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DDPM Butterfly Fine-Tuned Model 🦋
Model Description
This model is a fine-tuned version of:
google/ddpm-celebahq-256
trained using the Hugging Face Diffusers library.
The model was adapted using the Smithsonian Butterflies dataset to generate butterfly-style images using a Denoising Diffusion Probabilistic Model (DDPM).
Training Details
Base Model:
- google/ddpm-celebahq-256
Dataset:
- huggan/smithsonian_butterflies_subset
Resolution:
- 256x256
Training Method:
- Full U-Net fine tuning
Epochs:
- 15
Batch Size:
- 4
Learning Rate:
- 1e-05
Optimizer:
- AdamW
Mixed Precision:
- FP16
Features
✅ Fine tuned diffusion model
✅ Supports Hugging Face Diffusers pipeline
✅ Faster sampling using DDIM scheduler
✅ Checkpoint based training
✅ Gradient checkpointing enabled
Usage
from diffusers import DDPMPipeline
pipe = DDPMPipeline.from_pretrained(
"tenperformer/ddpm-butterfly-finetuned"
)
image = pipe(
num_inference_steps=40
).images[0]
image.show()
Training Notes
The model was trained with:
Gradient accumulation
Mixed precision training
Periodic checkpoints
Hugging Face Hub backup
Limitations
This is an experimental fine-tuned DDPM model.
Outputs may contain artifacts due to:
Small dataset size
Limited training duration
DDPM sampling randomness
License
MIT
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