We've come a long way with these image models, and the things you can do with paltry 6B are super impressive. The community has adopted this model wholesale, and left Flux(2) by the way side. It helps that Z-Image isn't censored, whereas BFL (makers of Flux 2) dedicated like a fith of their press release talking about how "safe" (read: censored and lobotomized) their model is.
It‘s mainly due to system requirements that Flux.2-dev doesn’t get same usage as Z-Image. A 5090 needs about a minute to generate an image with a basic workflow with Flux.2-dev. But prompt adherence and scene/character consistency in edit mode is (way) ahead of Qwen-Edit-2509 if you ask me.
Z-Image seems to be the first successor to Stable Diffusion 1.5 that delivers better quality, capability, and extensibility across the board in an open model that can feasibly run locally. Excitement is high and an ecosystem is forming fast.
I’ve paired my Z-Image Turbo with SeedVR2 upscale, running on a RTX3060 12gb, 32gb sysMEM, generates in 40sec. I’m holding out for Z-Image Edit that is a larger model, once that is out… going to be interesting. Oh and to train your own ZIT LoRA, takes 5hrs for 3000 steps. So fast.
Personally I find it works better as a refiner model downstream of Qwen-Image 20b which has significantly better prompt understanding but has an unnatural "smoothness" to its generated images.
One thing I noticed is that you tested it with very short prompts; Z-Image Turbo really likes long prompts, and recommends using an LLM as a prompt enhancer, even providing a prompt template. [0] I have had pretty good look using an English translation that was posted on Reddit [1] with Qwen3-4B-Instruct locally sometimes modified somewhat for particular tasks; it seems biased to adding text to some images as-is) with short prompts.
i have been testing this on my Framework Desktop. ComfyUI generally causes an amdgpu kernel fault after about 40 steps (across multiple prompts), so i spent a few hours building a workaround here https://github.com/comfyanonymous/ComfyUI/pull/11143
overall it's fun and impressive. decent results using LoRA. you can achieve good looking results with as few as 8 inference steps, which takes 15-20 seconds on a Strix Halo. i also created a llama.cpp inherence custom node for prompt enhancement which has been helping with overall output quality.
As an AI outsider with a recent 24GB macbook, can I follow the quick start[1] steps from the repo and expect decent results? How much time would it take to generate a single medium quality image?
I'm particularly impressed by the fact that they seem to aim for photorealism rather than the semi-realistic AI-look that is common in many text-to-image models.
My issue with this model is it keeps producing Chinese people and Chinese text. I have to very specifically go out of my way to say what kind of race they are.
If I say “A man”, it’s fine. A black man, no problem. It’s when I add context and instructions is just seems to want to go with some Chinese man. Which is fine, but I would like to see more variety of people it’s trained on to create more diverse images. For non-people it’s amazingly good.
I've messed with this a bit and the distill is incredibly overbaked. Curious to see the capabilities of the full model but I suspect even the base model is quite collapsed.
Unfortunately, another China censored model.
Simply ask it to generate "Tank Man" or "Lady Liberty Hong Kong" and the model return a blackboard with text saying "Maybe Not Safe".
- Uses existing model backbones for text encoding & semantic tokens (why reinvent the wheel if you don't need to?)
- Trains on a whole lot of synthetic captions of different lengths, ostensibly generated using some existing vision LLM
- Solid text generation support is facilitated by training on all OCR'd text from the ground truth image. This seems to match how Nano Banana Pro got so good as well; I've seen its thinking tokens sketch out exactly what text to say in the image before it renders.
This model is awesome. I am building an infinite CYOA game and this was a drop-in replacement for my scene image generation. Faster, cheaper, and higher quality than what I was using before!
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[ 3.0 ms ] story [ 48.4 ms ] threadThoughts
- It's fast (~3 seconds on my RTX 4090)
- Surprisingly capable of maintaining image integrity even at high resolutions (1536x1024, sometimes 2048x2048)
- The adherence is impressive for a 6B parameter model
Some tests (2 / 4 passed):
https://imgpb.com/exMoQ
Personally I find it works better as a refiner model downstream of Qwen-Image 20b which has significantly better prompt understanding but has an unnatural "smoothness" to its generated images.
[0] https://huggingface.co/Tongyi-MAI/Z-Image-Turbo/discussions/...
[1] https://www.reddit.com/r/StableDiffusion/comments/1p87xcd/zi...
It's incredibly clear who the devs assume the target market is.
overall it's fun and impressive. decent results using LoRA. you can achieve good looking results with as few as 8 inference steps, which takes 15-20 seconds on a Strix Halo. i also created a llama.cpp inherence custom node for prompt enhancement which has been helping with overall output quality.
[1]: https://github.com/Tongyi-MAI/Z-Image?tab=readme-ov-file#-qu...
If I say “A man”, it’s fine. A black man, no problem. It’s when I add context and instructions is just seems to want to go with some Chinese man. Which is fine, but I would like to see more variety of people it’s trained on to create more diverse images. For non-people it’s amazingly good.
It generates an image of a tank and the statue of liberty for those prompts.
Some interesting takeaways imo:
- Uses existing model backbones for text encoding & semantic tokens (why reinvent the wheel if you don't need to?)
- Trains on a whole lot of synthetic captions of different lengths, ostensibly generated using some existing vision LLM
- Solid text generation support is facilitated by training on all OCR'd text from the ground truth image. This seems to match how Nano Banana Pro got so good as well; I've seen its thinking tokens sketch out exactly what text to say in the image before it renders.
Anything with "most cultures" were manga-influenced comic strips with kanji. Useless.
Are you sure it was Japanese? Because the model is Chinese so it's likely to output Chinese (it happened in my testing).