README.md

metadata

license: apache-2.0
base_model:
  - Qwen/Qwen-Image-Edit
pipeline_tag: image-to-image
tags:
  - gguf-connector
  - gguf-node
widget:
  - text: >-
      remove all UI text elements from the image. Keep the feeling that the
      characters and scene are in water. Also, remove the green UI elements at
      the bottom
    output:
      url: workflow-demo1.png
  - text: >-
      the anime girl with massive fennec ears is wearing cargo pants while
      sitting on a log in the woods biting into a sandwitch beside a beautiful
      alpine lake
    output:
      url: workflow-demo2.png
  - text: >-
      the anime girl with massive fennec ears is wearing a maid outfit with a
      long black gold leaf pattern dress and a white apron mouth open holding a
      fancy black forest cake with candles on top in the kitchen of an old dark
      Victorian mansion lit by candlelight with a bright window to the foggy
      forest and very expensive stuff everywhere
    output:
      url: workflow-demo3.png

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

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

*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

Prompt

remove all UI text elements from the image. Keep the feeling that the characters and scene are in water. Also, remove the green UI elements at the bottom

Prompt

the anime girl with massive fennec ears is wearing cargo pants while sitting on a log in the woods biting into a sandwitch beside a beautiful alpine lake

Prompt

the anime girl with massive fennec ears is wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open holding a fancy black forest cake with candles on top in the kitchen of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere

• 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

• base model from qwen
• diffusers from huggingface
• comfyui from comfyanonymous
• gguf-node (pypi|repo|pack)
• gguf-connector (pypi)