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jdopensource/
JoyAI-Image-Edit-Plus-Diffusers

Name: JoyAI-Image-Edit-Plus-Diffusers
Rating: 2.5 (365 reviews)

Qwen/Qwen3-VL-8B-Instruct

finetune

apache-2.0

JoyImageEditPlusPipeline

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JoyAI-Image Edit Plus

JoyAI-Image Edit Plus is a multi-image instruction-guided editing model from the JoyAI-Image family. It accepts multiple reference images and a text instruction to generate a new image that combines elements from the references according to the instruction.

Model Architecture

Component	Model	Size
Text Encoder	Qwen3-VL-8B-Instruct	8B
Transformer (MMDiT)	JoyImageEditPlusTransformer3DModel	16B
VAE	AutoencoderKLWan	240M
Scheduler	FlowMatchEulerDiscreteScheduler	-

Installation

JoyImageEditPlusPipeline has not yet been merged into the official diffusers release. Before it is available in a stable version, you need to install diffusers from the PR branch:

pip install git+https://github.com/tangyanf/diffusers.git@add-joyimage-edit-plus

If you have already installed diffusers, make sure to uninstall it first:

bash

pip uninstall diffusers -y
pip install git+https://github.com/tangyanf/diffusers.git@add-joyimage-edit-plus

Once the PR is merged into the official diffusers repository, you can switch back to the standard installation:

pip install diffusers --upgrade

Usage

python

import torch
from PIL import Image
from diffusers import JoyImageEditPlusPipeline

pipe = JoyImageEditPlusPipeline.from_pretrained(
    "jdopensource/JoyAI-Image-Edit-Plus-Diffusers",
    torch_dtype=torch.bfloat16,
).to("cuda")

# Load reference images
images = [
    Image.open("reference_0.png").convert("RGB"),
    Image.open("reference_1.png").convert("RGB"),
]

# Determine output resolution from the last reference image
target_h, target_w = pipe.vae_image_processor.get_default_height_width(images[-1])

# Generate
result = pipe(
    images=images,
    prompt="Combine the person from the second image with the scene from the first image.",
    negative_prompt="low quality, blurry, deformed",
    height=target_h,
    width=target_w,
    num_inference_steps=30,
    guidance_scale=4.0,
    generator=torch.Generator(device="cuda").manual_seed(42),
)
result.images[0].save("output.png")

Example

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

Input 0	Input 1	Output
input_0	input_1	output

Recommended Parameters

Parameter	Value
`num_inference_steps`	30
`guidance_scale`	4.0
`torch_dtype`	`torch.bfloat16`
Resolution	Auto-detected via `vae_image_processor.get_default_height_width()` (1024-base buckets)

CLI Inference

bash

python inference.py \
    --model_path jdopensource/JoyAI-Image-Edit-Plus-Diffusers \
    --images examples/input_0.png examples/input_1.png \
    --prompt "The woman is lovingly holding the cute puppy in her arms" \
    --num_inference_steps 30 \
    --guidance_scale 4.0 \
    --seed 42 \
    --output output.png

Model Details

Developed by: JD.com
License: Apache-2.0
Diffusers version: >= 0.39.0
Framework: PyTorch

Citation

bibtex

@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}
}