DiffSynth-Studio/docs/en/Model_Details/JoyAI-Image.md

JoyAI-Image

JoyAI-Image is a unified multi-modal foundation model open-sourced by JD.com, supporting image understanding, text-to-image generation, and instruction-guided image editing.

Installation

Before performing model inference and training, please install DiffSynth-Studio first.

git clone https://github.com/modelscope/DiffSynth-Studio.git
cd DiffSynth-Studio
pip install -e .

For more information on installation, please refer to Setup Dependencies.

Quick Start

Running the following code will load the jd-opensource/JoyAI-Image-Edit model for inference. VRAM management is enabled, the framework automatically controls parameter loading based on available VRAM, requiring a minimum of 4GB VRAM.

from diffsynth.pipelines.joyai_image import JoyAIImagePipeline, ModelConfig
import torch
from PIL import Image
from modelscope import dataset_snapshot_download

# Download dataset
dataset_snapshot_download(
    dataset_id="DiffSynth-Studio/diffsynth_example_dataset",
    local_dir="data/diffsynth_example_dataset",
    allow_file_pattern="joyai_image/JoyAI-Image-Edit/*"
)

vram_config = {
    "offload_dtype": torch.bfloat16,
    "offload_device": "cpu",
    "onload_dtype": torch.bfloat16,
    "onload_device": "cpu",
    "preparing_dtype": torch.bfloat16,
    "preparing_device": "cuda",
    "computation_dtype": torch.bfloat16,
    "computation_device": "cuda",
}

pipe = JoyAIImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="transformer/transformer.pth", **vram_config),
        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="JoyAI-Image-Und/model*.safetensors", **vram_config),
        ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="vae/Wan2.1_VAE.pth", **vram_config),
    ],
    processor_config=ModelConfig(model_id="jd-opensource/JoyAI-Image-Edit", origin_file_pattern="JoyAI-Image-Und/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)

# Use first sample from dataset
dataset_base_path = "data/diffsynth_example_dataset/joyai_image/JoyAI-Image-Edit"
prompt = "将裙子改为粉色"
edit_image = Image.open(f"{dataset_base_path}/edit/image1.jpg").convert("RGB")

output = pipe(
    prompt=prompt,
    edit_image=edit_image,
    height=1024,
    width=1024,
    seed=0,
    num_inference_steps=30,
    cfg_scale=5.0,
)

output.save("output_joyai_edit_low_vram.png")

Model Overview

Model ID	Inference	Low VRAM Inference	Full Training	Full Training Validation	LoRA Training	LoRA Training Validation
jd-opensource/JoyAI-Image-Edit	code	code	code	code	code	code

Model Inference

The model is loaded via JoyAIImagePipeline.from_pretrained, see Loading Models for details.

The input parameters for JoyAIImagePipeline inference include:

• prompt: Text prompt describing the desired image editing effect.
• negative_prompt: Negative prompt specifying what should not appear in the result, defaults to empty string.
• cfg_scale: Classifier-free guidance scale factor, defaults to 5.0. Higher values make the output more closely follow the prompt.
• edit_image: Image to be edited.
• denoising_strength: Denoising strength controlling how much the input image is repainted, defaults to 1.0.
• height: Height of the output image, defaults to 1024. Must be divisible by 16.
• width: Width of the output image, defaults to 1024. Must be divisible by 16.
• seed: Random seed for reproducibility. Set to None for random seed.
• max_sequence_length: Maximum sequence length for the text encoder, defaults to 4096.
• num_inference_steps: Number of inference steps, defaults to 30. More steps typically yield better quality.
• tiled: Whether to enable tiling for reduced VRAM usage, defaults to False.
• tile_size: Tile size, defaults to (30, 52).
• tile_stride: Tile stride, defaults to (15, 26).
• shift: Shift parameter for the scheduler, controlling the Flow Match scheduling curve, defaults to 4.0.
• progress_bar_cmd: Progress bar display mode, defaults to tqdm.

Model Training

Models in the joyai_image series are trained uniformly via examples/joyai_image/model_training/train.py. The script parameters include:

• General Training Parameters
  • Dataset Configuration
    • --dataset_base_path: Root directory of the dataset.
    • --dataset_metadata_path: Path to the dataset metadata file.
    • --dataset_repeat: Number of dataset repeats per epoch.
    • --dataset_num_workers: Number of processes per DataLoader.
    • --data_file_keys: Field names to load from metadata, typically paths to image or video files, separated by ,.
  • Model Loading Configuration
    • --model_paths: Paths to load models from, in JSON format.
    • --model_id_with_origin_paths: Model IDs with original paths, separated by commas.
    • --extra_inputs: Additional input parameters required by the model Pipeline, separated by ,.
    • --fp8_models: Models to load in FP8 format, currently only supported for models whose parameters are not updated by gradients.
  • Basic Training Configuration
    • --learning_rate: Learning rate.
    • --num_epochs: Number of epochs.
    • --trainable_models: Trainable models, e.g., dit, vae, text_encoder.
    • --find_unused_parameters: Whether unused parameters exist in DDP training.
    • --weight_decay: Weight decay magnitude.
    • --task: Training task, defaults to sft.
  • Output Configuration
    • --output_path: Path to save the model.
    • --remove_prefix_in_ckpt: Remove prefix in the model's state dict.
    • --save_steps: Interval in training steps to save the model.
  • LoRA Configuration
    • --lora_base_model: Which model to add LoRA to.
    • --lora_target_modules: Which layers to add LoRA to.
    • --lora_rank: Rank of LoRA.
    • --lora_checkpoint: Path to LoRA checkpoint.
    • --preset_lora_path: Path to preset LoRA checkpoint for LoRA differential training.
    • --preset_lora_model: Which model to integrate preset LoRA into, e.g., dit.
  • Gradient Configuration
    • --use_gradient_checkpointing: Whether to enable gradient checkpointing.
    • --use_gradient_checkpointing_offload: Whether to offload gradient checkpointing to CPU memory.
    • --gradient_accumulation_steps: Number of gradient accumulation steps.
  • Resolution Configuration
    • --height: Height of the image/video. Leave empty to enable dynamic resolution.
    • --width: Width of the image/video. Leave empty to enable dynamic resolution.
    • --max_pixels: Maximum pixel area, images larger than this will be scaled down during dynamic resolution.
    • --num_frames: Number of frames for video (video generation models only).
• JoyAI-Image Specific Parameters
  • --processor_path: Path to the processor for processing text and image encoder inputs.
  • --initialize_model_on_cpu: Whether to initialize models on CPU. By default, models are initialized on the accelerator device.

modelscope download --dataset DiffSynth-Studio/diffsynth_example_dataset --local_dir ./data/diffsynth_example_dataset

We provide recommended training scripts for each model, please refer to the table in "Model Overview" above. For guidance on writing model training scripts, see Model Training; for more advanced training algorithms, see Training Framework Overview.