Instructions to use dg845/DiffusionGemma-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dg845/DiffusionGemma-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dg845/DiffusionGemma-diffusers", 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
| library_name: diffusers | |
| `diffusers`-native checkpoint for the [google/diffusiongemma-26B-A4B-it](https://huggingface.co/google/diffusiongemma-26B-A4B-it) discrete diffusion LLM. | |
| You can use the model as follows. Note that you need `transformers>=5.12.0` for the underlying `DiffusionGemmaForBlockDiffusion` model. | |
| ```python | |
| import torch | |
| from diffusers import DiffusionGemmaPipeline | |
| pipe = DiffusionGemmaPipeline.from_pretrained( | |
| "dg845/DiffusionGemma-diffusers", | |
| torch_dtype=torch.bfloat16, | |
| ) | |
| pipe.to("cuda") | |
| # Compile the decoder model for faster inference | |
| pipe.model.model.decoder = torch.compile(pipe.model.model.decoder, mode="reduce-overhead"), | |
| output = pipe( | |
| prompt="Why is the sky blue?", | |
| gen_length=256, | |
| num_inference_steps=48, | |
| cache_implementation="static", | |
| generator=torch.Generator("cuda").manual_seed(42), | |
| ) | |
| print(output.texts[0]) | |
| ``` |