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--- |
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language: en |
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license: apache-2.0 |
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library_name: diffusers |
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tags: [] |
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datasets: imagefolder |
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metrics: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the training script had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ddpm-vangogh-128 |
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## Model description |
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This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library |
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on the `imagefolder` dataset. |
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## Intended uses & limitations |
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#### How to use |
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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 = "uumlaut/ddpm-vangogh-128" |
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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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``` |
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#### Limitations and bias |
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[TODO: provide examples of latent issues and potential remediations] |
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## Training data |
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[TODO: describe the data used to train the model] |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- gradient_accumulation_steps: 1 |
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- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None |
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- lr_scheduler: None |
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- lr_warmup_steps: 500 |
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- ema_inv_gamma: None |
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- ema_inv_gamma: None |
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- ema_inv_gamma: None |
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- mixed_precision: fp16 |
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### Training results |
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📈 [TensorBoard logs](https://huggingface.co/uumlaut/ddpm-vangogh-128/tensorboard?#scalars) |
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