Instructions to use Jinstudio/Krea-2-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jinstudio/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("Jinstudio/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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 586 Bytes
bf152ee | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | {
"_class_name": "Krea2Pipeline",
"_diffusers_version": "0.39.0.dev0",
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Qwen3VLModel"
],
"text_encoder_select_layers": [
2,
5,
8,
11,
14,
17,
20,
23,
26,
29,
32,
35
],
"tokenizer": [
"transformers",
"Qwen2Tokenizer"
],
"transformer": [
"diffusers",
"Krea2Transformer2DModel"
],
"vae": [
"diffusers",
"AutoencoderKLQwenImage"
],
"is_distilled": true,
"patch_size": 2
} |