Instructions to use custom-diffusion-library/cat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use custom-diffusion-library/cat with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("custom-diffusion-library/cat", 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
Update README.md
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README.md
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@@ -29,7 +29,7 @@ model_id = "CompVis/stable-diffusion-v1-4"
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe = pipe.to(device)
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diffuser_training.load_model(pipe.text_encoder, pipe.tokenizer, pipe.unet, '
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prompt = "<new1> cat swimming in a pool"
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images = pipe(prompt, num_inference_steps=200, guidance_scale=6., eta=1.).images
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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pipe = pipe.to(device)
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diffuser_training.load_model(pipe.text_encoder, pipe.tokenizer, pipe.unet, 'cat.bin')
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prompt = "<new1> cat swimming in a pool"
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images = pipe(prompt, num_inference_steps=200, guidance_scale=6., eta=1.).images
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