Unconditional Image Generation
Diffusers
diffusion
tiny
pokemon
U-Net
from_scratch
9m
pokepixels
pixels
diff
Instructions to use AxionLab-Co/PokePixels1-9M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AxionLab-Co/PokePixels1-9M with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AxionLab-Co/PokePixels1-9M", 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
- Xet hash:
- 6327f348114799c540c4a3cef027d79d3b160230989ab3c750cb6f58a465f48d
- Size of remote file:
- 105 MB
- SHA256:
- 7096d634a094803d3685ed0c19afd657ae84ec57558e9db1045688d8f9cd1db9
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