--- license: apache-2.0 language: - en - zh base_model: - Qwen/Qwen-Image pipeline_tag: text-to-image tags: - Qwen-Image - distillation - LoRA - lora library_name: diffusers --- Please refer to [Qwen-Image-Lightning github](https://github.com/ModelTC/Qwen-Image-Lightning/) to learn how to use the models. use with diffusers 🧨: make sure to install diffusers from `main` (`pip install git+https://github.com/huggingface/diffusers.git`) ``` from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler import torch import math # From https://github.com/ModelTC/Qwen-Image-Lightning/blob/342260e8f5468d2f24d084ce04f55e101007118b/generate_with_diffusers.py#L82C9-L97C10 scheduler_config = { "base_image_seq_len": 256, "base_shift": math.log(3), # We use shift=3 in distillation "invert_sigmas": False, "max_image_seq_len": 8192, "max_shift": math.log(3), # We use shift=3 in distillation "num_train_timesteps": 1000, "shift": 1.0, "shift_terminal": None, # set shift_terminal to None "stochastic_sampling": False, "time_shift_type": "exponential", "use_beta_sigmas": False, "use_dynamic_shifting": True, "use_exponential_sigmas": False, "use_karras_sigmas": False, } scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config) pipe = DiffusionPipeline.from_pretrained( "Qwen/Qwen-Image", scheduler=scheduler, torch_dtype=torch.bfloat16 ).to("cuda") pipe.load_lora_weights( "lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors" ) prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition." negative_prompt = " " image = pipe( prompt=prompt, negative_prompt=negative_prompt, width=1024, height=1024, num_inference_steps=8, true_cfg_scale=1.0, generator=torch.manual_seed(0), ).images[0] image.save("qwen_fewsteps.png") ```
