Intro
Alright this has been a long time coming. I'm the dude who figured out Qwen Edit 2509 a while back , and I've been on-and-off trying to figure out the same for 2511. Results in Comfy have always been worse than the examples shown by the Qwen team, and worse than the official Qwen chat implementation online. Well, I finally cracked it and it only took 5 months lol.
Anyway, turns out Qwedit 2511 is fucking sick. IMO it particularly excels at making new shots of characters while maintaining their likeness.
As usual, I'll start off with all the setup stuff at the top and then give an explanation + tips & info below that. Also I'm gonna be calling Qwen Edit "Qwedit" most of the time.
The posted images are all raw outputs from Qwedit, without being upscaled (despite mentioning it later in this post). They're also all done with only 20 steps instead of the hypothetical 30 I'd do if I wasn't planning to upscale them. Read further for more on that too.
The ref images were all made with Z-image base ( workflow here ), except for the anime one which came from Anima ( workflow here ).
What is this
These are minimalistic workflows for Qwen Image Edit 2511 that give the highest quality outputs. All other Qwen Edit workflows are now bad , use this one until those get updated with the new info. Aside from generally improving output quality (by a LOT), these changes also enable high-res edits and have better prompt adherence.
As for why , basically ComfyUI has some serious issues with how it's implemented Qwen Edit and there aren't any workflows out there (that I've found) which have resolved them. These issues result in poor prompt adherence and low resolution/quality outputs. Thankfully the fix is fairly straightforward.
The configuration for this is 100% portable and can be migrated to existing workflows to make them better; it works by changing how the reference inputs are handled, and uses 100% native comfy nodes . Feel free to update other workflows without crediting me.
There are also a couple of (optional) related upscale workflows inside; read on for info on why those are here.
Workflows
Normal Workflows
These are separated into single / 2 image workflows. It's done this way because the setup for multi-image is complicated and I didn't want to force you to use a ton of custom nodes to make it useable all-in-one.
These do still use one custom node for quality-of-life. Minimal quality-of-life that is; I promise there's nothing unnecessary or pointless.
Dev Workflows
Linking these separately to avoid cluttering the post attachment.
These are the same as the normal workflows but without any quality-of-life nodes or 'helpful' stuff . Grab these if you want to copy the logic over to other workflows, or if you just an easier view of how it works without any clutter.
I do not recommend using the dev workflows for actual gens because you will constantly forget to manually adjust stuff correctly.
Dev Single Image
Dev 2 Image
Models
Main Model
qwen_edit_2511_fp8
or GGUF versions
Important: the FP8 version of Qwedit is much higher quality than the Q8 GGUF, always use FP8 if you can. Only use the GGUFs if you need to use quants lower than Q8.
FP8 is 22GB, so you'll need a combined ~26GB of RAM + VRAM to run it
You don't need 24GB of VRAM to run it thanks to ComfyUI's blockswapping, but the less VRAM you have the slower it'll run
Only use Q6 & lower quants if you absolutely have to; the quality will noticeably go down
Goes in models/diffusion_models
Text Encoder
Use only the normal FP8 text encoder with Qwedit; abliterated/GGUF encoders will reduce your output quality.
qwen_2.5_vl_7b_fp8
Goes in models/text_encoders
VAE
qwen_image_vae
Goes in models/vae
Loras
You can use them as normal, just load them however you normally would. I left out lora loader nodes to avoid cluttering the workflow.
It's worth noting that many Qwen Image loras work with Qwen Edit too, but you'll need to test them individually to be sure.
Lightning Loras - BAD
All the lightning loras / distils for Qwedit (that I've tested) are shit and make your outputs look bad, so I'm not linking them here. The main issue is the same as with Klein Distilled: it makes people's skin look like plastic.
But you can technically use them. Don't do it tho. But you can if you want. But don't.
Alternative: if you want to cut your gen time down while testing prompts, just set it to 10 steps instead of 20, then go back to 20 once you're satisfied your prompt is correct. It'll still work fine, the quality just dips.
Custom Nodes
LayerStyle - A set of handy nodes that manipulate images. We're just using this for its image scaling node which allows you to scale by an image's long edge while maintaining divisibility by 16. You can skip this if you want to use a different scaling method, but you'll need to fix the workflow switch for scaling if you do.
SeedVR2 (OPTIONAL) - Only get this if you want to use the seedvr upscale workflow that's included.
How To Use
How To Use Part 1 - Basic Options
There are instructions in the workflow as well, but there's more detail here. Read parts 2 & 3 as well, they're important.
It works just like a normal Qwedit workflow, but has a couple of extra options available. This section just tells you what they are and how to use them.
Enhance with Double Ref
This is a switch that turns on double-ref mode. This feeds your input images in TWICE to the model, and generally produces much higher quality results. Downside? It takes about 50% longer to gen.
I recommend leaving this on 100% of the time for single-image prompts, unless you're just messing around and want speed. It is ALWAYS better for single image prompts, and will improve everything from prompt adherence to output clarity.
For multi-image prompts, it usually increases adherence but sometimes reduces it. So, if you're doing multi-image stuff I recommend switching this on/off as needed based on how it's going with your prompt.
Input Scale
When off, your image doesn't get scaled (it still gets cropped to be divisible by 16). When on, the long edge of your image gets scaled to the number you put in the box. For example, if you feed in a 2560x1440 image and set the scale to 1920 it will scale your image to 1920x1080. That will then get cropped to 1920x1072 so it's divisible by 16.
Custom Output Size
When the switch is off, your output image will be the same size as your input image (after it's been scaled). If you turn this switch on, it will instead output an image with the dimensions you specify.
As a general rule, you should try to set your scales to be similar along at least one edge. For example, a 1920x1440 input image and a 1024x1440 input image are both suitable for a 1440x1440 output image. You can be more flexible with this if you know what you're doing.
How To Use Part 2 - Multi-image Prompting Requirement
This section is not a prompting guide (that's further below). This is about an actual requirement for prompting multi-image stuff. It is NOT required for single-image prompts.
You do multi-image prompts like normal, except you need to write a very basic description of your input images. Qwedit needs you to do this in order to know which image is which. I explain why in detail later.
You may find this slightly annoying, but I guarantee you it's dramatically better than using Qwedit the normal way that other workflows do - and it's pretty easy.
The format:
At the start of your prompt, write an extremely simple description for each of your input images; one sentence for each
Start each sentence with "Picture 1:", "Picture 2:", etc
You must write it this way because Qwedit was trained on this exact format
Afterwards, write your actual prompt as usual; you can refer to your input images as "Picture 1" and so on
The model uses these descriptions to understand which input picture is which, and it works better with SIMPLE descriptions. You only need to help it know which one is which, it doesn't need a full rundown.
Examples
Picture 1: a man wearing a t-shirt. Picture 2: a top hat. Make the man in Picture 1 wear the top hat from Picture 2.
Picture 1: a living room. Picture 2: a woman. Put the woman from Picture 2 into the living room in Picture 1.
Picture 1: a man wearing a professional suit. Picture 2: a man wearing a superhero outfit. Make the man in Picture 1 wear the outfit from Picture 2.
How To Use Part 3 - Upscaling
Because the qwen VAE tends to put a subtle halftone pattern over images (see limitations just below this section), I recommend downscaling and then re-upscaling your images afterwards. A big benefit of being able to work at high res with the edit model is that you rarely lose any detail doing this.
This eliminates the halftone pattern if you're using something like seedvr, or at least reduces it if you're using other upscalers. I think seedvr is best for this, but it's very beefy and hard to run on older GPUs.
Note: the workflow is set to do 20 steps of inference. It actually gives sharper results at 30 steps, but I don't bother with that because it takes longer and I down-upscale them afterwards anyway. If you aren't planning on down-upscaling them, you might consider doing 30 steps for the extra sharpness.
Seedvr2 sometimes gives better output at 0.5x downscale, and other times 0.75, so the workflow is configured to run BOTH for you to pick which one turned out best.
Normal upscalers are a bit different; a relatively small downsize to something like 1920p -> 1600p is usually reasonable, before then running the upscaler. Play around with it. The non-seedvr workflow has a longest_edge scale option so you can tweak the number specifically.
My preferred regular upscaler is 4x Nomos2 HQ DAT2 , but you can use whatever.
Examples of upscaling:
Here's the pic raw output of the robot-arm girl in a dress from the post: https://ibb.co/B5jhrsL9 (if you zoom in you'll see the qwen halftone pattern, it looks like a grid)
Here's the pic after it's been run through seedvr after a 0.75x downscale: https://ibb.co/hJcn2f5t
Here's the pic after it's been run through a regular Nomos2 upscale after a downscale to 1600p: https://ibb.co/Kc2YSbVc
Limitations of Qwen Edit
Limitation 1
The Qwen VAE will often put a subtle halftone grid pattern over your images. It's noticeable if you zoom in, and more noticeable at higher resolutions. This is a feature of pretty much every Qwen-based model, but it's particularly present with the Edit model.
You can easily resolve this by downscaling your image a bit, then re-upscaling it again to your desired resolution. The section above explains this in better detail.
It sounds like a big issue, but the downscale-upscale trick solves it easily and it's not always necessary either. The higher quality your input image, the less bad the halftone pattern will be.
Limitation 2
Qwedit struggles with complex multi-image stuff most of the time (it's just a limitation of the model). This workflow makes it much better, but it's still not great. You'll have to play around with it to know which things work and which things don't.
I recommend using a different model if you want to do anything complicated with multi-image.
Limitation 3
It takes a while to gen stuff if not using the lightning loras. Very similar to the time it takes with Klein 9B base. The double-ref trick increases it by roughly 50%, and multi-image edits take a lot longer.
For low res images (typical 1mpx size) it's pretty okay, around 50 seconds on a 5090 with the double-ref option turned on.
But then there's high-res stuff. Gen time scales non-linearly as you go higher. Going from 1024x1024 (1 mpx) to 1440x1440 (2 mpx) takes around 2.5x as long. Going from 1 mpx to 3 mpx is around 4x as long. 5 mpx is 9.5x as long. In conclusion, stick to 2-3 mpx unless you're cool with long-ass gen times. Stick around 1-2 mpx for multi-image gens, or turn off the double ref switch.
On…
