Instructions to use randomani/DDIM-tattoo-32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use randomani/DDIM-tattoo-32 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("randomani/DDIM-tattoo-32", 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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```python
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# !pip install diffusers
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from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
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model_id = "google/
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# load model and scheduler
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ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
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# run pipeline in inference (sample random noise and denoise)
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```python
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# !pip install diffusers
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from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
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model_id = "google/DDIM-tattoo-32"
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# load model and scheduler
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ddpm = DDPMPipeline.from_pretrained(model_id) # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
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# run pipeline in inference (sample random noise and denoise)
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