calcuis / qi-gguf

gguf quantized version of qwen-image

• if ready, could try to test it with:

import torch
from diffusers import DiffusionPipeline, GGUFQuantizationConfig, QwenImageTransformer2DModel

model_path = "https://huggingface.co/calcuis/qi-gguf/blob/main/qwen-image-q2_k.gguf"
transformer = QwenImageTransformer2DModel.from_single_file(
    model_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
    config="callgg/qi-decoder",
    subfolder="transformer"
)

pipe = DiffusionPipeline.from_pretrained(
    "callgg/qi-decoder",
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)
pipe.enable_model_cpu_offload()

prompt = "a pig holding a sign that says hello world"
positive_magic = {"en": "Ultra HD, 4K, cinematic composition."}
negative_prompt = " "

image = pipe(
    prompt=prompt + positive_magic["en"],
    negative_prompt=negative_prompt,
    height=1024,
    width=1024,
    num_inference_steps=24,
    true_cfg_scale=2.5,
    generator=torch.Generator().manual_seed(0)
).images[0]
image.save("output.png")