Instructions to use ownt/pathology-text-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ownt/pathology-text-vae with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ownt/pathology-text-vae", 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
File size: 747 Bytes
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license: apache-2.0
pipeline_tag: any-to-any
---
This repository contains the model described in [OmniFlow: Any-to-Any Generation with Multi-Modal Rectified Flows](https://huggingface.co/papers/2412.01169).
Code: [https://github.com/jacklishufan/OmniFlows](https://github.com/jacklishufan/OmniFlows)
Usage:
```
from omniflow import OmniFlowPipeline
pipeline = OmniFlowPipeline.load_pretrained('ckpts/v0.5',device='cuda')
pipeline.cfg_mode = 'new'
imgs = pipeline("portrait of a cyberpunk girl with neon tattoos and a visor,staring intensely. Standing on top of a building",height=512,width=512,add_token_embed=0,task='t2i')
```
See [Notebook](https://github.com/jacklishufan/OmniFlows/blob/main/scripts/Demo.ipynb) for more examples
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