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

ERNIE-Image

ERNIE-Image is a powerful image generation model with 8B parameters developed by Baidu, featuring a compact and efficient architecture with strong instruction-following capability. Based on an 8B DiT backbone, it delivers performance comparable to larger (20B+) models in certain scenarios while maintaining parameter efficiency. It offers reliable performance in instruction understanding and execution, text generation (English/Chinese/Japanese), and overall stability.

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 baidu/ERNIE-Image model for inference. VRAM management is enabled, the framework automatically controls parameter loading based on available VRAM, requiring a minimum of 3G VRAM.

from diffsynth.pipelines.ernie_image import ErnieImagePipeline, ModelConfig
import torch

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 = ErnieImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device='cuda',
    model_configs=[
        ModelConfig(model_id="baidu/ERNIE-Image", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors", **vram_config),
        ModelConfig(model_id="baidu/ERNIE-Image", origin_file_pattern="text_encoder/model.safetensors", **vram_config),
        ModelConfig(model_id="baidu/ERNIE-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors", **vram_config),
    ],
    tokenizer_config=ModelConfig(model_id="baidu/ERNIE-Image", origin_file_pattern="tokenizer/"),
    vram_limit=torch.cuda.mem_get_info("cuda")[1] / (1024 ** 3) - 0.5,
)

image = pipe(
    prompt="一只黑白相间的中华田园犬",
    negative_prompt="",
    height=1024,
    width=1024,
    seed=42,
    num_inference_steps=50,
    cfg_scale=4.0,
)
image.save("output.jpg")

Model Overview

Model ID	Inference	Low VRAM Inference	Full Training	Full Training Validation	LoRA Training	LoRA Training Validation
baidu/ERNIE-Image: T2I	code	code	code	code	code	code

Model Inference

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

The input parameters for ErnieImagePipeline inference include:

• prompt: The prompt describing the content to appear in the image.
• negative_prompt: The negative prompt describing what should not appear in the image, default value is "".
• cfg_scale: Classifier-free guidance parameter, default value is 4.0.
• height: Image height, must be a multiple of 16, default value is 1024.
• width: Image width, must be a multiple of 16, default value is 1024.
• seed: Random seed. Default is None, meaning completely random.
• rand_device: The computing device for generating random Gaussian noise matrices, default is "cuda". When set to cuda, different GPUs will produce different results.
• num_inference_steps: Number of inference steps, default value is 50.

If VRAM is insufficient, please enable VRAM Management. We provide recommended low-VRAM configurations for each model in the "Model Overview" table above.

Model Training

ERNIE-Image series models are trained uniformly via examples/ernie_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, e.g., "baidu/ERNIE-Image:transformer/diffusion_pytorch_model*.safetensors", 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. Leave empty to enable dynamic resolution.
    • --width: Width of the image. Leave empty to enable dynamic resolution.
    • --max_pixels: Maximum pixel area, images larger than this will be scaled down during dynamic resolution.
• ERNIE-Image Specific Parameters
  • --tokenizer_path: Path to the tokenizer, leave empty to auto-download from remote.

We provide an example image dataset for testing, which can be downloaded with the following command:

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.