import torch

from diffusers import DiffusionPipeline, GGUFQuantizationConfig, QwenImageTransformer2DModel


torch_dtype = torch.bfloat16
model_id = "Qwen/Qwen-Image"

transformer = QwenImageTransformer2DModel.from_single_file(
    "https://huggingface.co/OzzyGT/qwen-image-lighting-gguf/blob/main/qwen-image-lighting-Q4_K_S.gguf",
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch_dtype),
    torch_dtype=torch_dtype,
    config="Qwen/Qwen-Image",
    subfolder="transformer",
)
pipe = DiffusionPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch_dtype)
pipe.enable_model_cpu_offload()
prompt = "stock photo of two people, a man and a woman, wearing lab coats writing on a white board with markers, the white board has text that reads 'The Diffusers library by Hugging Face makes it easy for developers to run image generation and inference using state-of-the-art diffusion models with just a few lines of code' with sloppy writing and traces clearly made by a human. The photo is taken from the side and has depth of field so some parts of the board looks blurred giving it a more professional look"

generator = torch.Generator(device="cuda").manual_seed(42)

image = pipe(
    prompt=prompt,
    negative_prompt="",
    width=1664,
    height=928,
    num_inference_steps=8,
    true_cfg_scale=1.0,
    generator=generator,
).images[0]

image.save("gguf_lighting_qwen.png")

Result

How to test (16GB GPU)

Install diffusers from main:

pip install git+https://github.com/huggingface/diffusers

python

import torch
from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
from transformers import Qwen2_5_VLForConditionalGeneration

from diffusers import DiffusionPipeline, GGUFQuantizationConfig, QwenImageTransformer2DModel


torch_dtype = torch.bfloat16
model_id = "Qwen/Qwen-Image"

transformer = QwenImageTransformer2DModel.from_single_file(
    "https://huggingface.co/OzzyGT/qwen-image-lighting-gguf/blob/main/qwen-image-lighting-Q4_K_S.gguf",
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch_dtype),
    torch_dtype=torch_dtype,
    config="Qwen/Qwen-Image",
    subfolder="transformer",
)

quantization_config = TransformersBitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)

text_encoder = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id,
    subfolder="text_encoder",
    quantization_config=quantization_config,
    torch_dtype=torch_dtype,
)
text_encoder = text_encoder.to("cpu")

pipe = DiffusionPipeline.from_pretrained(
    model_id, transformer=transformer, text_encoder=text_encoder, torch_dtype=torch_dtype
)
pipe.enable_model_cpu_offload()
prompt = "stock photo of two people, a man and a woman, wearing lab coats writing on a white board with markers, the white board has text that reads 'The Diffusers library by Hugging Face makes it easy for developers to run image generation and inference using state-of-the-art diffusion models with just a few lines of code' with sloppy writing and traces clearly made by a human. The photo is taken from the side and has depth of field so some parts of the board looks blurred giving it a more professional look"

generator = torch.Generator(device="cuda").manual_seed(42)

image = pipe(
    prompt=prompt,
    negative_prompt="",
    width=1664,
    height=928,
    num_inference_steps=8,
    true_cfg_scale=1.0,
    generator=generator,
).images[0]

image.save("gguf_lighting_qwen.png")