Create SDXL_Lighting.py

SDXL_Lighting.py

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+import torch
+from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, EulerDiscreteScheduler
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+base = "stabilityai/stable-diffusion-xl-base-1.0"
+repo = "ByteDance/SDXL-Lightning"
+ckpt = "sdxl_lightning_4step_unet.safetensors" # Use the correct ckpt for your step setting!
+
+## download
+# git clone https://hf-mirror.com/stabilityai/stable-diffusion-xl-base-1.0
+# wget -c hf-mirror.com/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors
+# wget -c https://hf-mirror.com/ByteDance/SDXL-Lightning/sdxl_lightning_4step_unet.safetensors
+# base = xxx/stable-diffusion-xl-base-1.0
+# ckpt = SDXL-Lightning/sdxl_lightning_4step_unet.safetensors
+
+# Load model.
+# unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
+# unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device="cuda"))
+# pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+
+unet = UNet2DConditionModel.from_config(base, subfolder="unet").to("cuda", torch.float16)
+unet.load_state_dict(load_file(ckpt), device="cuda"))
+pipe = StableDiffusionXLPipeline.from_pretrained(base, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda")
+
+# Ensure sampler uses "trailing" timesteps.
+pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
+
+# Ensure using the same inference steps as the loaded model and CFG set to 0.
+pipe("A girl smiling", num_inference_steps=4, guidance_scale=0).images[0].save("output.png")