Instructions to use jacklishufan/diffusion-kto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jacklishufan/diffusion-kto with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jacklishufan/diffusion-kto", 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
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by akashgokul - opened
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This model is fine-tuned from
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### Usage
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```
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import torch
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from diffusers import AutoencoderKL, UNet2DConditionModel, DiffusionPipeline
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vae_path = model_name = "runwayml/stable-diffusion-v1-5"
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</p>
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This model is fine-tuned from Stable Diffusion v1-5 on Pick-a-Pic v2 dataset using KTO.
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### Usage
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```python
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import torch
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from diffusers import AutoencoderKL, UNet2DConditionModel, DiffusionPipeline
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vae_path = model_name = "runwayml/stable-diffusion-v1-5"
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