Instructions to use Pie31415/dm_anime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Pie31415/dm_anime with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Pie31415/dm_anime", torch_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
File size: 560 Bytes
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license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
datasets:
- huggan/selfie2anime
---
# This model is a fine-tuned diffusion model for unconditional image generation of animefaces.
Even after fine-tuning the diffusion model for 10 epochs the generated images are still cursed... 💀. Maybe more epochs would help?

## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Pie31415/dm_anime')
image = pipeline().images[0]
image
``` |