Qwen Image ComfyUI: Generate and Edit Images

Qwen Image Edit is one of the most capable open-weights image models for instruction-based editing. It combines 20 billion parameters with native ControlNet support, multi-image editing, and excellent text rendering, capabilities previously restricted to closed-source APIs.

Qwen AI was developed by Alibaba Cloud, the group behind the broader Qwen family of language and vision models. This guide covers everything from understanding the model to downloading files, installing in ComfyUI, and getting the best results from your edits.

Qwen Image: An Overview

20B Parameters, Text Rendering, and Editing

Qwen-Image is a 20B parameter MMDiT (Multimodal Diffusion Transformer) model released under the Apache 2.0 license. Its architecture combines two large components: a 20.4B DiT Transformer that constructs the image, and an 8.3B Qwen2.5-VL text encoder that processes the prompt.

Note

Multimodal Diffusion Transformer (MMDiT) is a generative neural network architecture that combines a transformer with a diffusion (denoising) process to process and align multiple data in a single framework.

This dual-encoding mechanism enables the editing module to balance semantic consistency and visual fidelity, which is why the model produces instruction-following edits that larger but simpler architectures struggle with.

Qwen-Image's standout strength is complex text rendering. It generates images with multi-line layouts and paragraph-level content in both Chinese and English, a capability most other open-weight models handle poorly.

Qwen Image Edit vs. Flux Kontext: Key Differences

Qwen Image is significantly larger than Flux at 20.4B vs. 12B parameters. It uses a Qwen2.5-VL vision-language model as its text encoder, giving it strong bilingual understanding that Flux models don't match.

Where Flux Kontext leads on generation speed, Qwen Image Edit is the stronger tool for tasks that require combining multiple reference images, editing text within an image, or applying precise object-level changes that need to be preserved across identity and composition.

What's New in Qwen Image Edit 2509 and 2511?

2509 (September 2025)

Qwen-Image-Edit-2509 introduced multi-image editing support, covering person+person, person+product, and person+scene combinations. It also added native ControlNet support with built-in depth maps, edge maps, and keypoint maps.

Three areas saw major improvements:

• Person editing - better facial identity preservation
• Product editing - identity retention across styles
• Text editing - font type, color, and material changes

2511 (December 2025)

Qwen-Image-Edit-2511 was released on December 26, 2025, building on 2509 with improved multi-image editing consistency, multi-angle view generation from a single reference image (re-shoot from any camera angle), and refined face identity preservation across pose and style transformations. Native LoRA support is built-in, so community LoRAs work without additional calibration nodes.

Qwen-Image-Edit-2511 is now the recommended default for new setups. The ComfyUI workflow structure is nearly identical to 2509, so the main change is the diffusion model file. Download qwen_image_edit_2511_fp8mixed.safetensors for 24GB cards, or qwen_image_edit_2511_bf16.safetensors for full precision, and pair it with the matching Qwen-Image-Edit-2511 Lightning LoRA for 4-step generation. The text encoder and VAE are shared across versions.

Qwen-Image VRAM Requirements: What GPU Do You Need?

VRAM Requirements by Precision

Running Qwen Image Edit on 8GB, 16GB, and 24GB GPUs

On 8GB, you are limited to Q2_K or Q3_K_S quantizations with the Lightning LoRA enabled. Results are usable but noticeably softer than higher-precision variants.

At 16GB, FP8 is the sweet spot: near-full quality without GGUF quantization. The unquantized model needs 24GB or more (such as an RTX 4090) along with 64GB of system RAM for optimal performance.

Run Qwen Image Edit in the Cloud Without a Local GPU

Renting a GPU by the minute is the fastest way to run Qwen Image Edit when your own card falls short of the VRAM in the table above. Instead of trading quality away on an 8GB or 12GB card, you can load the full FP8 or BF16 model on a cloud GPU and get clean output on the first run. You pay only for the minutes the instance is active, so a short editing session costs a fraction of a dollar.

The GPU you need depends on the precision you want to run. FP8 and GGUF workflows fit comfortably on a 48GB card, while full BF16 precision and heavy multi-image edits are better served by an 80GB GPU. The table below maps each Qwen Image Edit workflow to a recommended instance on Thunder Compute, and our guide to using ComfyUI in the cloud covers the full GPU picker across other workflows.

The fp8 files are a VRAM optimization rather than a speed feature. On an Ampere card such as the A6000, ComfyUI loads the fp8 weights and upcasts them to bf16 for the actual compute, so you get the smaller memory footprint without native fp8 acceleration, which is fine for image editing. The L40 is an Ada card with native fp8 tensor cores, so it runs the fp8 math directly and generates faster if you want the extra speed.

Setup is the other place a cloud instance saves time. Rather than downloading the diffusion model, text encoder, VAE, and LoRA and placing each file in the right folder, you can launch a preconfigured environment with ComfyUI ready to run. That removes the most common setup mistake, files landing in the wrong directory, before you write your first prompt.

Skip the VRAM math and launch Qwen Image Edit on an 80GB A100 for $1.09/hr. See live GPU pricing and availability.

How to Install Qwen Image Edit in ComfyUI

Update ComfyUI to the latest version before downloading anything. Native support for Qwen Image Edit 2511 was added in December 2025, and any build predating that could show red-node errors on load.

Downloading the Model Files

All files are available for free on Hugging Face. Download each file and place it in the directory shown.

For low-VRAM setups, download a GGUF variant instead and place it in ComfyUI/models/unet/. You will also need the ComfyUI-GGUF custom node by city96, installable through ComfyUI Manager.

Directory Structure

The correct directory layout for model files is as follows:

ComfyUI/
└── models/
    ├── text_encoders/
    │   └── qwen_2.5_vl_7b_fp8_scaled.safetensors
    ├── diffusion_models/
    │   └── qwen_image_edit_2511_fp8mixed.safetensors
    ├── unet/
    │   └── Qwen-Image-Edit-2511-Q4_K_M.gguf  (GGUF path)
    ├── loras/
    │   └── Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors  (optional)
    └── vae/
        └── qwen_image_vae.safetensors

Placing files in the wrong directories is the most common setup mistake. After placing all files, do a full ComfyUI restart.

Loading the Official Workflow

The official workflow JSON files are hosted on Comfy-Org GitHub and the ComfyUI examples documentation page. Drag the workflow image or JSON file directly onto the ComfyUI canvas to load it. ComfyUI decodes the embedded metadata and restores the full node graph automatically.

Read next

Prefer a simpler interface? Forge Neo is a traditional web UI alternative to ComfyUI that works with Qwen.

How to Use Qwen Image Edit in ComfyUI

Single-Image Editing: Prompts, Steps, and Settings

Load your source image into the Load Image node, write your edit instruction in the positive prompt field, and add a short negative prompt for unwanted elements such as blurriness or watermarks.

Recommended KSampler settings by variant:

• BF16: CFG 4.0, 40 steps
• FP8: CFG 4.0, 20 steps
• FP8 + Lightning LoRA: CFG 1.0, 4 steps

Using the wrong settings for your variant is the most common cause of blocky or poor-quality output. The Lightning LoRA setting (CFG 1.0, 4 steps) is a different inference regime. Mixing it with standard settings degrades quality significantly.

Practical Editing Use Cases with Example Prompts

Qwen Image Edit handles a wide range of tasks that other open-weight models struggle with. Here are the most reliable use cases with effective prompt patterns.

Changing text in an image: "Replace the text on the sign that reads 'SALE' with 'OPEN'. Keep the font style, color, and size consistent with the original."

Swapping outfit or clothing: "Change the jacket from red to navy blue. Keep the person's face, hair, and background unchanged."

Background replacement: "Replace the background with a modern office environment, maintaining the subject in the foreground with consistent lighting."

Style transfer: "Convert this photo to a Studio Ghibli-style illustration, maintaining the subject's features and pose."

Product poster editing: "Place this product in a flat lay composition on a white marble surface with soft natural lighting and a simple shadow."

Multi-image merge: "The person from image 1 wears the outfit from image 2, placed in the background from image 3. Match the lighting so all elements look photographed together."

The most effective editing prompts are imperative and specific about what to preserve. If you want the subject's face unchanged, say so explicitly. If the background should stay the same, include it. Vague instructions like "make it better" produce inconsistent results.

Multi-Image Editing: Combining Up to Three Input Images

Qwen-Image-Edit-2509 and later versions accept up to three input images. The output aspect ratio defaults to that of the first image. Write prompts that reference each image's role explicitly. If you only need to edit one image, right-click the unused input nodes and select Bypass to disable them.

Inpainting With Qwen Image Edit to Keep the Original Intact

Inpainting is the right tool when an edit must leave the rest of the image untouched. A standard Qwen edit regenerates the entire frame, which can introduce small drift in areas you did not intend to change, most often in the face. Inpainting avoids this by regenerating only a masked region and compositing it back over the original pixels.

To set it up, right-click the Load Image node and open the Mask Editor, then paint over the area you want to change. Paint slightly beyond the target so the edited region blends at its edges. Both the image and the mask feed into the inpaint node, and your prompt describes only the local change, for example "add a silver tiara" or "change the hair color to pink."

The tradeoff is that inpainting holds the pose fixed. Because the workflow reveals only the masked area and keeps everything else identical, edits that move the head or body will not blend cleanly. Use inpainting to add, remove, or recolor elements while the subject stays in place, and use a full edit when the whole composition needs to change.

Using ControlNet with Qwen Image Edit

ControlNet adds structural precision without separate preprocessing tools. The model supports depth maps, edge maps, and keypoint maps as control conditions. You need the Union ControlNet LoRA (qwen_image_union_diffsynth_lora.safetensors), which handles all control types in a single file. In the QwenImageDiffsynthControlnet node, set strength values between 0.5 and 0.8 to preserve generative quality while applying the structural constraint.

Expanding What Qwen Image Edit Can Do

Using Qwen-Image LoRA Models for Style and Realism

The Qwen Image community has built a growing library of LoRAs for style transfer, realism, and character consistency. Native LoRA support is built-in, so community LoRAs work without additional calibration nodes.

To apply a LoRA in ComfyUI, add a LoRA Loader node between the model loader and the KSampler. Use a strength of 0.6–1.0 for style LoRAs, and drop it to 0.3–0.5 when stacking multiple LoRAs to avoid compounding artifacts.

Using the Lightning LoRA for Faster Generation

The Lightning LoRA supports 4-step and 8-step inference and runs on as little as 8GB of VRAM with 16GB of system RAM. Quality degradation at 4 steps is minimal, making it worth enabling for rapid iteration.

When using the Lightning LoRA, set CFG to 1.0 and steps to 4. Leaving them at standard values with the LoRA loaded produces blocky, over-saturated output.

Sage Attention for Speed Optimization

For faster generation, consider installing Sage Attention. It reduces attention computation time and meaningfully speeds up generation on compatible hardware. If not installed, disable the Sage Attention node in the workflow. It runs normally without it.

Running Qwen Image from the Command Line

The Qwen CLI handles model download, one-shot queries, interactive chat, and launching a local API-compatible server. Key flags include --prompt, --negative-prompt, --steps, --fast (8-step Lightning LoRA), --ultra-fast (4-step Lightning LoRA v2.0), --seed, --num-images, and --lora. It is especially useful for headless servers or scripted batch jobs where a browser UI is not practical.

Supported Qwen Image Resolutions

Qwen-Image performs well from 768x768 to 2048x2048. Processing time increases significantly at larger sizes. Sticking to native training aspect ratios gives the most reliable results.

In editing mode, Qwen Image Edit outputs match the aspect ratio of the first input image at approximately 1 megapixel. A 768x960 input (4:5 ratio) produces an 896x1160 output, for example. You can override this by connecting a custom latent size node to the workflow.

Last Thoughts on Qwen Image

Qwen-Image leads the open-source field on multi-image composition, instruction-based editing, and bilingual text rendering. The GGUF and FP8 variants bring it within reach of 8 to 16GB GPUs, while the RTX A6000's 48GB runs FP8 and GGUF comfortably. For full BF16 precision with the model, text encoder, and VAE all resident in VRAM, an 80GB card such as an A100 is the cleaner choice.

Read next

See how Qwen-Image compares to other open-source image generation models.

FAQ