OzzyGT/Krea_2_Turbo_sdnq_dynamic_4bit

Krea 2 Turbo SDNQ Dynamic INT4

Left: original bf16  ·  Right: this SDNQ int4 model (same prompt and seed).

This is an int4 quantized version of krea/Krea-2-Turbo using SDNQ (SD.Next Quantization) with the dynamic option and Hadamard Rotation.

Note: You need SDNQ v0.2.0 or v0.2.2 and above (v0.2.1 is incompatible)

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 sdnq  # register the SDNQ backend before loading
import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("OzzyGT/Krea_2_Turbo_sdnq_dynamic_4bit", 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")