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--- |
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library_name: diffusers |
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license: apache-2.0 |
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datasets: |
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- Drozdik/tattoo_v0 |
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language: |
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- en |
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tags: |
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- art |
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--- |
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## Model Details |
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**Abstract**: |
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*Trained a Unconditional Diffusion Model on tattoo dataset with DDIM noise scheduler * |
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## Inference |
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**DDPM** models can use *discrete noise schedulers* such as: |
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- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py) |
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- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py) |
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- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) |
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for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. |
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For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead. |
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See the following code: |
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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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image = ddpm().images[0] |
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# save image |
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image.save("ddpm_generated_image.png") |
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``` |
