Instructions to use muneebable/class-conditional-diffusion-cub-200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use muneebable/class-conditional-diffusion-cub-200 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("muneebable/class-conditional-diffusion-cub-200", 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
- Draw Things
- DiffusionBee
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- diffusers
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- unconditional-image-generation
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- diffusion-models-class
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# class-conditional-diffusion-cub-200
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- diffusers
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- unconditional-image-generation
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- diffusion-models-class
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datasets:
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- dpdl-benchmark/caltech_birds2011
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library_name: diffusers
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# class-conditional-diffusion-cub-200
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