Sign up

calcuis/qwen-image-edit-gguf

🤗 On Hugging Faceimage-to-imageapache-2.0381 GBGGUF✓ HF checksums availableupdated today
Magnet

qwen-image-edit-gguf

  • use 8-step (lite-lora auto applied); save up to 70% loading time
  • run it with gguf-connector; simply execute the command below in console/terminal
ggc q6
GGUF file(s) available. Select which one to use:
1. qwen-image-edit-iq4_nl.gguf
2. qwen-image-edit-q2_k.gguf
3. qwen-image-edit-q4_0.gguf
4. qwen-image-edit-q8_0.gguf
Enter your choice (1 to 4): _
  • opt a gguf file in your current directory to interact with; nothing else

!screenshot

run it with gguf-node via comfyui

  • drag qwen-image-edit to > ./ComfyUI/models/diffusion_models
  • *anyone below, drag it to > ./ComfyUI/models/text_encoders
  • option 1: just qwen2.5-vl-7b-edit [7.95GB]
  • option 2: both qwen2.5-vl-7b [4.43GB] and mmproj-clip [608MB]
  • option 3: just qwen2.5-vl-7b-test [5.03GB]
  • drag pig [254MB] to > ./ComfyUI/models/vae

!screenshot

*note: option 1 (pig quant) is an all-in-one choice; for option 2 (llama.cpp quant), you need to prepare both text-model and mmproj-clip; option 3 (llama.cpp quant) is an experimental attempt, a merge (text+mmproj), similar to option 1, an all-in-one choice also but pig x llama.cpp crossover

!screenshot

  • get more gguf encoder either here (pig quant) or here (llama.cpp quant)

run it with diffusers

  • might need the most updated diffusers; for i quant support, should after this commit; install the updated git version diffusers by:
pip install git+https://github.com/huggingface/diffusers.git
  • see example inference below (edit it if needed):
import torch, os
from diffusers import QwenImageTransformer2DModel, GGUFQuantizationConfig, QwenImageEditPipeline
from diffusers.utils import load_image

model_path = "https://huggingface.co/calcuis/qwen-image-edit-gguf/blob/main/qwen-image-edit-iq4_nl.gguf"

transformer = QwenImageTransformer2DModel.from_single_file(
    model_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
    config="callgg/image-edit-decoder",
    subfolder="transformer"
    )
pipeline = QwenImageEditPipeline.from_pretrained("callgg/image-edit-decoder", transformer=transformer, torch_dtype=torch.bfloat16)
print("pipeline loaded")
pipeline.enable_model_cpu_offload()
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
prompt = "Add a hat to the cat"
inputs = {
    "image": image,
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 2.5,
    "negative_prompt": " ",
    "num_inference_steps": 20,
}
with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
    output_image.save("output.png")
    print("image saved at", os.path.abspath("output.png"))

reference