Instructions to use triton7777/ddpm-butterflies-128 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use triton7777/ddpm-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("triton7777/ddpm-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
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Download README.md from triton7777/ddpm-butterflies-128: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/triton7777/ddpm-butterflies-128/resolve/main/README.md
- Command line
-
hf download hf://triton7777/ddpm-butterflies-128/README.md
-
curl -L -o README.md https://huggingface.co/triton7777/ddpm-butterflies-128/resolve/main/README.md
1.37 kB
metadata
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
ddpm-butterflies-128
Model description
This diffusion model is trained with the 🤗 Diffusers library
on the huggan/smithsonian_butterflies_subset dataset.
Intended uses & limitations
How to use
# TODO: add an example code snippet for running this diffusion pipeline
Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
Training data
[TODO: describe the data used to train the model]
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16