Instructions to use unsloth/Krea-2-Turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use unsloth/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("unsloth/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: 791 Bytes
f5033c5 | 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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | {
"_class_name": "AutoencoderKLQwenImage",
"_diffusers_version": "0.39.0.dev0",
"_name_or_path": "Qwen/Qwen-Image",
"attn_scales": [],
"base_dim": 96,
"dim_mult": [
1,
2,
4,
4
],
"dropout": 0.0,
"input_channels": 3,
"latents_mean": [
-0.7571,
-0.7089,
-0.9113,
0.1075,
-0.1745,
0.9653,
-0.1517,
1.5508,
0.4134,
-0.0715,
0.5517,
-0.3632,
-0.1922,
-0.9497,
0.2503,
-0.2921
],
"latents_std": [
2.8184,
1.4541,
2.3275,
2.6558,
1.2196,
1.7708,
2.6052,
2.0743,
3.2687,
2.1526,
2.8652,
1.5579,
1.6382,
1.1253,
2.8251,
1.916
],
"num_res_blocks": 2,
"temperal_downsample": [
false,
true,
true
],
"z_dim": 16
}
|