Instructions to use dboshardy/ddim-butterflies-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dboshardy/ddim-butterflies-128 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dboshardy/ddim-butterflies-128", 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 model card README.md
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README.md
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@@ -36,13 +36,13 @@ on the `huggan/smithsonian_butterflies_subset` dataset.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.
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- train_batch_size: 32
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- eval_batch_size:
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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:
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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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### 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: 32
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- eval_batch_size: 32
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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: 250
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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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