SDNQ
Collection
Models quantized with SDNQ
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15 items
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Updated
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1
4 bit (UINT4 with SVD rank 32) quantization of vladmandic/Qwen-Lightning-Edit 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.QwenImageEditPipeline.from_pretrained("Disty0/Qwen-Image-Edit-Lightning-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
image1 = Image.open("input1.png")
image2 = Image.open("input2.png")
prompt = "The magician bear is on the left, the alchemist bear is on the right, facing each other in the central park square."
inputs = {
"image": [image1, image2],
"prompt": prompt,
"generator": torch.manual_seed(0),
"true_cfg_scale": 1.0,
"negative_prompt": " ",
"num_inference_steps": 8,
"guidance_scale": 1.0,
"num_images_per_prompt": 1,
}
with torch.inference_mode():
output = pipeline(**inputs)
output_image = output.images[0]
output_image.save("qwen-image-edit-lightning-sdnq-uint4-svd-r32.png")