Qwen Image

I didnt know that 2511 could do that without waiting for the AIO model.

I'm always getting slightly plasticy and airbrushed results from Qwen Image Edit, the teeth and yes don't look very natural, especially if it's not a face portrait. I see Nano Banana and Grok Imagine and GPT Image doing such great work and makes me wonder if any Image to Image Comfyui workflow with locally hosted models can ever come close. Would love to see other share their thoughts or workflows if you have any. Thanks!

I've created an agent for generating Japanese film-style image cues. The images produced using this combination are of very high quality. I've also tried using these cues to create images in MyJet, and the results are quite good. There are some noticeable differences in the results; which one do you prefer? If there's a lot of interest, I'll open-source this agent.

I've uploaded a Comfyui workflow for local use, you can click this link to download it directly: [https://drive.google.com/file/d/1pLz52RDPdyQMgwS5LVeMrQ2GVFrhLy78/view?usp=drive\_link](https://drive.google.com/file/d/1pLz52RDPdyQMgwS5LVeMrQ2GVFrhLy78/view?usp=drive_link)

However, I strongly recommend replacing the node used for image-based prompts from qwen3 with a larger language model like Gemini or GPT for better results.

Therefore, I've also prepared two cloud-based workflows for your convenience: If you want to use the Comfyui cloud platform, the workflow is here: [https://www.runninghub.cn/post/2053673047776866305/?inviteCode=rh-v1317](https://www.runninghub.cn/post/2053673047776866305/?inviteCode=rh-v1317)

If you prefer to use MJ, you can use it through TapNow,the workflow is here: [https://app.tapnow.ai/tapflow/view/2e3b1d50](https://app.tapnow.ai/tapflow/view/2e3b1d50)

I’ve compared some character LoRAs that I trained myself on both Z-Image Turbo (ZIT) and Qwen Image 2512. Every character LoRA in this comparison was trained using the exact same dataset on both ZIT and Qwen.

All comparisons above were done in ComfyUI using 12 steps, 1 CFG, multiple resolutions. I intentionally bumped up the steps higher than the defaults (8 for ZIT, 4 for Qwen Lightning) hoping to get maximum results.

As you can see in the images, ZIT is still better in terms of realism compared to Qwen.
Even though I used the res\_2s sampler and bong\_tangent scheduler for Qwen (because the realism drops without them), the skin texture still looks a bit plastic. ZIT is clearly superior in terms of realism. Some of the prompt tests above also used references from the dataset.

For distant shots, Qwen LoRAs often require FaceDetailer (as i did on Dua Lipa concert image above) to make the likeness look better. ZIT sometimes needs FaceDetailer too, but not as often as Qwen.

ZIT is also better in terms of prompt adherence (as we all expected). Maybe it’s due to the Reinforcement Learning method they use.

As for Concept Bleeding/ Semantic Leakage (I honestly don't understand this deeply, and I don't even know if I'm using the right term ). maybe one of you can explain it better? I just noticed a tendency for diffusion models to be hypersensitive to certain words.

This is where ZIT has a flaw that I find a bit annoying: the concept bleeding on ZIT is worse than Qwen (maybe because of smaller parameters or the distilled model?). For example, with the prompt "a passport photo of \[subject\]". Even though both models tend to generate Asian faces with this prompt but the association with Asian faces is much stronger on ZIT. I had to explicitly mention the subject's traits for non-Asian character LoRAs. Because the concept bleeding is so strong on ZIT, I haven't been able to get a good likeness on the "Thor" prompt like the one in the image above.

And it’s already known that another downside of ZIT is using multiple LoRAs at once. So far, I haven't successfully used 3 LoRAs simultaneously. 2 is still okay.

Although I’m still struggling to make LoRAs involving specific acts that work well when combined with character lora, i’ve trained that work fine when combined with character lora. You can check out those on: [https://civitai.com/user/markindang](https://civitai.com/user/markindang)

All of these LoRAs were trained using ostris/ai-toolkit. Big thanks to him!

Qwen2512+FaceDetailer: [https://drive.google.com/file/d/17jIBf3B15uDIEHiBbxVgyrD3IQiCy2x2/view?usp=drive\_link](https://drive.google.com/file/d/17jIBf3B15uDIEHiBbxVgyrD3IQiCy2x2/view?usp=drive_link)
ZIT+FaceDetailer: [https://drive.google.com/file/d/1e2jAufj6\_XU9XA2\_PAbCNgfO5lvW0kIl/view?usp=drive\_link](https://drive.google.com/file/d/1e2jAufj6_XU9XA2_PAbCNgfO5lvW0kIl/view?usp=drive_link)