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", 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
File size: 503 Bytes
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"_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"
]
}
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