Text-to-Image
Diffusers
Safetensors
English
Krea2Pipeline
image-generation
krea2
bitsandbytes
8-bit precision
Instructions to use OzzyGT/Krea_2_Turbo_bnb_nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use OzzyGT/Krea_2_Turbo_bnb_nf4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("OzzyGT/Krea_2_Turbo_bnb_nf4", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Krea 2 Turbo bitsandbytes NF4
Left: original bf16 路 Right: this NF4 model (same prompt and seed).
This is an NF4 (4-bit NormalFloat) quantized version of krea/Krea-2-Turbo using bitsandbytes.
Note: You need bitsandbytes installed
Usage
You can find ready-to-use scripts in the diffusers-recipes repository.
Sample image
The quantized (right) image above was generated with the following prompt and settings (seed 7):
import torch
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("OzzyGT/Krea_2_Turbo_bnb_nf4", torch_dtype=torch.bfloat16)
pipe.to("cuda")
prompt = (
"A cozy corner bookstore-cafe on a rainy evening, cinematic wide shot. "
'A large hand-lettered chalkboard sign in the window reads "FRESH COFFEE & OLD BOOKS" '
"and below it in smaller chalk letters \"open 'til late\". "
"Warm golden light spills onto wet cobblestones that mirror pink and blue neon reflections. "
"Inside, tall mahogany shelves are packed with hundreds of colorful book spines with tiny legible titles, "
"a barista in a striped apron pours delicate latte art, steam curling upward, "
"a tabby cat sleeps on a windowsill beside a stack of paperbacks. "
"Intricate detail, sharp focus, shallow depth of field, photorealistic, rich color grading."
)
image = pipe(
prompt,
num_inference_steps=8,
guidance_scale=0.0,
height=1024,
width=1024,
generator=torch.Generator("cuda").manual_seed(7),
).images[0]
image.save("sample.png")
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