Instructions to use predictia/cerra_tas_vqvae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use predictia/cerra_tas_vqvae 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("predictia/cerra_tas_vqvae", 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] - Transformers
How to use predictia/cerra_tas_vqvae with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="predictia/cerra_tas_vqvae")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("predictia/cerra_tas_vqvae", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43f2a4027d3940e2f05f0c1e8ca49ee5812e5ca1e9e7b19cfefde92f26f63f81
- Size of remote file:
- 356 kB
- SHA256:
- 8a7897fcb6be7686838731586108d70d66536871ee4f36a8640ec8770351baa4
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