4 ms·

I've done some preliminary testing with Z-Image Turbo in the past week. Thoughts - It's fast (~3 seconds on my RTX 4090) - Surprisingly capable of maintainin

by vunderba 11mo ago

I've done some preliminary testing with Z-Image Turbo in the past week. 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 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.

echelon 11mo ago
So does this finally replace SDXL? Is Flux 1/2/Kontext left in the dust by the Z Image and Qwen combo?
- tripplyons 11mo ago
  SDXL has been outclassed for a while, especially since Flux came out.
  - aeon_ai 11mo ago
    Subjective. Most in creative industries regularly still use SDXL. 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
    - Scrapemist 11mo ago
      Source?
      - echelon 11mo ago
        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.
        kouteiheika 11mo ago
        > it was difficult to fine tune Yep. It's pretty difficult to fine tune, mostly because it's a distilled model. You can fine tune it a little bit, but it will quickly collapse and start producing garbage, even though fundamentally it should have been an easier architecture to fine-tune compared to SDXL (since it uses the much more modern flow matching paradigm). I think that's probably the reason why we never really got any good anime Flux models (at least not as good as they were for SDXL). You just don't have enough leeway to be able to train the model for long enough to make the model great for a domain it's currently suboptimal for without completely collapsing it.
        magicalhippo 11mo ago
        > It's pretty difficult to fine tune, mostly because it's a distilled model. What about being distilled makes it harder to fine-tune?
        kouteiheika 11mo ago
        AFAIK a big part of it is that they distilled the guidance into the model. I'm going to simplify all of this a lot so please bear with me, but normally the equation to denoise an image would look something like this: pos = model(latent, positive_prompt_emb) neg = model(latent, negative_prompt_emb) next_latent = latent + dt * (neg + cfg_scale * (pos - neg)) So what this does - you trigger the model once with a negative prompt (which can be empty) to get the "starting point" for the prediction, and then you run the model again with a positive prompt to get the direction in which you want to go, and then you combine them. So, for example, let's assume your positive prompt is "dog", and your negative prompt is empty. So triggering the model with your empty prompt with generate a "neutral" latent, and then you nudge it into the direction of your positive prompt, in the direction of a "dog". And you do this for 20 steps, and you get an image of a dog. Now, for Flux the equation looks like this: next_latent = latent + dt * model(latent, positive_prompt_emb) The guidance here was distilled into the model. It's cheaper to do inference with, but now we can't really train the model too much without destroying this embedded guidance (the model will just forget it and collapse). There's also an issue of training dynamics. We don't know exactly how they trained their models, so it's impossible for us to jerry rig our training runs in a similar way. And if you don't match the original training dynamics when finetuning it also negatively affects the model. So you might ask here - what if we just train the model for a really long time - will it be able to recover? And the answer is - yes, but at this point the most of the original model will essentially be overwritten. People actually did this for Flux Schnell, but you need way more resources to pull it off and the results can be disappointing: https://huggingface.co/lodestones/Chroma https://huggingface.co/lodestones/Chroma
    - CuriouslyC 11mo ago
      I don't think that's fair. SDXL is crap at composition. It's really good with LoRAs to stylize/inpaint though.
  - deleted 11mo ago
    [deleted]
- vunderba 11mo ago
  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]. https://imgur.com/a/5qYP0Vc https://imgur.com/a/5qYP0Vc
- mythz 11mo ago
  SDXL has long been surpassed, it's primary redeeming feature is fine tuned variants for different focus and image styles. IMO HiDream had the best quality OSS generations, Flux Schnell is decent as well. Will try out Z-Image soon.
- dragonwriter 11mo ago
  > So does this finally replace SDXL? If Flux Base is easy to train and works as well with multiple LoRAs as SDXL, quite probably, yes, at least as the center of mass for new community efforts. > Is Flux 1/2/Kontext left in the dust by the Z Image and Qwen combo? Flux.2 looks like it may be the kind of weights-available editing models, but Qwen is strong here, too (and it remains to be seen if much-lighter Z-Image Edit is a powerhouse here, as well.) But for most local generation tasks, its probably hard to justify the weight of Flux.2 (even though recent improvements in ComfyUI with assistance from NVidia make quantized versions usable on modest consumer systems, its still big and slow.) Add BFLs restrictive licensing on top of the difference in resource requirements for training, and I don't imagine you would see nearly as much community LoRA and finetuning work for Flux.2 as for Z-Image, so the practical difference is likely to be narrower than the difference in base model quality.
amrrs 11mo ago
On fal, it takes less than a second many times. https://fal.ai/models/fal-ai/z-image/turbo/api 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.
- venusenvy47 11mo ago
  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. https://fal.ai/pricing https://fal.ai/pricing
  - tethys 11mo ago
    It works with prepaid credits, so there should be no risk. Minimum credit amount is $10, though.
    - vunderba 11mo ago
      This. You can also run most (if not all) of the models that Fal.ai directly from the playground tab including Z-Image Turbo. https://fal.ai/models/fal-ai/z-image/turbo https://fal.ai/models/fal-ai/z-image/turbo
  - Bombthecat 11mo ago
    For images I like them: https://runware.ai/ https://runware.ai/ super cheap and super fast, they also support Loras and you can upload your own models. And you work with credits
    - Bombthecat 11mo ago
      Why the down vote? Are they scam?
tarruda 11mo ago
> It's fast (~3 seconds on my RTX 4090) 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.
- p-e-w 11mo ago
  The diffusion process is usually compute-bound, while transformer inference is memory-bound. Apple Silicon is comparable in memory bandwidth to mid-range GPUs, but it’s light years behind on compute.
  - tarruda 11mo ago
    > but it’s light years behind on compute. Is that the only factor though? I wonder if pytorch is lacking optimization for the MPS backend.
    - rfoo 11mo ago
      This is the only factor. People sometimes perceive Apple's NPU as "fast" and "amazing" which is simply false. It's just that NVIDIA GPU sucks (relatively) at *single-user* LLM inference and it makes people feel like Apple not so bad.
- tails4e 11mo ago
  I heard last year the potential future of gaming is not rendering but fully AI generated frames. 3 seconds per 'frame' now, it's not hard to believe it could do 60fps in a few short years. It makes it seem more likely such a game could exist. I'm not sure I like the idea, but it seems like it could happen
  - wcoenen 11mo ago
    Increasing the framerate by rendering at a lower resolution + upscaling, or outright generation of extra frames has already been a thing for a few years now. NVidia calls it Deep Learning Super Sampling (DLSS)[1]. AMD's equivalent is called FSR[2]. [1] https://en.wikipedia.org/wiki/Deep_Learning_Super_Sampling https://en.wikipedia.org/wiki/Deep_Learning_Super_Sampling [2] https://en.wikipedia.org/wiki/GPUOpen#FidelityFX_Super_Resolution https://en.wikipedia.org/wiki/GPUOpen#FidelityFX_Super_Resol...
nialv7 11mo ago
China really is keeping the open weight/source AI scene alive. If in five years a consumer GPU market still exists it would be because of them.
- p-e-w 11mo ago
  Pretty sure the consumer GPU market mostly exists because of games, which has nothing to do with China or AI.
  - deleted 11mo ago
    [deleted]
  - samus 11mo ago
    The consumer GPU market is not treated as a primary market by GPU makers anymore. Similar to how Micron went B2B-only.
    - adventured 11mo ago
      The parent comment of course understands that. Nvidia views the gaming market as an entry threat, a vector from which a competitor can come after their AI GPU market. That's the reason Nvidia won't be looking to exit the gaming scene no matter how large their AI business gets. If done correctly, staying in the gaming GPU market helps to suppress competition. Exiting the consumer market is likely a mistake by Micron. If China takes that market segment, they'll eventually take the rest, eliminating most of Micron's value. Holding consumer is about keeping entry attacks covered.
      - CamperBob2 11mo ago
        Exiting the consumer market is likely a mistake by Micron. I actually think their move to shut down the Crucial channel will prove to be a good one. Why? Because we're heading toward a bimodal distribution of outcomes: either the AI bubble won't pop, and it will pay to prioritize the data center customers, or it will pop. In the latter case a consumer/business-facing RAM manufacturer will have to compete with its own surplus/unused product on scales never seen before. Worst case scenario for Micron/Crucial, all those warehouses full of wafers that Altman has reserved are going to end up back in the normal RAM marketplace anyway. So why not let him foot the bill for fabbing and storing them in the meantime? Seems that the RAM manufacturers are just trying to make the best of a perilous situation.
soontimes 11mo ago
If that’s your website please check GitHub link - it has a typo (gitub) and goes to a malicious site
- deleted 11mo ago
  [deleted]
- vunderba 11mo ago
  Thanks for the heads up. I just checked the site through several browsers and proxying through a VPN. There's no typo and it properly links to: https://github.com/Tongyi-MAI/Z-Image https://github.com/Tongyi-MAI/Z-Image Screenshot of site with network tools open to indicate link https://imgur.com/a/FZDz0K2 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.
  - rprwhite 11mo ago
    The link with the typo is in the footer.
    - vunderba 11mo ago
      Well holy crap - that's been there for about forever! I need a "domain name" spellchecker built into my Gulp CI/CD flow. EDIT: Fixed! Thanks soontimes and rprwhite!
rendaw 11mo ago
That's 2/4? The kitkat bars look nothing like kitkat bars for the most part (logo? splits? white cream filling?). The DNA armor is made from normal metal links.
- vunderba 11mo ago
  Fair. Nobody said it was going to surpass Flux.1 Dev (a 12B parameter model) or Qwen-Image (a 20B parameter model) where prompt adherence is strictly concerned. It's the reason I'm holding off until the Z-Image Base version is released before adding to the official GenAI model comparisons. But for a 6B model that can generate an image in under 5 seconds, it punches far above its weight class. As to the passing images, there is white chocolate kit-kat (I know, blasphemy, right?).
dragonwriter 11mo ago
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. [0] https://huggingface.co/Tongyi-MAI/Z-Image-Turbo/discussions/8#6927ecfb89d327829b15e815 https://huggingface.co/Tongyi-MAI/Z-Image-Turbo/discussions/... [1] https://www.reddit.com/r/StableDiffusion/comments/1p87xcd/zimage_prompt_enhancer/ https://www.reddit.com/r/StableDiffusion/comments/1p87xcd/zi...

echelon 11mo ago

So does this finally replace SDXL? Is Flux 1/2/Kontext left in the dust by the Z Image and Qwen combo?

tripplyons 11mo ago
SDXL has been outclassed for a while, especially since Flux came out.
- aeon_ai 11mo ago
  Subjective. Most in creative industries regularly still use SDXL. 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
  - Scrapemist 11mo ago
    Source?
    - echelon 11mo ago
      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.
      - kouteiheika 11mo ago
        > it was difficult to fine tune Yep. It's pretty difficult to fine tune, mostly because it's a distilled model. You can fine tune it a little bit, but it will quickly collapse and start producing garbage, even though fundamentally it should have been an easier architecture to fine-tune compared to SDXL (since it uses the much more modern flow matching paradigm). I think that's probably the reason why we never really got any good anime Flux models (at least not as good as they were for SDXL). You just don't have enough leeway to be able to train the model for long enough to make the model great for a domain it's currently suboptimal for without completely collapsing it.
        magicalhippo 11mo ago
        > It's pretty difficult to fine tune, mostly because it's a distilled model. What about being distilled makes it harder to fine-tune?
        kouteiheika 11mo ago
        AFAIK a big part of it is that they distilled the guidance into the model. I'm going to simplify all of this a lot so please bear with me, but normally the equation to denoise an image would look something like this: pos = model(latent, positive_prompt_emb) neg = model(latent, negative_prompt_emb) next_latent = latent + dt * (neg + cfg_scale * (pos - neg)) So what this does - you trigger the model once with a negative prompt (which can be empty) to get the "starting point" for the prediction, and then you run the model again with a positive prompt to get the direction in which you want to go, and then you combine them. So, for example, let's assume your positive prompt is "dog", and your negative prompt is empty. So triggering the model with your empty prompt with generate a "neutral" latent, and then you nudge it into the direction of your positive prompt, in the direction of a "dog". And you do this for 20 steps, and you get an image of a dog. Now, for Flux the equation looks like this: next_latent = latent + dt * model(latent, positive_prompt_emb) The guidance here was distilled into the model. It's cheaper to do inference with, but now we can't really train the model too much without destroying this embedded guidance (the model will just forget it and collapse). There's also an issue of training dynamics. We don't know exactly how they trained their models, so it's impossible for us to jerry rig our training runs in a similar way. And if you don't match the original training dynamics when finetuning it also negatively affects the model. So you might ask here - what if we just train the model for a really long time - will it be able to recover? And the answer is - yes, but at this point the most of the original model will essentially be overwritten. People actually did this for Flux Schnell, but you need way more resources to pull it off and the results can be disappointing: https://huggingface.co/lodestones/Chroma https://huggingface.co/lodestones/Chroma
  - CuriouslyC 11mo ago
    I don't think that's fair. SDXL is crap at composition. It's really good with LoRAs to stylize/inpaint though.
- deleted 11mo ago
  [deleted]
vunderba 11mo ago
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]. https://imgur.com/a/5qYP0Vc https://imgur.com/a/5qYP0Vc
mythz 11mo ago
SDXL has long been surpassed, it's primary redeeming feature is fine tuned variants for different focus and image styles. IMO HiDream had the best quality OSS generations, Flux Schnell is decent as well. Will try out Z-Image soon.
dragonwriter 11mo ago
> So does this finally replace SDXL? If Flux Base is easy to train and works as well with multiple LoRAs as SDXL, quite probably, yes, at least as the center of mass for new community efforts. > Is Flux 1/2/Kontext left in the dust by the Z Image and Qwen combo? Flux.2 looks like it may be the kind of weights-available editing models, but Qwen is strong here, too (and it remains to be seen if much-lighter Z-Image Edit is a powerhouse here, as well.) But for most local generation tasks, its probably hard to justify the weight of Flux.2 (even though recent improvements in ComfyUI with assistance from NVidia make quantized versions usable on modest consumer systems, its still big and slow.) Add BFLs restrictive licensing on top of the difference in resource requirements for training, and I don't imagine you would see nearly as much community LoRA and finetuning work for Flux.2 as for Z-Image, so the practical difference is likely to be narrower than the difference in base model quality.

amrrs 11mo ago

On fal, it takes less than a second many times. https://fal.ai/models/fal-ai/z-image/turbo/api 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.

venusenvy47 11mo ago
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. https://fal.ai/pricing https://fal.ai/pricing
- tethys 11mo ago
  It works with prepaid credits, so there should be no risk. Minimum credit amount is $10, though.
  - vunderba 11mo ago
    This. You can also run most (if not all) of the models that Fal.ai directly from the playground tab including Z-Image Turbo. https://fal.ai/models/fal-ai/z-image/turbo https://fal.ai/models/fal-ai/z-image/turbo
- Bombthecat 11mo ago
  For images I like them: https://runware.ai/ https://runware.ai/ super cheap and super fast, they also support Loras and you can upload your own models. And you work with credits
  - Bombthecat 11mo ago
    Why the down vote? Are they scam?

tarruda 11mo ago

> It's fast (~3 seconds on my RTX 4090) 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.

p-e-w 11mo ago
The diffusion process is usually compute-bound, while transformer inference is memory-bound. Apple Silicon is comparable in memory bandwidth to mid-range GPUs, but it’s light years behind on compute.
- tarruda 11mo ago
  > but it’s light years behind on compute. Is that the only factor though? I wonder if pytorch is lacking optimization for the MPS backend.
  - rfoo 11mo ago
    This is the only factor. People sometimes perceive Apple's NPU as "fast" and "amazing" which is simply false. It's just that NVIDIA GPU sucks (relatively) at *single-user* LLM inference and it makes people feel like Apple not so bad.
tails4e 11mo ago
I heard last year the potential future of gaming is not rendering but fully AI generated frames. 3 seconds per 'frame' now, it's not hard to believe it could do 60fps in a few short years. It makes it seem more likely such a game could exist. I'm not sure I like the idea, but it seems like it could happen
- wcoenen 11mo ago
  Increasing the framerate by rendering at a lower resolution + upscaling, or outright generation of extra frames has already been a thing for a few years now. NVidia calls it Deep Learning Super Sampling (DLSS)[1]. AMD's equivalent is called FSR[2]. [1] https://en.wikipedia.org/wiki/Deep_Learning_Super_Sampling https://en.wikipedia.org/wiki/Deep_Learning_Super_Sampling [2] https://en.wikipedia.org/wiki/GPUOpen#FidelityFX_Super_Resolution https://en.wikipedia.org/wiki/GPUOpen#FidelityFX_Super_Resol...

nialv7 11mo ago

China really is keeping the open weight/source AI scene alive. If in five years a consumer GPU market still exists it would be because of them.

p-e-w 11mo ago
Pretty sure the consumer GPU market mostly exists because of games, which has nothing to do with China or AI.
- deleted 11mo ago
  [deleted]
- samus 11mo ago
  The consumer GPU market is not treated as a primary market by GPU makers anymore. Similar to how Micron went B2B-only.
  - adventured 11mo ago
    The parent comment of course understands that. Nvidia views the gaming market as an entry threat, a vector from which a competitor can come after their AI GPU market. That's the reason Nvidia won't be looking to exit the gaming scene no matter how large their AI business gets. If done correctly, staying in the gaming GPU market helps to suppress competition. Exiting the consumer market is likely a mistake by Micron. If China takes that market segment, they'll eventually take the rest, eliminating most of Micron's value. Holding consumer is about keeping entry attacks covered.
    - CamperBob2 11mo ago
      Exiting the consumer market is likely a mistake by Micron. I actually think their move to shut down the Crucial channel will prove to be a good one. Why? Because we're heading toward a bimodal distribution of outcomes: either the AI bubble won't pop, and it will pay to prioritize the data center customers, or it will pop. In the latter case a consumer/business-facing RAM manufacturer will have to compete with its own surplus/unused product on scales never seen before. Worst case scenario for Micron/Crucial, all those warehouses full of wafers that Altman has reserved are going to end up back in the normal RAM marketplace anyway. So why not let him foot the bill for fabbing and storing them in the meantime? Seems that the RAM manufacturers are just trying to make the best of a perilous situation.

soontimes 11mo ago

If that’s your website please check GitHub link - it has a typo (gitub) and goes to a malicious site

deleted 11mo ago
[deleted]
vunderba 11mo ago
Thanks for the heads up. I just checked the site through several browsers and proxying through a VPN. There's no typo and it properly links to: https://github.com/Tongyi-MAI/Z-Image https://github.com/Tongyi-MAI/Z-Image Screenshot of site with network tools open to indicate link https://imgur.com/a/FZDz0K2 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.
- rprwhite 11mo ago
  The link with the typo is in the footer.
  - vunderba 11mo ago
    Well holy crap - that's been there for about forever! I need a "domain name" spellchecker built into my Gulp CI/CD flow. EDIT: Fixed! Thanks soontimes and rprwhite!

rendaw 11mo ago

That's 2/4? The kitkat bars look nothing like kitkat bars for the most part (logo? splits? white cream filling?). The DNA armor is made from normal metal links.

vunderba 11mo ago
Fair. Nobody said it was going to surpass Flux.1 Dev (a 12B parameter model) or Qwen-Image (a 20B parameter model) where prompt adherence is strictly concerned. It's the reason I'm holding off until the Z-Image Base version is released before adding to the official GenAI model comparisons. But for a 6B model that can generate an image in under 5 seconds, it punches far above its weight class. As to the passing images, there is white chocolate kit-kat (I know, blasphemy, right?).

dragonwriter 11mo ago

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. [0] https://huggingface.co/Tongyi-MAI/Z-Image-Turbo/discussions/8#6927ecfb89d327829b15e815 https://huggingface.co/Tongyi-MAI/Z-Image-Turbo/discussions/... [1] https://www.reddit.com/r/StableDiffusion/comments/1p87xcd/zimage_prompt_enhancer/ https://www.reddit.com/r/StableDiffusion/comments/1p87xcd/zi...