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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! | |
# 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") | |
# Ensure sampler uses "trailing" timesteps. | |
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing") | |