Qwen-Image-Distill-Full

Qwen-Image Full Distillation Accelerated Model

Model Introduction

This model is a distilled and accelerated version of Qwen-Image.
The original model requires 40 inference steps and uses classifier-free guidance (CFG), resulting in a total of 80 forward passes.
The distilled accelerated model only requires 15 inference steps and does not need CFG, resulting in only 15 forward passes — achieving about 5× speed-up.
Of course, the number of inference steps can be further reduced if needed, but generation quality may decrease.

The training framework is built using DiffSynth-Studio.
The training dataset consists of 16,000 images generated by the original model using randomly sampled prompts from DiffusionDB.
Training was conducted for about 1 day on 8 × MI308X GPUs.

Performance Comparison

Inference Code

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

from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
import torch


pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Distill-Full", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    tokenizer_config=ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="tokenizer/"),
)
prompt = "Delicate portrait, underwater girl, flowing blue dress, hair floating, clear light and shadows, bubbles surrounding, serene face, exquisite details, dreamy and beautiful."
image = pipe(prompt, seed=0, num_inference_steps=15, cfg_scale=1)
image.save("image.jpg")

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license: apache-2.0