Instructions to use bardofcodes/pattern_analogies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bardofcodes/pattern_analogies with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bardofcodes/pattern_analogies", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| { | |
| "_class_name": "PatternAnalogyTrifuser", | |
| "_diffusers_version": "0.32.0.dev0", | |
| "analogy_encoder": [ | |
| "analogy_encoder", | |
| "AnalogyEncoder" | |
| ], | |
| "analogy_input_processor": [ | |
| "analogy_input_processor", | |
| "AnalogyInputProcessor" | |
| ], | |
| "analogy_projector": [ | |
| "analogy_projector", | |
| "AnalogyProjector" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "DDIMScheduler" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |