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
metadata
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.
Code: 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 for more examples