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
| 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 | |
| ``` |