Instructions to use stillerman/poke-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stillerman/poke-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("stillerman/poke-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| from ...utils import ( | |
| OptionalDependencyNotAvailable, | |
| is_torch_available, | |
| is_transformers_available, | |
| is_transformers_version, | |
| ) | |
| try: | |
| if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")): | |
| raise OptionalDependencyNotAvailable() | |
| except OptionalDependencyNotAvailable: | |
| from ...utils.dummy_torch_and_transformers_objects import ( | |
| VersatileDiffusionDualGuidedPipeline, | |
| VersatileDiffusionImageVariationPipeline, | |
| VersatileDiffusionPipeline, | |
| VersatileDiffusionTextToImagePipeline, | |
| ) | |
| else: | |
| from .modeling_text_unet import UNetFlatConditionModel | |
| from .pipeline_versatile_diffusion import VersatileDiffusionPipeline | |
| from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline | |
| from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline | |
| from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline | |