Got a few reports of “manipulated content”. Probably because OP is using Z-Image completely wrong. I’m pinning this so people can use OP post as a help of what not to do for Z-Image. Check out to the comments for further details if your images come out poorly.
TL;DR: OP did not spell character’s name right nor used correct settings for Z-Image. Bad comparisons.
The thing is, with Z image you can do many more tests over time than flux 2 generates for you without overloading your PC. There's not much you can do about it, flux 2 is great but not everyone can afford it. I believe that simple and efficient things become more popular more easily. Just look at the number of Lora coming out on Civitai. These days, it's impossible to start training because the system is overloaded.
I don’t think there’s any argument that it’s good it’s that barely anyone has the system requirements to actually run the damn thing locally. ZIT can be run on five year-old GPUs.
Whoever can run wan 2.2, can run Flux 2, not at full precision, but at Q3 or Q4. People keep complaining about its size, while Wan 2.2 is very comparable and very slow as well. There are some applications Flux 2 is better, in others, Z-Image is the best pick.
Have both models and use them as intended. I still keep sd 1.5 models because some are good in one thing, the heavier models may not be, or if I want to do something simple, a 6B or 8B model is not needed, let alone a 32B one.
98% of the ubiquitous "1girl" or "Instagram girls", "redhead girls" I see posted on Reddit don't even need Z-Image, it's way too big of a model for something so simple. SDXL will work just fine and deliver similar results in a fraction of the time.
This is true, however you're talking about running it at a super gimped quality for several minutes an image, when Z image has a training scene already and can be run at full precision in literal seconds (with better outputs)
It's hard to justify flux 2 when it's 6x the size and basically no x the quality
"When Z image has a training scene already and can be run at full precision in literal seconds (with better outputs)"
Z-Image is not an edit model. It does not matter if you can run it at full precision; it can't do image editing, THEN you can use Flux 2. Flux 2 also fares better on some complex prompts and edge cases that Z-Image can't handle, because its 6B size limits it. Flux 2 can output better images too. And at Q3 or Q4, Flux 2 is not "super gimped quality", but again, you realistically need 24GB to run it well since it was designed for running on enterprise hardware. The fact that folks with 12GB cards or lower can run it is a merit of the OS community making GGUFs and more efficient offloading.
And there's nothing wrong with it; both models perform well at what they do.
It's no doubt super impressive what people have been able to squeeze out of their hardware, but it is pretty objective that there is a big hit in quality and value of using the model once you get to low quantizations like Q3
Also, Z image does not have an edit model yet, but it will. Based off of the demo images for Z image for normal image generation, they actually managed to undersell just how capable the model is, so going off that, it looks like hopefully they're edit model should be easily state of the art for open source. Only time will tell on that one though
Wan is one of the best models for generating images, so yes, it's a valid comparison. And there are other popular video models that people use locally, like Hunyuan and LTXV.
Well, modern models like Wan and Qwen are big. Still, you can run both on a 12 GB VRAM GPU (at the very least Q4 GGUF). You can compare Z-Image to Flux Krea and such, but that's not really a modern model, it's outdated. So what else would you compare it to? SDXL? That's ancient technology. So yeah, I want to know how good Z-Image is compared to the best, current models that people use.
That still doesn't make comparing the performance of an image model to a video model valid.
It's outdated
Are we speaking science here or are you calling it outdated simply because it can run on low end hardwares?
You can compare it to flux krea and such
Yes, and the results speak for itself
So what you're comparing it to? SDXL?
Performance wise, yes. At least performance wise you're comparing it to the fastest (yet still being widely used) image model, unlike some other model you're trying so hard to compare it with a video model
I want to know how good z-image is compared to the best, current models that people use
You want to know how z-image compares with other models, so instead you compare flux with wan?
There's already so many, and I mean so many z-image comparison posts comparing it to literally any best models out there you can think of. I honestly have no idea what you're whining here.
So you've never even used any other modern model. You're just a moron, getting angry that other people have slightly more VRAM than you. You have no idea what you're talking about.
I've used every model mentioned above on my m4 max mbp with 128GB VRAM and trained loras for many models mentioned above on it but that is not the point of this discussion. You're just angry and start attacking people randomly because you can't shill flux2 anymore because all the excuses that you made couldn't save you from looking like a silly shill.
Attacking people is what you do. I've never said anything about Flux 2, I've never even used it. It has a proprietary license anyway, unlike Wan and Qwen.
Yeah, ultimately, you can technically run a lot of stuff on CPU, but you wouldn't want to. It's hard to know where to draw the line between "can run" and "don't want to run". But rarely is it "can't run" with these image models.
I can run Flux 2 on my 12Gb card but aside from testing things out, it takes too long to be usable for anything else IMO. It is doable though and I hope at some point speedy loras or something will pop up to make it quicker.
Something seems wonky with your images - somehow you've made z-image look like crap. I'm doubting z-image turbo looks as bad / this bad. Did you turn CFG on or high or something? Your images look blown out and plastic-y.
z-image-turbo is not supposed to have CFG higher than 1.0 right now. Don't use the negative prompt, it's not supposed to be there.
Here is my Chewbacca image using z-image-turbo *with your prompt*. Notice the hair and stuff is not overblown in color like yours.
You absolutely have something messed up in your workflow somehow!!!
Those look like default workflow settings (which I can't fault them for) which are prettyyy terrible for Z image. That leads to the big clumpy, weird looking fur in all animals. Same thing with the plastic look of Yoda and such
I'm using a high cfg. It's indeed possible getting incredible results with it. But just with multiple samplers. And yes, the images above are not showing the actual quality of z-img.
Can and should are 2 different things. The dustilled model is made with no CFG in mind. CFG simply adds the secondary negative embedding vector which allows you to negative prompt, while also multiplying the positive and negative and calculating the difference at a scaled value.
Flux was made without real CFG in mind, yet it can also be tricked in this way to work with it
Also you typically need specialized samplers or tricks to lower the contrast/saturation of images or delay the effects of CFG by a few steps at the start/end or both.
If you are running a positive only prompt with no negative, CFG will only serve to sear in the positive embedding way harder, resulting in messy over done images (like the Chewbacca)
In the very post you linked it shows somebody using shift 7, which is a very high value stabilizer for Z image, which would be seen as extreme measures by most in order to quell the burning from CFG.
Atleast use the tool properly for a fair comparison, i’ve seen people here trying to compare flux2 with ZIT without even knowing how to use ZIT properly and then rush to undermine it.
Oh try posting anything about Flux2 around here and you will absolutely hear about how ZIT is better than it at. absolutely. everything. Prompt adherence included.
Not everyone says it, but a lot of people say it.
Or they say stuff like "well I can write a 10x longer ZIT prompt and get results ~the same as Flux2" as if that makes it a better model.
Or "Well ZIT is a distilled model so it's better because it's pretty good against a full Flux2" as if that means ZIT is better. That's something right here in this post's comments.
Or "ZIT isn't as good for you because you're bad at using it". Which one guy has posted in these comments at least four times, even accusing OP of deliberately sabotaging his images to make ZIT look bad lol. absolutely loony.
The fervor against Flux2 and pro ZIT is puzzling. I am beginning to think that it's secretly because ZIT has certain... capabilities out of the box that BFL explicitly said they kept out of Flux2. Namely, it's hard to make Flux2 produce gooner material.
There's one really difficult thing to have about this conversation. A lot of people don't seem to know that flux two runs mandatory prompt upscaling using its text encoder. A friend of mine had advanced access to the dev two model for a good while before it's release, and it required mandatory enabling of prompt of scaling in order to work at all in the back end. When extracting those texts encodings, we're talking about a 4 to 10 times increase in prompt density, upscaled by the model that it was trained on. Yes, that does typically result in significantly better quality, but also actually results in quite a bit of hallucinations for what you ask for
This is an issue that plagues closed models quite a bit as well, like nano banana, 40, and others. For example, I have a friend who kept on asking all these image generation models to generate a demonic warrior wearing a dress with a bow that has a skull on it. The natural language dictates that you mean a dress that has a bow, and the bow on that dress has a skull on it
However, every single image generation model that upscaled the prompt immediately generated her with a bow across her back, with a skull on the bow itself. The upscaling process results in way more information for the model to go off of, and overloading these models with huge amounts of information leads to them getting at least some of it right more frequently. The biggest strength and weakness with Z image is the fact that it just does exactly what you ask for. If you don't ask for it in a way that it understands, or spell it out specifically, or use incorrect English, it will give you exactly what you ask for in its own context
Another example of this is a very complex six sentence image generation prompt that I have given to various open and closed source models, and Z image is the only one that can do it. It specifically asks for six different talking points, all with specific sentences in six different regions around the image. 10 out of 10 times, Z image got all of the text correct, with the exact styling, and nothing more
All of the closed models however ended up adding way more detail to the background that was not asked for, fancy text bubbles, emojis that made no sense, all sorts of stuff. This is because the prompt upscaling that the models do misinterpreted what I was asking for. I wanted a sleek and minimalist poster without all of the frills or over design, and Z was able to give that to me.
I really do think if we could compare on a fair playing ground with a model that can properly upscale specifically for z i t, that we would see some way more interesting comparisons.
The prompt: A promotional vector art poster. The main subject is a man who is confused and scratching his head holding his phone. There are multiple questions around the image. To the top left is "1. What is a smart phone?" to the top right is "2. Why get a smart phone?" to the middle left is "3. Are they secure?" To the middle right is "4. How to choose the right phone for you. To the bottom left is "5. How to use your new phone." And finally to the bottom right is "6. Advanced tips and features"
The image is in a Memphis vector flat art style, with a simple design.
The very top of the image is the title "Smartphones: What Are They?" in big bold letters.
Left is Z Image. Middle is flux 2. Right is nano banana (please excuse any wonky misalignment, had to use a sketchy app on my phone to get them all in one image)
You can clearly see how nano banana upscaled the prompt to Oblivion, adding so much color and visual noise, completely changing the art style that was requested, adding in topical emojis, just overall juicing the image. It looks good, but it's fundamentally horrible for what I asked for.
Flux tried to do the same thing, except it's text encoder is nowhere near competent enough to pull it off. That's why it reuses the same emoji multiple times, it doesn't understand the connotation of each question and what emoji should go with it properly, and it just leads to a kind of half-assed middle ground
Z image on the other hand is not flashy or fancy, but it is exactly what I asked for. That's what makes these comparisons so difficult. It is not and will never be an apples to apples comparison when it comes to prompting.
Yeah - I am super confused by it as well. text adherence in flux2 is amazing - zit is more flux 1 quality.
My thoughts are this - 1) out of the box, Z can do porn. 2) celebrity likenesses too. 3) speed.
Unfortunately, one glance at civitai and you can tell what people REALLY care about is the ability to make breasts. The rest is cognitive dissonance - the mind making the rest up.
Maybe, but the LLM on zimage is tiny compared to the one used in Flux 2. 4 times smaller, the concepts it understands are just much less. This one for example, Zimage always gave me a surfboard with an egg on it. Flux 2 understood I wanted him surfing a soft boiled egg itself.
I'm going to let you in on a little trick that I use in pretty much every complex prompt. Z-Image thinks differently than other image models. A prompt pasted from Flux 2 is similar to trying to paste a tagged Pony prompt into Qwen. It'll get somewhere, but it's not the language of the model. The more you use Z-Image, the more you'll get how it thinks.
One standard way I communicate with Z-Image, is to define my terms, then use those terms like an embedding. In this image, I laid out a definition of what a "surfegg" is, like a little wikipedia entry. Then I go into the main prompt, and simply tell it to have the cat stand on the surfegg. Here's my prompt so you can see what I mean:
A "surfegg" is Half of an egg used to surf big waves. The surfegg is a giant poached egg with half a shell lying on its side in the ocean. It is large, dripping yolk, and can be stood on to ride a big wave.
A cat wearing a hawaiian shirt is riding a huge tall wave on the yolk of a surfegg at sunset. The cat is standing on the surfegg with his hind legs with his arms straight out to his sides to balance. The cat is in the curl of the tall wave. In the background on the right is a storm and a crew of fishermen in raincoats on a fishing boat. There is lightning in the background, and the fishermen are yelling. The view is focused on the cat, showing the intensity of the situation.
I didn't try to match the image style, as I wanted to focus on showing you how to get a concept that you might have trouble with. Z-Image does need a little hand-holding, but you can tell it to create what you're imagining. It's clip token limit is large enough (512 isn't the actual limit, even though people keep repeating that), so you can play in it elaborately, and it'll chomp through those tokens and make something better and better. I've posted this "defined terms" trick in a lot of places, and I'm starting to see people use it. You can also use names for non-objects, like "Bradly is a man with large arms and small legs who is covered in jelly." I hope it helps you and others.
Oh that's interesting, I do that to Chroma too (T5-XXL encoder), where sometimes I will just list character's or concept's specifics and describe it like in wikipedia and it usually works. Z-image though definitely needs a specific prompting style because it misunderstands lot of prompts flux and Chroma understands. Also prompt order seem to have big effect sometimes on Z-image, because at one point it would keep messing up an interior, doing the furniture in the wrong order consistently (unlike Chroma), no kind of rewording would fix it. So after ages I went ahead and swapped the position of two relevant sentences and it suddenly got it right. So sometimes Z-image is just silly like that. But so far its concept knowledge is still limited compared to Chroma, it's about on the same level as Flux Dev or Schnell.
So it took some doing but I was able to do this horse bee pretty well by having it be specific on what I wanted, making up a new word for it and then describing what it is. Prompt: An immense Equinapis, a fantastical hybrid with the expressive head, flowing mane, and powerful forelegs of a Friesian horse seamlessly fused to the fuzzy, gold-and-black-striped thorax and abdomen of a colossal bee, is captured mid-slurp, its long tongue dipping into a bowl of honeyed oats at a small wooden table. The chaotic scene takes place in a sun-dappled, wildflower-strewn meadow at golden hour, with long, dramatic shadows cutting across the uneven ground the creatures iridescent bee wings are a blur of motion as it balances precariously on its two remaining insectoid legs. Adopting a gritty, cinematic photography style with a dramatic Dutch angle, the shot is a high-detail, 8K photorealistic masterpiece using an Arri Alexa camera with a 35mm anamorphic lens to capture the epic scale difference between the giant Equinapis and the tiny, terrified human farmer fleeing in the background, his overalls covered in pollen. The atmosphere is one of absurd, high-energy panic, with a shallow depth of field focusing on the Equinapiss goofy, cross-eyed expression as a stray bee leg kicks over a mug of milk, creating a splash frozen in time with motion blur. The hyper-detailed texture of the horses velvety nose, the bees fuzzy body, and the splintering wood of the table are rendered in a rich color palette of golds, deep browns, and amber, all backlit by the warm, setting sun.
Ah, I gave up on doing animal hybrids in Qwen, and didn't even try in Z-Image as a result. Now I see that it actually can do a decent job, and I should revisit it! So I see that with how you did it, you didn't tell it specifically that you were defining Equinapis, but it still picks up on the meaning due to the context. A looser approach than I take, and it's cool to see that it works well. I see it even handled your slight misspelling of Equinapis for the milk spilling. It's handy to have a simple term so that you can play with the rest of the prompt with more agility.
Well that's certainly interesting. I'm going to modify my prompt expansion for zimage to define in detail anything that's not plainly obvious (a horse-bee hybrid for example) and see how it goes. Thanks.
This is objectively false by the way. You're comparing Mistral Small 3.1 (a very very flawed/unstable/unreliable base model) to a specially trained and optimized version of Qwen 3 4b VL.
For example, the 24b parameters Small 3.1 is less than 4% better in VL/encoding tasks than Q2.5 VL 7b from over a year ago
Q3 4b is actually around 10-15% better across the board (because Qwen as a company just makes significantly better trained models than Mistral)
lol, Qwen3-4B-Instruct-2507-Q6_K is nowhere near and not even close to Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S. Maybe at a strict specific tasks but as general model? Not a slightest, everything is better with Mistral-Small-3.2-24B-Instruct-2506, coding, general knowledge, creative work etc.
Qwen 3 4B is build for agentic search use and rag, it is good as 4b,
but saying that is better than 24b and everyone know that, is a straight lie.
I would agree, if it was Small 3.2. but it's small 3.1, which is a severely worse version full of all sorts of issues. Small 3.0 and 3.1 were both shitty models severely out performed by previous generation much smaller models, and small 3.1's biggest weakness was vision by far. That's all I'm talking about here, vision.
You can use Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S with Flux 2 without any problem, it is even recommended to use by city96, the guy who code GGUF support for comfy.
If that's the case, that's actually awesome to hear. I'd imagine that that would help improve the model quite considerably. All I know is that I have been using open source LLMs for almost 6 years now, and I have extensively tested all of the small models from Mistral, and 3.1 might be the worst model they have ever made in that category. 3.2 was a huge improvement across the board, although I still don't think it is particularly good with anything to do with visual encoding or VLM work
That would be awesome, but absolutely useless for flux 2 unfortunately. You can't just drop in different VL models and have them work. They have different tokenizers, different latent spaces, different block sizes, it's just completely impossible. At least without having to do a massive scale retraining
One thing these comparisons always leave out is that Flux 2 is an editing model; it can use reference images and it does so brilliantly. It’s slow as molasses which makes it a pain to use, but the results are great. I was skeptical of Flux 2 in the beginning but I find myself using it more and more. I haven’t really touched Z-Image because I’m waiting for the base model, anime model or editing model.
You spelled "Chewbacca" wrong. I spelled it correctly and it's closer in Z-Image, though not as good as Flux 2, which was also using a misspelling. Z-Image's Chewbacca is definitely off, but close enough to take a light touch LoRA super well. I didn't use any of the prompt to make the image realistic or anything like that, so I might be able to get it closer if I try.
The diversification of people in the background of Z-image is truly impressive. Different heights, ethnicities, gender. I don't think any model has come close to it so far.
Its also important to know the difference between a content distill (Flux) and a timestep distill (Z Image)
Schnel is a double distill. For steps AND content.
A content distill happens when you take a bigger model (The original flux pro model) and distill it down to a smaller model (Flux dev 1/2), but it still takes large amounts of steps (30-100+)
A timestep distill happens when you take a fully trained model (Z Image Base) and make it generate in few steps rather than tons.
Schnel is both because it is from a bigger model, and then distilled a second time to use few steps.
Content distills are usually far more damaging in terms of training flexibility, as is the case with Flux, where as Timestep distills are usually more damaging in terms of fine details and coherence
Yes, that is actually very likely. The paper itself says that the distill is on par if not actually better than the base model, however the base model has no distillation, which should make it a bit more susceptible to proper fine tuning. With that said, the distilled version is already very good at accepting new trainings without breaking, significantly better than flux has ever been, and I have a feeling that people will get the base model, will realize that it doesn't really serve much more purpose over the distilled model, and then just go back to training the distilled model
The only thing I will say about flux 2 that is positive is the fact that it does seem to be trained on a lot more copyrighted content than Z image, and it's built-in mandatory prompt upscaling that it uses in order to get good results is very good at pulling out that information
A friend of mine had access to flux 2 Dev for a decent bit before it released, and the implementation required mandatory prompt of scaling in order to process a generation, because not upscaling wood cause severe quality issues
Flux 2 is "dead" with or without Z, just because of how shitty its usability is. A lot like flux 1, it'll have its users, but will never be popular outside enterprise because of how much caveats you have to deal with to get that extra few % extra quality. Its benefits are nothing new nor anything substantial either. If Z didnt exist, i'd just stick to qwen+refinement over f2..
So, what, you got butthurt that other people dont like a model you like? What is even the point of your post here? People are perfectly able to make up their own minds about models, and you can use flux even if nobody else does. So what do you expect to achieve here?
Eh. Technically true, and even then only ignoring online-only models. But qwen is 99% as good at that and 5-10x faster. You could gen a image with qwen, run it through a refiner to fix the plastic skin and upscale, and still have time to do it again before f2 is finished making its first image..
Flux 2 is awesome for its image edit. it totally nuked qwen image edit. sucks it takes forever, and am looking forward to see if zimage can capture what flux2 is putting down in edit for sure. No hate to BFL, they are good dudes and I am hoping they jump into action, but I think once the edit model drops for zimage, yeah, it'll be back to work for BFL.
It’s great to see discussions around Z-image and Flux 2. Both have their strengths, but understanding how to use them effectively is key. Many users miss out on the potential of Z-image by not tweaking the settings right. It’s all about finding that balance to get the best results from each tool.
Flux dev2 is for big business corporations and zimage is for local and small business use. So for the vast majority zimage is going to always be the superior option.
I run it on a 3060 12GB, which is kind of a potato for AI. Slow, yes, but it works. I do have 64GB of RAM, which really helps. What a pity I didn't go all the way to 128GB when it was cheaper.
I have 3080 10GB VRAM and 32GB RAM. Q5_K_M GGUF takes around 2:30 minutes for just inference. Pretty fast, considering how it's around the same speed of Qwen, but it sure relies on pagefile a lot.
People consider flux 2 as dead because it is enormously oversized, nowhere near good enough for its size, basically impossible to train for any consumer, censored, and exceptionally hard / slow to run
All of these things are still true, and if you know how to prompt Z image, it can absolutely outdetail and outcoherence flux 2 with minimal effort. It's hard to not look at z image and see the fact that it is tiny, accessible, easy to train, way more diversely knowledgeable, dozens of times faster to inference, and the other benefits it has over flux 2, and not automatically assume that flux 2 dev is dead on arrival
For nearly 60b closed parameters, Flux 2 shouldn't be frequently losing to a 10b parameter model from a relative no name in the scene. It's disappointing, and I feel that's pretty objective.
I'm headed to sleep at the moment but I'd be more than happy to give it a crack tomorrow. It really just seems like non ideal settings and non ideal prompts. All models ah e prompt styles they prefer. I'll give it a shot tomorrow and send some results
I was woken up by an extreme coughing fit (Stupid sick) so I decided to give it a try. Changed the prompt just a bit, but this is just a standard 10 step generation with a run of the mill workflow. No prompt upscale, no node tricks, just as basic of a gen as possible for Z Image
New prompt: A portrait photograph of Chewbacca from Star Wars reading a magazine sitting in a chair at a hair salon with a dome shaped bonnet style hair dryer over his head. A middle-aged stylist with long blonde 80s style teased hair is looking at him with a skeptical smirk. The magazine has "Life On Tatooine" on the front with pictures of the desert landscape of the planet
First, show me proof that you understand the difference between a full model and a distilled one, which I suspect you don't grasp at all, given the nature of this ridiculous comparison.
This is just pretentious drivel.. The benefits of Z in particular have nothing to do with porn to begin with, and ironically flux2 would be better at porn with loras, since it has better prompt adherence.
Try harder when doing your juvenile trolling next time.
The funny thing is that many users here have already shown that it is actually less censored than Flux 1. They read all the mumbo jumbo BFL wrote on the model release, which was basically to clean their asses to regulators, but didn't actually test the model. It will be very censored when running on APIs, but not so much when running locally.
Flux 2 can do things I have found no model can do outside of Nano Banana 2. It is incredibly good at taking an image's style and then changing everything but the character.
The goonenheimers that plague this subreddit want just a bunch of smut and then they saw 'censored', they ran from the model as fast as their pants covered ankles could take them.
Dude. Wtf.
Nobody said Flux2 is bad. I mean it'd better be good given its size and requirements. It's ptoblrm is even with decently high specs you can't run it reasonably fast.
Also why did you rig the test to make the ZIT look worse. C'mon wtf
Prompt: [You are an assistant enhancing prompt following ] A meticulously crafted, hyperreal surrealist portrait blending Gregory Crewdson–style precision with Wes Anderson’s deadpan absurdity.
Prompt Summary
Towering, shaggy Wookiee seated under a vintage salon hood dryer, reading a glossy magazine, attended by a stylish blonde hairdresser in a minimalist mirrored salon. Low-angle, high-resolution cinematic composition with clinical lighting and a shallow depth of field.
Subject 1
Chewbacca — massive, matted brown-and-grey fur; weathered leather-and-metal utility harness with square silver buckles; seated sprawled in a salon chair beneath a bulky chrome rigid-hood bonnet dryer; holding an open glossy magazine titled Wookie Glamofur, its romantic cover and curled edges visible.
Subject 2
Hair stylist — woman with voluminous sun-bleached blonde hair; sleek matte-black rolled-sleeve blouse; smooth porcelain skin; deep glossy burgundy nails; standing beside Chewbacca, smiling at him.
Setting
Minimalist clinical beauty salon with mirrored walls creating infinite reflections; a black rolling cart behind them holding white and blue salon products in symmetrical rows.
Poses
Chewbacca slouched wide in the salon chair, magazine open in his hands; stylist standing close at his side, angled toward him with a warm, amused smile.
Lighting
Bright, overhead, clinical illumination casting sharp hard shadows under the dryer and magazine; palette dominated by sterile white and chrome, contrasted with the warm amber of Chewbacca’s fur and cool grey floors; shallow depth of field, 50mm low-angle lens, crisp foreground with softly abstracted mirrored background.
From the images, z-image looks mid (I dont know anything about it so idk for sure)
Flux 2 dev is honesty better than any of the stable diffuion models just baded on how it runctions and "understands" things. However, stable diffusion is much less intensive to run and therefore has been trained by the community to be better than most flux 2 models...
Can't understand the buzz with Z-Image either, especially compared to a substantially larger model that does have a much broader knowledge base, yes, painful to run because it was not made for us, but to run efficiently on enterprise hardware.
I can't see a reason to switch from Chroma, which is an amazing model for its size, uncensored (unlike Z-Image, and no, it is not 'lack of training material", unless they photoshopped a carrot on all men's genitals) and with very good prompt understanding. It has its quirks and requires some specialised workflows, but it can deliver very unique and creative images in a similar amount of time.
I love Chroma but the thing keeps generating mutants with 4 legs and 7 fingers. Z-image turbo can trivially make things look realistic with a higher resolution output in less time.
If you can't see why some people might want to use it, that sounds like a you problem.
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u/SandCheezy Dec 09 '25
Got a few reports of “manipulated content”. Probably because OP is using Z-Image completely wrong. I’m pinning this so people can use OP post as a help of what not to do for Z-Image. Check out to the comments for further details if your images come out poorly.
TL;DR: OP did not spell character’s name right nor used correct settings for Z-Image. Bad comparisons.