Text-to-Image
Diffusers
Safetensors
PyTorch
StableDiffusionPipeline
unconditional-image-generation
diffusion-models-class
Instructions to use shellypeng/model_am with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use shellypeng/model_am with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("shellypeng/model_am", 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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# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
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- diffusers
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- unconditional-image-generation
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- diffusion-models-class
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pipeline_tag: text-to-image
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inference: true
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# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
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