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Create app.py
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app.py
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import torch
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from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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import gradio as gr
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base = "stabilityai/stable-diffusion-xl-base-1.0"
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repo = "ByteDance/SDXL-Lightning"
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ckpt = "sdxl_lightning_1step_unet_x0.safetensors" # Use the correct ckpt for your step setting!
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# Load model.
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def generate():
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unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
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unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
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pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
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# Ensure sampler uses "trailing" timesteps and "sample" prediction type.
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pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", prediction_type="sample")
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# Ensure using the same inference steps as the loaded model and CFG set to 0.
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def greet(prompt):
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image = pipe(prompt, num_inference_steps=1, guidance_scale=0).images[0].save("output.png")
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return image
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demo = gr.Interface(fn=greet, inputs="text", outputs="image")
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demo.launch()
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