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calcuis/
qwen-image-edit-plus-gguf

Name: qwen-image-edit-plus-gguf
Availability: InStock

Qwen/Qwen-Image-Edit-2509

quantized

apache-2.0

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qwen-image-edit-plus-gguf

run it with gguf-connector; simply execute the command below in console/terminal

ggc q8

GGUF file(s) available. Select which one to use:

qwen-image-edit-plus-v2-iq3_s.gguf

qwen-image-edit-plus-v2-iq4_nl.gguf

qwen-image-edit-plus-v2-mxfp4_moe.gguf

Enter your choice (1 to 3): _

opt a gguf file in your current directory to interact with; nothing else

ggc q8 accepts multiple image input (see picture above; two images as input)
as lite lora auto applied, able to generate output with merely 4/8 steps instead of the default 40 steps; save up to 80% loading time

up to 3 pictures plus customize prompt as input (above is 3 images input demo)

though ggc q8 is accepting single image input (see above), you could opt the legacy ggc q7 (see below); similar to image-edit model before

ggc q7

run it with gguf-node via comfyui

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

run it with diffusers

might need the most updated git version for QwenImageEditPlusPipeline, should after this pr; for i quant support, should after this commit; install the updated git version diffusers by:

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

simply replace QwenImageEditPipeline by QwenImageEditPlusPipeline from the qwen-image-edit inference example (see here)

import torch, os
from diffusers import QwenImageTransformer2DModel, GGUFQuantizationConfig, QwenImageEditPlusPipeline
from diffusers.utils import load_image

model_path = "https://huggingface.co/calcuis/qwen-image-edit-plus-gguf/blob/main/qwen-image-edit-plus-v2-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-plus",
    subfolder="transformer"
    )
pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", 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"))

run nunchaku safetensors straight with gguf-connector (experimental feature)

run it with the new q9 connector; simply execute the command below in console/terminal

ggc q9

Safetensors available. Select which one to use:

qwen-image-edit-lite-blackwell-fp4.safetensors

qwen-image-edit-lite-int4.safetensors (for non-blackwell card)

Enter your choice (1 to 2): _

opt a safetensors file in your current directory to interact with; nothing else

note: able to generate output with 4/8 steps (see above); surprisingly fast even with low end device; compatible with safetensors in nunchaku repo (depends on your machine; opt the right one)

run the lite model (experimental) with gguf-connector

ggc q0

GGUF file(s) available. Select which one to use:

qwen-image-edit-lite-iq4_nl.gguf

qwen-image-edit-lite-q4_0.gguf

qwen-image-edit-lite-q4_k_s.gguf

Enter your choice (1 to 3): _

opt a gguf file in your current directory to interact with; nothing else

note: a new lite lora auto applied to q0 and q9; able to generate output with 4/8 steps; and more working layers in these versions, should be more stable than p0 (v2.0) below

for lite v2.0, please use p0 connector (experimental)

ggc p0

GGUF file(s) available. Select which one to use:

qwen-image-edit-lite-v2.0-iq2_s.gguf

qwen-image-edit-lite-v2.0-iq3_s.gguf

qwen-image-edit-lite-v2.0-iq4_nl.gguf

Enter your choice (1 to 3): _

opt a gguf file in your current directory to interact with; nothing else

run the new lite v2.1 (experimental) with gguf-connector

for lite v2.1, please use p9 connector

ggc p9

GGUF file(s) available. Select which one to use:

qwen-image-edit-lite-v2.1-q4_0.gguf

qwen-image-edit-lite-v2.1-mxfp4_moe.gguf

Enter your choice (1 to 2): _

opt a gguf file in your current directory to interact with; nothing else

note: ggc p9 is able to generate picture with 4/8 steps but need a higher guidance (i.e., 3.5); if too many elements involved, you might consider increasing the steps (i.e., 15) for better output

reference

gguf-node (pypi|repo|pack)
gguf-connector (pypi)