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import torch | |
import requests | |
from PIL import Image | |
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler | |
# Load the pipeline | |
pipeline = DiffusionPipeline.from_pretrained( | |
"sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline", | |
torch_dtype=torch.float16 | |
) | |
# Feel free to tune the scheduler! | |
# `timestep_spacing` parameter is not supported in older versions of `diffusers` | |
# so there may be performance degradations | |
# We recommend using `diffusers==0.20.2` | |
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config( | |
pipeline.scheduler.config, timestep_spacing='trailing' | |
) | |
pipeline.to('cuda:0') | |
# Download an example image. | |
cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw) | |
# Run the pipeline! | |
result = pipeline(cond, num_inference_steps=75).images[0] | |
# for general real and synthetic images of general objects | |
# usually it is enough to have around 28 inference steps | |
# for images with delicate details like faces (real or anime) | |
# you may need 75-100 steps for the details to construct | |
result.show() | |
result.save("output.png") | |