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Disty0/Chroma1-HD-SDNQ-uint4-svd-r32
Chroma1-HD-SDNQ-uint4-svd-r32 is a text-to-image model from Disty0. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as apache-2.0.
4 bit (UINT4 with SVD rank 32) quantization of lodestones/Chroma1-HD using SDNQ.
Downloads · 30 days
77
5% of all-time downloads
All-time downloads
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4.9B
11.9 GB on disk
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.safetensors11.9 GB · 100%
How the weights are stored.
U84.3B · 89%
From the Hugging Face model README
4 bit (UINT4 with SVD rank 32) quantization of lodestones/Chroma1-HD using SDNQ.
Usage:
pip install git+https://github.com/Disty0/sdnq
import torch
import diffusers
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.ChromaPipeline.from_pretrained("Disty0/Chroma1-HD-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
prompt = [
"A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
]
negative_prompt = ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
generator=torch.Generator("cpu").manual_seed(433),
num_inference_steps=40,
guidance_scale=3.0,
num_images_per_prompt=1,
).images[0]
image.save("chroma-1-hd-sdnq-uint4-svd-r32.png")
Original BF16 vs SDNQ quantization comparison:
| Quantization | Model Size | Visualization |
|---|---|---|
| Original BF16 | 17.8 GB | ![]() |
| SDNQ UINT4 | 5.4 GB | ![]() |