Instructions to use krea/Krea-2-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use krea/Krea-2-Turbo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Turbo", dtype=torch.bfloat16, device_map="cuda") prompt = "A small, dark-colored cat is captured mid-stride, walking down the center of a narrow, abandoned street. The street is paved and appears cracked and worn. On either side of the street are tall, dilapidated buildings with visible brickwork and windows. A street lamp stands on the right side. The entire image is rendered in a monochromatic blue, with a distinct halftone dot pattern overlaying the scene, giving it a retro or printed appearance. The focus is soft, and the lighting is diffused, creating a hazy, atmospheric effect. The perspective is from ground level, looking down the length of the street, which narrows into the distance., halftone texture" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Update README.md
#19
by Anon252008 - opened
add a rebalancer parameter to sglang diffusion and support it in the /v1/images/generations endpoint. This should allow me to pass in a post-prompt rebalancing / conditioning parameter like "1, 1, 1, 1, 1, 1, 1, 2.5, 5.0, 1.1, 4.0, 1.0" with a multiplier of "2" for the krea-2-turbo image model located at {INSERT PATH TO MODEL HERE}