyUI auto-detects the model as joyimage .

Installation

The model runs natively in ComfyUI. Native support is proposed upstream in Comfy-Org/ComfyUI#14428 ; until it is merged, install the fork branch:

git clone -b joyimage-edit-pr https://github.com/feice-huang/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt

Once the PR is merged upstream, the stock ComfyUI release will run these weights with no fork needed.

Then download the weights straight into ComfyUI/models/ :

hf download jdopensource/JoyAI-Image-Edit-Plus-ComfyUI \
  --local-dir /path/to/ComfyUI/models

Restart ComfyUI.

Usage

Example workflow: workflow_joyimage_edit.json

Build the graph from these native nodes:

1. Load Diffusion Model ( UNETLoader ) → diffusion_models/joy_image_edit_plus_bf16.safetensors
2. Load CLIP ( CLIPLoader ) → text_encoders/qwen3vl_joyimage_bf16.safetensors , type joyimage
3. Load VAE ( VAELoader ) → vae/joy_image_edit_vae.safetensors
4. Load Image ( LoadImage ) for each reference (1–6)
5. TextEncodeJoyImageEditPlus — feed clip , vae , the instruction, and the reference images into image1 … image6 . Wire one instance for the positive prompt and one (empty prompt, same images) for the negative. Each node bucket-resizes the references to the 1024-base buckets, VAE-encodes them, and appends the reference latents to the conditioning; its image output feeds VAEDecode / empty-latent sizing.
6. KSampler → VAEDecode → SaveImage

Recommended parameters

Example

Prompt: "The woman is lovingly holding the cute puppy in her arms"

Input 0	Input 1	Output

Model details

• Developed by : JD.com
• License : Apache-2.0
• Framework : PyTorch / ComfyUI

Links

• Source code and documentation: github.com/jd-opensource/JoyAI-Image
• Original Diffusers-format weights: jdopensource/JoyAI-Image-Edit-Plus-Diffusers
• Single-image edit model (ComfyUI): jdopensource/JoyAI-Image-Edit-ComfyUI

Citation

@misc{joyai-image-2025,
  title={JoyAI-Image: A Unified Multimodal Foundation Model for Image Understanding, Generation, and Editing},
  author={Joy Future Academy, JD},
  year={2025},
  url={https://github.com/jd-opensource/JoyAI-Image}
}