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.
Couple that with the LoRA, in about 3 seconds you can generate completely personalized images.
The speed alone is a big factor but if you put the model side by side with seedream and nanobanana and other models it's definitely in the top 5 and that's killer combo imho.
I don't know anything about paying for these services, and as a beginner, I worry about running up a huge bill. Do they let you set a limit on how much you pay? I see their pricing examples, but I've never tried one of these.
Yeah, I've definitely switched largely away from Flux. Much as I do like Flux (for prompt adherency), BFL's baffling licensing structure along with its excessive censorship makes it a noop.
For ref, the Porcupine-cone creature that ZiT couldn't handle by itself in my aforementioned test was easily handled using a Qwen20b + ZiT refiner workflow and even with two separate models STILL runs faster than Flux2 [dev].
Most of the people I know doing local AI prefer SDXL to Flux. Lots of people are still using SDXL, even today.
Flux has largely been met with a collective yawn.
The only thing Flux had going for it was photorealism and prompt adherence. But the skin and jaws of the humans it generated looked weird, it was difficult to fine tune, and the licensing was weird. Furthermore, Flux never had good aesthetics. It always felt plain.
Nobody doing anime or cartoons used Flux. SDXL continues to shine here. People doing photoreal kept using Midjourney.
They maybe have an rhlf phase, but I mean there is also just the shape of the distribution of images on the internet and, since this is from alibaba, their part of the internet/social media (Weibo) to consider
Considering how gaga r/stablediffusion is about it, they weren’t wrong. Apparently Flux 2 is dead in the water even though the knowledge it has contained in the model is way, way higher than Z-Image (unsurprisingly).
Z-Image is getting traction because it fits on their tiny GPUs and does porn sure, but even with more compute Flux 2[dev] has no place.
Weak world knowledge, worse licensing, and it ruins the #1 benefit of a larger LLM backbone with post-training for JSON prompts.
LLMs already understand JSON, so additional training for JSON feels like a cheaper way to juice prompt adherence than more robust post-training.
And honestly even "full fat" Flux 2 has no great spot: Nano Banana Pro is better if you need strong editing, Seedream 4.5 is better if you need strong generation.
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 will generate anything. Xi/Pooh porn, Taylor Swift getting squashed by a tank at Tiananmen Square, whatever, no censorship at all.
With simplistic prompts, you quickly conclude that the small model size is the only limitation. Once you realize how good it is with detailed prompts, though, you find that you can get a lot more diversity out of it than you initially thought you could.
Absolute game-changer of a model IMO. It is competitive with Nano Banana Pro in some respects, and that's saying something.
I could imagine the Chinese government is not terribly interested in enforcing its censorship laws when this would conflict with boosting Chinese AI. Overregulation can be a significant inhibitor to innovation and competitiveness, as we often see in Europe.
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 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.
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.
All modern models have their default looks. Meaningful variety of outputs for the same inputs in finetuned models is still an open technical problem. It's not impossible, but not solved either.
I would say there's isn't an equivalent. Some people will probably tell you ComfyUI - you can expose workflows via API endpoints and parameterize them. This is how e.g. Krita AI Diffusion uses a ComfyUI backend.
For various reasons, I doubt there are any large scale SaaS-style providers operating this in production today.
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 have a 24GB M5 macbook pro. In ComfyUI using default z-image workflow, generating a single image just took me 399 seconds, during which the computer froze and my airpods lost audio.
On replicate.com a single image takes 1.5s at a price of 1000 images per $1. Would be interesting to see how quick it is on ComfyUI Cloud.
Overall, running generative models locally on Macs seems very poor time investment.
If you don't know anything about AI in terms of how these models are run, comfyui's macos version is probably the easiset to use. There is already a Z-Image workflow that you can get and comfyui will get all the models you need and get it work together. Can expect decent speed
I follow an author who publishes online on places like Scribblehub and has a modestly successful Patreon. Over the years he has spent probably tens of thousands of dollars on commissioned art for his stories, and he's still spending heavily on that. But as image models have gotten better this has increasingly been supplemented with AI-images for things that are worth a couple dollars to get right with AI, but not a couple hundred to get a human artist to do them
Roughly speaking the art seems to have three main functions:
1. promote the story to outsiders: this only works with human-made art
2. enhance the story for existing readers: AI helps here, but is contentious
3. motivate and inspire the author: works great with AI. The ease of exploration and pseudo-random permutations in the results are very useful properties here that you don't get from regular art
By now the author even has an agreement with an artist he frequently commissions that he can use his style in AI art in return for a small "royalty" payment for every such image that gets published in one of his stories. A solution driven both by the author's conscience and by the demands of the readers
Except for gaming, that doesn't sound like a huge market worthy of pouring millions into training these high-quality models. And there is a lot of competition too. I suspect there are some other deep-pocketed customers for these images. Probably animations? movies? TV ads?
I'd say that picture ad market alone would suffice.
OTOH these are open-weight models released to the public. We don't get to use more advanced models for free; the free models are likely a byproduct of producing more advanced models anyway. These models can be the freemium tier, or gateway drugs, or a way of torpedoing the competition, if you don't want to believe in the goodwill of their producers.
Dying businesses like newspapers and local banks, who use it to save the money they used to spend on shutterstock images? That’s where I’ve seen it at least. Replacing one useless filler with another.
Incredibly fast, on my 5090 with CUDA 13 (& the latest diffusers, xformers, transformers, etc...), 9 samplig steps and the "Tongyi-MAI/Z-Image-Turbo" model I get:
Did you use PyTorch Native or Diffusers Inference? I couldn't get the former working yet so I used Diffusers, but it's terribly slow on my 4080 (4 min/image). Trying again with PyTorch now, seems like Diffusers is expected to be slow.
Uh, not sure? I downloaded the portable build of ComfyUI and ran the CUDA-specific batch file it comes with.
(I'm not used to using Windows and I don't know how to do anything complicated on that OS. Unfortunately, the computer with the big GPU also runs Windows so I'm stuck with it here.)
Supports MPS (Metal Performance Shaders). Using something that skips Python entirely along with a mlx or gguf converted model file (if one exists) will likely be even faster.
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.
Thoughts
- 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.
It is amazing how far behind Apple Silicon is when it comes to use non- language models.
Using the reference code from Z-image on my M1 ultra, it takes 8 seconds per step. Over a minute for the default of 9 steps.
Apple Silicon is comparable in memory bandwidth to mid-range GPUs, but it’s light years behind on compute.
https://github.com/Tongyi-MAI/Z-Image
Screenshot of site with network tools open to indicate link
https://imgur.com/a/FZDz0K2
EDIT: It's possible that this issue might have existed in an old cached version. I'll purge the cache just to make sure.
EDIT: Fixed! Thanks soontimes and rprwhite!
https://fal.ai/models/fal-ai/z-image/turbo/api
Couple that with the LoRA, in about 3 seconds you can generate completely personalized images.
The speed alone is a big factor but if you put the model side by side with seedream and nanobanana and other models it's definitely in the top 5 and that's killer combo imho.
https://fal.ai/pricing
https://fal.ai/models/fal-ai/z-image/turbo
Is Flux 1/2/Kontext left in the dust by the Z Image and Qwen combo?
For ref, the Porcupine-cone creature that ZiT couldn't handle by itself in my aforementioned test was easily handled using a Qwen20b + ZiT refiner workflow and even with two separate models STILL runs faster than Flux2 [dev].
https://imgur.com/a/5qYP0Vc
Once Z-image base comes out and some real tuning can be done, I think it has a chance of replacing it for the function SDXL has
Flux has largely been met with a collective yawn.
The only thing Flux had going for it was photorealism and prompt adherence. But the skin and jaws of the humans it generated looked weird, it was difficult to fine tune, and the licensing was weird. Furthermore, Flux never had good aesthetics. It always felt plain.
Nobody doing anime or cartoons used Flux. SDXL continues to shine here. People doing photoreal kept using Midjourney.
It's incredibly clear who the devs assume the target market is.
Z-Image is getting traction because it fits on their tiny GPUs and does porn sure, but even with more compute Flux 2[dev] has no place.
Weak world knowledge, worse licensing, and it ruins the #1 benefit of a larger LLM backbone with post-training for JSON prompts.
LLMs already understand JSON, so additional training for JSON feels like a cheaper way to juice prompt adherence than more robust post-training.
And honestly even "full fat" Flux 2 has no great spot: Nano Banana Pro is better if you need strong editing, Seedream 4.5 is better if you need strong generation.
It's not clear to me what you mean either, especially since female models are overwhelmingly more popular in general[1].
[1]: "Female models make up about 70% of the modeling industry workforce worldwide" https://zipdo.co/modeling-industry-statistics/
Ok so a ~2:1 ratio. Those examples have a 25:1 ratio.
https://www.youtube.com/watch?v=LTJvdGcb7Fs
That said I do find the focus on “safety” tiring.
https://imgur.com/a/7FR3uT1
With simplistic prompts, you quickly conclude that the small model size is the only limitation. Once you realize how good it is with detailed prompts, though, you find that you can get a lot more diversity out of it than you initially thought you could.
Absolute game-changer of a model IMO. It is competitive with Nano Banana Pro in some respects, and that's saying something.
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.
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.
Local AI will eventually be booming. It'll be more configurable, adaptable, hackable. "Free". And private.
Crude APIs can only get you so far.
I'm in favor of intelligent models like Nano Banana over ComfyUI messes (the future is the model, not the node graph).
I still think we need the ability to inject control layers and have full access to the model, because we lose too much utility by not having it.
I think we'll eventually get Nano Banana Pro smarts slimmed down and running on a local machine.
With how expensive RAM currently is, I doubt it.
For various reasons, I doubt there are any large scale SaaS-style providers operating this in production today.
[1]: https://github.com/Tongyi-MAI/Z-Image?tab=readme-ov-file#-qu...
On replicate.com a single image takes 1.5s at a price of 1000 images per $1. Would be interesting to see how quick it is on ComfyUI Cloud.
Overall, running generative models locally on Macs seems very poor time investment.
I'm still curious whether this would run on a MacBook and how long would it take to generate an image. What machine are you using?
Roughly speaking the art seems to have three main functions:
1. promote the story to outsiders: this only works with human-made art
2. enhance the story for existing readers: AI helps here, but is contentious
3. motivate and inspire the author: works great with AI. The ease of exploration and pseudo-random permutations in the results are very useful properties here that you don't get from regular art
By now the author even has an agreement with an artist he frequently commissions that he can use his style in AI art in return for a small "royalty" payment for every such image that gets published in one of his stories. A solution driven both by the author's conscience and by the demands of the readers
- Illustrating blog posts, articles, etc.
- A creativity tool for kids (and adults; consider memes).
- Generating ads. (Consider artisan production and specialized venues.)
- Generating assets for games and similar, such as backdrops and textures.
Like any tool, it takes certain skill to use, and the ability to understand the results.
OTOH these are open-weight models released to the public. We don't get to use more advanced models for free; the free models are likely a byproduct of producing more advanced models anyway. These models can be the freemium tier, or gateway drugs, or a way of torpedoing the competition, if you don't want to believe in the goodwill of their producers.
The bang:buck ratio of Z-Image Turbo is just bonkers.
- 1.5s to generate an image at 512x512
- 3.5s to generate an image at 1024x1024
- 26.s to generate an image at 2048x2048
It uses almost all the 32Gb Gb of VRAM and GPU usage. I'm using the script from the HF post: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
(I'm not used to using Windows and I don't know how to do anything complicated on that OS. Unfortunately, the computer with the big GPU also runs Windows so I'm stuck with it here.)
Supports MPS (Metal Performance Shaders). Using something that skips Python entirely along with a mlx or gguf converted model file (if one exists) will likely be even faster.