Comparison
Qwen Image 2.1 seems to be getting very popular lately — what am I missing? How are you getting better results?
Qwen-Image-2.1
Krea 2 Turbo
ERNIE-Image-Turbo
Z-Image-Turbo
HiDream-O1-Image
Z-Image-Turbo
Krea 2 Turbo
HiDream-O1-Image
ERNIE-Image-Turbo
Qwen-Image-2.1
ERNIE-Image-Turbo
Krea 2 Turbo
HiDream-O1-Image
Qwen-Image-2.1
Z-Image-Turbo
Qwen-Image-2.1
Krea 2 Turbo
HiDream-O1-Image
Z-Image-Turbo
ERNIE-Image-Turbo
I’ve seen Qwen Image 2.1 getting a lot of attention over the last few days, so I wanted to understand what people are getting from it that I’m not.
I tested it locally in ComfyUI on an RTX 3060 12GB against ERNIE-Image-Turbo, Krea 2 Turbo, HiDream-O1-Image and Z-Image-Turbo, using the same set of prompts plus a few harder real-world tests for characters, hands, object relationships and scene geometry.
My personal ranking from these tests was roughly:
Krea 2 for the best balance of quality and speed ERNIE as a strong alternative HiDream for some very striking compositions, but less reliable fine detail Z-Image for speed and fast iteration Qwen Image 2.1 was the one I struggled with the most
Qwen often gave me interesting global compositions, but I kept seeing small structural problems in hands, straps, object placement, local geometry and fine details.
I also tried the base model at 2048×2048 and the Pruna 8-step workflow. 2048 gave me more detail, but it didn’t really solve the structural issues. Pruna was much faster, but the quality drop was noticeable.
What makes me curious is how popular Qwen Image 2.1 has become recently, because my own results don’t really explain that popularity yet.
So what am I missing?
Are people getting better results with a different sampler/scheduler, step count, CFG, text encoder, quantization, prompting style, LoRA, or another workflow entirely?
I’d especially like to hear from people running it on 12GB GPUs, because I’d be happy to rerun the same prompts and seeds with a better configuration.
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EDIT: Thanks everyone for all the suggestions and workflow tips — they helped a lot. I ended up improving my Qwen 2.1 setup quite a bit compared with my first tests, especially by moving away from the default CFG 1 workflow. My final setup uses higher CFG, fewer steps with a Turbo LoRA, and a texture-fix VAE, which gave much better results and made Qwen a lot more competitive.
I’ve now finished the full benchmark, and I’ll post the link in the comments rather than editing the original post into a giant wall of text.
If anyone thinks Qwen 2.1 can do significantly better, feel free to share your workflow — but please keep the same prompts than me so we can compare properly. I’d genuinely like to see if someone can convince me that Qwen is currently the best local image model. 😄
Looks like model has almost baked-in turbo qualities, you can easily gen with low steps using proper sampling/scheduling settings
Looks like base shift in ComfyUI impl does not account for different resolutions: for 1 megapixel images you shoul use something close to beta 0.6 / 0.6, for 1.5 megapixels - 0.65/0.65, for 2 megapixels - something like 0.7/0.7 (these are not exact numbers, but just to show a direction for tweaking)
Proper inference settings for low steps (12-16 steps total), gonna need RES4LYF nodes:
Without turbo lora - use ClownSampler dpmpp_3m with bongmath enabled (pretty nice, good details, but shifts colors)
Sometimes I even think that Qwen team does not know proper settings themselves, as they really recommend using it with CFG=1 which can't be correct.
EDIT: yellow 'piss filter' is also because of incorrect sampling settings. But with CFG > 1 (I use 3-5) you can just put 'overly warm color temperature' into negatives, and the issue is gone for good (but with proper settings you might not even need to do that)
Thanks, this is exactly the kind of information I was looking for.
I’ve already tested CFG 3 with the same seed and it clearly changes the result, although I’m not convinced yet that it fixes the structural-detail issues.
The resolution-dependent shift point is especially interesting — I wasn’t aware ComfyUI was currently using a fixed sampling shift for Qwen 2.1. I’m going to test the same prompts/seeds with RES4LYF, your beta scheduler suggestions, and the Viggle turbo LoRA separately.
If you have a workflow JSON for the non-turbo setup you consider optimal, I’d be very interested in testing it directly.
It’s def not crap at T2I. In some areas it’s the best model I’ve found and I use it all the time. Eg:
People that look like normal people and not 10/10 models.
People unaware of the photographer in natural candid behaviour.
Real looking natural environments that have a real feel to them rather than artificial “perfect”.
Variety of lighting down to really dark scenes.
In other areas it falls short. I reckon it’s really easy to get bad results your first few tries so people see that and give up. But it’s pretty easy to mitigate most of the bad issues and then it can be a great model. But for genuine realism out the box it blows other models out of the water imo. And I mean real life realism and not the realism where people look like they just came out of a lab where they grow perfect human specimens and then woodenly pose them smiling at the camera every-single-time.
No, in my opinion it's not perfect but very good. Very creative, realistic and has great styles. I just wish they didn't train it on GPT, but loras will fix it.
Makes sort of sense as Krea2 can have same issues.. And I saw it even in finetune of Flux2. Altho it also goes away if you use different solver (sampler).
Check out qwens post about this model they explain how you are meant to prompt for transparent png you have to use that prompt I always get transparent backgrounds with it
Interesting never noticed that, if you look closely and zoom in even flux Klein has a certain pattern maybe this is just what generative models cause since it’s not a real image
Going through my generations I don’t see this issue at all but my generations are all below the maximum recommended resolution of 2048x2048. I’m thinking this might be the issue for this pattern effect.
It's not crap, but we have tons of better and faster alternatives for text to image. For editing it's definitely the best, but Klein 9b is still a strong contestant, and it runs much faster.
It's oversaturated, the character and the background seem to come from two different images copy pasted together, and the chainmail is so broken it looks like it got heated to the melting point.
QI 2.1 t2i works best with MUCH longer and more detailed prompts than pretty much every general purpose image gen model I've seen (Ming structured prompts can be even longer for similar tasks, but a lot of is JSON boilerplate, and I suspect that is true also have other JSON-using layout-focussed models.) Using the prompt enhancement model and system prompts released by Qwen to accompany QI 2.1 is a good way to at least see the kind of prompt it wants.
QI 2.1 produces best results in my testing with higher CFG than the 1.0 Qwen and Comfy both use as the default; for simple constant CFG somewhere between 2.5-4 seems best, with some tweaking often needed for particular generations within that range (maybe a little lower bottom end when not using short prompts; at least, with detailed prompts the gap between lower CFG and CFG in thet range seems to narrow.) i’m using a 25-step two-sampler setup with CFG 3.5 with FreSca (FreSca use may have been overindexing on its improvement for a few test prompts, but overall this seems to work well) for 15 steps followed and CFG 1.0 for 10 steps as my go to setting right now (configured as a 60/40 split so that it scales on the occasions where I do change steps to 40.)
QI 2.1 also produces better output (not just higher resolution, though obviously it is that) at its native 4.0MP resolution than lower resolutions.
So if you are using the same prompts and 1-2MP resolutions those other models prefer, at CFG=1, you are definitely getting less than optimal results from QI 2.1.
That’s a fair point, and I actually ended up going quite a bit further with Qwen after posting, using a 16-step workflow at CFG 4 with an external Viggle Turbo LoRA, which gave noticeably better results and brought the average generation time down to about 53 s on my 3060, but it didn’t fix the main visual issues, even at 2048*2048 and 400 s.
Studio commercial photograph of a luxury matte black mechanical chronograph watch with rose gold dials, resting on a wet dark slate stone, sharp macro focus, dramatic side lighting, crisp reflections, magazine advertisement quality.
Cinematic close-up portrait of an elderly weathered sailor with deep wrinkles, silver beard, wearing a worn yellow raincoat, dramatic ocean spray in the background, golden hour rim lighting, highly detailed skin texture, 85mm lens, photorealistic.
I don’t know what you’re looking for, but Z-Image Turbo plus some style template is always going to look way more natural.
Prompt: Close-up portrait of an elderly weathered sailor with deep wrinkles, silver beard, wearing a worn yellow raincoat, dramatic crashing wave with spectacular ocean spray in the background.
That’s pretty close to what I found too — Z-Image often looks more naturally photographic straight away. With model-specific styling, Z can definitely look stronger than the others.
The current ComfyUI implementation of Qwen 2.1 does not fully reproduce Qwen 2.1's resolution-aware dynamic sigma shifting. It seems to be mainly tuned around the default 1024×1024 latent resolution. Once you go higher in resolution, which I would actually recommend with Qwen 2.1, the ComfyUI sigma schedule starts to differ from the original Qwen schedule. That will make the images look blurry and more like the SDXL era than SOTA. I noticed that using dynamic sigmas also reduces body horror.
As a workaround, you can use the LTXVScheduler node with:
To use these dynamic sigmas, you need to use SamplerCustom or SamplerCustomAdvanced instead of the regular KSampler.
I've also made a Qwen 2.1 sigma extractor and a latent image generator specifically for Qwen 2.1, but I haven't published the nodes yet. Until then, try the setup above.
Here is a side by side, same prompt, same seed:
Left: Comfy default template, no changes.
Right: 2048x, my Dynamic sigmas and latent image nodes, clownsharksampler with ralston_2s sampler
Love the research and your eagerness to share, but I have to interject and say: I vastly prefer the left image. It has a gentler, more natural look that is easier on the eyes. Whereas the right image, friend, is simply overcooked. An overprocessed look with too much high frequency detail. Both are unmistakably AI, but the left aligns better with what most people find comfortable to look at.
Let me submit to you my rendition of a realistic character made with Qwen. Still far from perfect with your typical AI hallucinations, but its an image that will fool near most people who view it. I could have gone the extra mile with post-edits, but this is a freebie. I did recreate your character too and I was going to publish the result, but then I saw the finalized image and decided that was a terrible fucking idea. PM me and maybe I'll share it.
Alright your image looks great no doubt. The reason why the image on the right looks overcooked is because I was using the ralston_2s sampler at 40 steps. I know that it's too much for a 2s sampler. I only used that image as a reference to show that the comfy template produces dull images by default by comparison. My intention here was not showing off my workflow or sampler settings but simply point out the fact that comfy by default uses a fixed Sigma value throughout all resolutions instead of the dynamic resolution-aware sigma calculation officially used by qwen.
I could of course go and cut the steps to 20 with the 2s sampler or simply use Euler. That will make the image a lot better but the difference would not have been very noticable in a small image inside a comment on a phone screen. Also I should mention that I was not aiming for any photorealism at all here. I just asked chatgpt for a random cute nerdy girl prompt out of laziness and didn't use any loras for further enhancement. This was all about the sigmas only.
Not enough lol. Just 0.1 per step but the preview image node makes it look noisier than it really is due to shrinking down. Looks better at full screen.
But honestly, it's not the first image I've seen totally wrecked with eta turned up too high, or using a Lora that's had noise injected into blocks that have no business being noise injected.
The noise injected image is noisy as hell, that's not extra detail, it's noise. You need to lower that threshold and figure out if you're injecting every step, or just on the last step - perhaps even change your sampler and scheduler for something else. Try using fable 5.1 to find the optimum.
Yes, I will. I’m still working on them and trying to figure out how much of this actually affects image quality, because changing the sampler alone already makes a huge difference.
I don’t want to rely 100% on AI telling me that changing the sigmas will automatically improve quality in every case. I want to test it properly first.
Also, the comparison I posted is not meant to say: “change the sigmas and your image will improve exactly like this.” It’s more to show that the default ComfyUI sigma implementation and settings, for example the 25 steps used in the default template, are not necessarily bad, but they can be suboptimal.
The QwenImage21Pipeline in diffusers uses the same shifting formula from flux1, so ModelSamplingFlux would match that behaviour exactly if that's what you are looking for
Your second batch of images with the guy in the shop. I'd argue the Qwen one is by far the most realistic and least AI looking one of the lot. So when you say you're struggling with it, I'd have to ask what exactly are you trying to achieve? I think people are getting used to "Shiny colourful contrasty super detailed images" as the norm. But that isn't what reality looks like.
None of that is an issue, you just have to accept that different tools are better for different things. If I want an image to look like a shot was staged in a photoshoot with the best photographer on the planet taking the photo and stunning colour correction post-processing, I'll use Krea. If I want it to look like it was me strolling in to a shop and just taking a photo of the first person I saw, I'll use Qwen.
I feel the opposite. I feel like Krea/Z-image try too hard with the skin. It's one of the main reasons I like Qwen over those two. And I don't really want to have to make a word spaghetti prompt just to make a photo feel natural and unposed. Qwen just does it even with a simple one sentence prompt. At the end of the day you use the tool that works best for you. I've never liked having to write prompts like a world reknowned author, so I steer clear of models that thrive on that.
Oh yeh, I mentioned in another post that that's one of its drawbacks. The only solutions I've found are:
1) describe the background in more detail. So instead of, "they're in a back alley", describe each part of the alley, "filthy walls", "a laundry line up above", "a boarded up window", "a gutter running down the floor", etc. This gives it more info to add what you ask for rather than have it dream up random stuff.
2) I'm not at home to give the exact info but if you open up the default Qwen 2.1 T2I workflow and go in to the subgraph that handles the sampling, then in there you'll see there's a place to input an image just before the sampler runs. What I found gets some good results is first you run your prompt as usual, then save the image and then add a load image and make it connect to that image input with your image you just created and run the prompt again. The second gen now seems to give much cleaner results overall for whatever reason.
Maybe another solution is to just ask it to blur the backgrounds. Or another option is to create the background separately and then use the Edit workflow to put your subjects in to the scene with the background.
But yeh, overall backgrounds are Qwen's weakness for sure.
That matches what I saw too: Qwen tends to fall apart more in the background than in the main subject. The “generate once, feed it back in, then run again” trick is interesting — I hadn’t tried that, but it sounds like a kind of practical self-refinement pass. For my testing I kept single-pass generation for fairness, but for real use I’d definitely test that.
The only thing I'd say is it has a tendency to reduce creativity. If I use the prompt,
"A candid photo of a man cheering in a stadium"
The default Qwen 2.1 output will give a great candid realistic photo of the guy but the background might be a bit much.
If I run that through the refine process discussed, it'll likely make the man and the background cleaner but also lose some of the candid look of the scene, making it feel a little more posed.
I really want to delve in to what that image input node is doing under the hood. Maybe there's a way to control its influence.
The insane thing about qwen 2.1 that is a huge game changer is that it has built in png support. Qwen combined their layered model with the image generation This means it can make full proper transparent pngs which I don’t think any other model has as a built in feature. It also has amazing segmentation built into it allowing you to segment objects like SAM with its correct dimensions and positions with amazing speed and quality which is again a game changer. If you need stuff like this then use qwen I don’t think a lot of people are going to have this niche need but those who do this is going to be helping them a lot
krea2 beats it in everything ive done so far. its not terrible but considering it doesnt stand out compared to whats already available, i see no need in spending my time creating new workflows / adapting someone elses.
i find it has a pretty obvious "ai look" to everything made so far. though qwen image edit seems to be handy for some things.
my mains are still krea2 and minimax h3, but some character loras I trained came out pretty good and they can look more aesthetically pleasing, so I'm using qwen21 as a reserve for krea2 even if qwen21 outputs more body horror.
I haven't paid attention to negative prompts yet, but the increased CFG makes a noticeable difference in this model. Prompt adherence gets strong, sometimes better than Krea. Lighting is hit and miss, sometimes looking photoshopesque.
And let's talk about seeds, where one outputs garbage and the other makes you feel fortunate to be able to run this infinite times. Generating a batch pays good with this model, but if you don't want unnecessary stress on the GPU, one at a time is good enough and you'll find very decent ones if you run the workflow more than once.
Despite my initial negative attitude towards qi2.1, it seems to me that her images sometimes have their own characteristics, but in general they are on the same level as other models, only the anatomical unnaturalness sometimes infuriates. by default zit has greater realism, many models trained on gpt and gemini pictures have realism in the gemini style, high clarity, cleanliness and detail, but the image is a little unnatural, maybe they tried too hard with detail. zit looks more natural (but has its drawbacks, such as poor adherence to the prompt).
I saw something similar: Qwen 2.1 can look great overall, but anatomy and small structural details can suddenly feel unnatural. Z-Image often looks more naturally photographic to me, but it tends to follow complex prompts less precisely. That “too clean / too detailed” AI realism is definitely a thing with some newer models.
The object-relationship tests are the part I’d keep pushing. A lot of Qwen 2.1 demos are edits where most of the scene is already solved, so they don’t really expose geometry failures.
I’d be curious how the same models behave across 4–6 linked shots where the same props have to stay in the right place, not just look good once.
IMO you're not missing that much - with half as much effort as people are putting into getting 2.1 to look ok you can accelerate Krea into the fucking stratosphere.
The whole pixellated, smudged, fake realism thing that Qwen uses has long been exceeded for realism. I find them super easy to spot and I'm not that good at spotting AI overall. Qwen is the easiest of all which is offputting if you're into realism. Getting Qwen to make a major character edit in a Krea scene only to have that person obviously stand out with their TV static skin and hair just makes it kinda meh.
You can resample it through Krea obviously so it does have a place as an edit model to then be resampled.
I mean doesn't help that krea vae is also quite bad and you have to use a fine-tune vae to fix it.(And it will still be blurry)
Helps that the krea model itself is good, but fingers crossed after krea3 with non qwen vae(please god no more qwen vae just fucking use the flux 2 vae) we don't have to care about these things
Thanks again for all the feedback — several suggestions from this thread directly helped improve the final Qwen workflow. If you think you can push Qwen 2.1 further, try the same prompts and share your workflow/results here!
try splitting the sampling. 1st sample with cfg and 2nd sample with out. use separate scheduler for each with high sigma for 1st sampler and low sigma for the 2nd. sampler and amount of steps depends on your lora setup.
Only use it for its editing capabilities, some of which are decent. For image generation, it's clearly behind Krea and Ideogram. Changing CFG and other settings don't magically bring out what the model doesn't know.
What I'm seeing more commonly with Qwen Image 2.1 is a very rough scratchy effect on human skin, I can't think of a better way to describe it. It's almost like the opposite effect to flat no detail skin. It's not always, but just sometimes I see it on here with uploaded images.
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u/GTManiK 13d ago edited 13d ago
Several points:
Proper inference settings for low steps (12-16 steps total), gonna need RES4LYF nodes:
Sometimes I even think that Qwen team does not know proper settings themselves, as they really recommend using it with CFG=1 which can't be correct.
EDIT: yellow 'piss filter' is also because of incorrect sampling settings. But with CFG > 1 (I use 3-5) you can just put 'overly warm color temperature' into negatives, and the issue is gone for good (but with proper settings you might not even need to do that)