mirror of
https://www.modelscope.cn/lightx2v/Qwen-Image-Lightning.git
synced 2026-04-02 21:02:53 +08:00
63 lines
1.9 KiB
Markdown
63 lines
1.9 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen-Image
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pipeline_tag: text-to-image
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tags:
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- Qwen-Image;
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- distillation;
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- LoRA
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library_name: diffusers
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---
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Please refer to [Qwen-Image-Lightning github](https://github.com/ModelTC/Qwen-Image-Lightning/) to learn how to use the models.
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use with diffusers 🧨:
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make sure to install diffusers from `main` (`pip install git+https://github.com/huggingface/diffusers.git`)
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```
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from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
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import torch
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import math
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# From https://github.com/ModelTC/Qwen-Image-Lightning/blob/342260e8f5468d2f24d084ce04f55e101007118b/generate_with_diffusers.py#L82C9-L97C10
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scheduler_config = {
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"base_image_seq_len": 256,
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"base_shift": math.log(3), # We use shift=3 in distillation
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"invert_sigmas": False,
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"max_image_seq_len": 8192,
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"max_shift": math.log(3), # We use shift=3 in distillation
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"num_train_timesteps": 1000,
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"shift": 1.0,
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"shift_terminal": None, # set shift_terminal to None
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"stochastic_sampling": False,
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"time_shift_type": "exponential",
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"use_beta_sigmas": False,
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"use_dynamic_shifting": True,
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"use_exponential_sigmas": False,
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"use_karras_sigmas": False,
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}
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scheduler = FlowMatchEulerDiscreteScheduler.from_config(scheduler_config)
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pipe = DiffusionPipeline.from_pretrained(
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"Qwen/Qwen-Image", scheduler=scheduler, torch_dtype=torch.bfloat16
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).to("cuda")
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pipe.load_lora_weights(
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"lightx2v/Qwen-Image-Lightning", weight_name="Qwen-Image-Lightning-8steps-V1.0.safetensors"
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)
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prompt = "a tiny astronaut hatching from an egg on the moon, Ultra HD, 4K, cinematic composition."
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negative_prompt = " "
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=1024,
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height=1024,
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num_inference_steps=8,
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true_cfg_scale=1.0,
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generator=torch.manual_seed(0),
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).images[0]
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image.save("qwen_fewsteps.png")
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``` |