Instructions to use ezlee258258/Inversion-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ezlee258258/Inversion-DPO with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ezlee258258/Inversion-DPO", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
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README.md
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@@ -31,15 +31,13 @@ import torch
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# Load the fine-tuned UNet
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unet = UNet2DConditionModel.from_pretrained(
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"ezlee258258/Inversion-DPO",
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subfolder="unet"
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torch_dtype=torch.float16
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)
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# Load the pipeline with the fine-tuned UNet
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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unet=unet
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torch_dtype=torch.float16
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)
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pipe = pipe.to("cuda")
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# Load the fine-tuned UNet
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unet = UNet2DConditionModel.from_pretrained(
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"ezlee258258/Inversion-DPO",
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subfolder="unet"
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)
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# Load the pipeline with the fine-tuned UNet
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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unet=unet
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)
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pipe = pipe.to("cuda")
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