import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("tenperformer/ddpm-butterfly-finetuned", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]YAML Metadata Warning:The pipeline tag "image-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
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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