Instructions to use hotshotco/Hotshot-XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hotshotco/Hotshot-XL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hotshotco/Hotshot-XL", 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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README.md
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# Model Description
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- **Developed by**: Natural Synthetics Inc.
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- **Model type**: Diffusion-based text-to-GIF generative model
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- **License**: CreativeML Open RAIL++-M License
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- **Model Description**: This is a model that can be used to generate and modify GIFs based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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- **Resources for more information**: Check out our [GitHub Repository](https://github.com/hotshotco/hotshot-xl).
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# Model Description
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- **Developed by**: Natural Synthetics Inc.
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- **Model type**: Diffusion-based text-to-GIF generative model
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- **License**: [CreativeML Open RAIL++-M License](https://huggingface.co/hotshotco/Hotshot-XL/raw/main/LICENSE.md)
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- **Model Description**: This is a model that can be used to generate and modify GIFs based on text prompts. It is a Latent Diffusion Model that uses two fixed, pretrained text encoders (OpenCLIP-ViT/G and CLIP-ViT/L).
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- **Resources for more information**: Check out our [GitHub Repository](https://github.com/hotshotco/hotshot-xl).
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