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CyberRealistic Z-Image Turbo is a realism-focused finetune of Z-Image Turbo by Tongyi-MAI.
The idea behind it is deliberately simple: keep what makes Z-Image Turbo good - speed, strong prompt understanding, good composition and extremely efficient few-step generation - while moving the default visual language further toward believable photography.
CyberRealistic doesn't try to turn Z-Image Turbo into a completely different model. The original already has a very capable photographic foundation. The finetune mainly changes what the model considers a "normal" photograph: more natural skin, less synthetic rendering, more believable faces, stronger material texture, more grounded lighting and better anatomical consistency.
Z-Image Turbo already knows how to make a good image. CyberRealistic mainly changes where it starts.
What's different from base Z-Image Turbo
Stronger photographic look out of the box.
More natural skin texture with less waxy or overly polished rendering.
Improved faces, eyes, hair and small facial details.
Better anatomical consistency, especially hands, feet and complex poses.
Fewer duplicated limbs and extra hands in more difficult compositions.
More believable fabric, hair, skin, metal, glass and other material textures.
Stronger response to available light, practical lighting and real-world camera language.
Less dependence on stacks of words like
masterpiece,8k,ultra detailedandphotorealistic.Keeps the speed and general prompt behavior that make Z-Image Turbo useful.
The focus is photography, but that doesn't mean the model is locked to photography. Illustration, cinematic stylization, fantasy, advertising, vintage photography and other looks are still available when you describe them.
Prompting
If you're coming from SDXL, Pony or Illustrious, the biggest change is simple:
Describe the image instead of building a tag stack.
Z-Image Turbo uses a Qwen3-based text encoder and responds very well to normal descriptive language. Short comma-separated clauses are completely fine, but every part of the prompt should ideally tell the model something visual.
Instead of:
woman, realistic, masterpiece, best quality, detailed skin, cinematic, 8k
try:
A woman standing beside an open apartment window on a warm summer evening, photographed with soft natural light falling across her face, loose dark hair, natural skin texture and an out-of-focus city street behind her.
The second prompt gives the model an actual scene to construct.
Put the subject first
Start with what the image is about.
A middle-aged mechanic leaning over the open engine bay of an old red pickup truck...
works better than hiding the subject halfway through a long list of style instructions.
You don't need to obsess over exact prompt order, but the main subject and composition should be clear early.
Be specific
Specific visual language usually does more than generic quality words.
Instead of:
beautiful lighting
try:
soft late-afternoon sunlight entering through a dusty workshop window
Instead of:
detailed clothing
try:
a faded blue denim jacket with worn seams and slightly frayed cuffs
Instead of:
cinematic portrait
try:
photographed from chest height with a 50mm lens, shallow depth of field and soft window light from camera left
Describe the light
Lighting is one of the easiest ways to change the realism and mood of the image.
Useful examples:
soft overcast daylight
direct midday sunlight creating hard shadows
a single warm tungsten lamp above the table
cold fluorescent supermarket lighting
late-afternoon sunlight entering through venetian blinds
direct on-camera flash in a dark room
You can still use words like cinematic, but describing where the light actually comes from gives the model much more information.
Quality tags are not magic switches
Words such as:
masterpiece
best quality
8k
ultra detailed
absurdres
score_9
can still influence the wording of the prompt, but Z-Image Turbo doesn't treat them like the traditional SDXL/Pony quality system.
Use that prompt space to describe what you actually want to see.
Camera language works well
For photographic images, camera terminology can be useful when it describes a visible effect:
35mm documentary photograph
85mm portrait lens with shallow depth of field
handheld photograph with slight motion blur
direct flash snapshot
wide-angle environmental portrait
medium-format color photograph
Don't feel forced to specify a camera and lens in every prompt. Sometimes simply saying casual phone photo gives you exactly the look you need.
Prompt length
There is no perfect prompt length, but these are useful practical ranges:
10–30 words: exploration and seed hunting.
30–80 words: good balance between control and freedom.
80–150 words: complex scenes, precise lighting or detailed compositions.
Long prompts aren't automatically better. Contradictory prompts are the bigger problem.
If you ask for soft natural window light, hard direct flash, deep cinematic shadows and flat commercial studio lighting at the same time, the model still has to decide which instruction wins.
Text inside images
Z-Image Turbo is unusually capable at rendering text compared with older diffusion models.
If exact text matters, put it in quotation marks:
A small neon sign above the diner entrance reading "OPEN ALL NIGHT"
Keep important text reasonably short. It's good, but it still isn't a replacement for a typography application.
Recommended settings
Z-Image Turbo is a distilled few-step model.
Don't treat it like an SDXL checkpoint that needs 30–50 steps.
A good starting point is:
Steps: 8–9
CFG / Guidance: effectively OFF
Resolution: start around 1 megapixel and increase if your hardware allows it
Negative prompt: normally unnecessary
In the original Diffusers implementation, guidance is 0.0.
In standard ComfyUI workflows, the equivalent no-CFG setup is generally CFG 1.0.
More steps are not automatically better with Turbo. If something isn't working, changing the prompt, seed, sampler or composition usually makes more sense than simply increasing the step count.
ComfyUI
For ComfyUI, I recommend starting with the current Z-Image Turbo workflow/template rather than applying old SDXL settings.
Z-Image Turbo has its own sampling behavior and is designed around very low step counts.
Negative conditioning is normally zeroed out in the standard Turbo workflow because the model runs without traditional classifier-free guidance.
Example prompts
Natural-light portrait
A woman in her early thirties sitting beside an open café window, loose brown hair falling across one side of her face, wearing a simple cream-colored sweater. Photographed from slightly below eye level with a 50mm lens, soft overcast daylight entering from the window, natural skin texture, muted colors and a busy street softly blurred in the background.
Documentary photography
An elderly fishmonger arranging silver mackerel on crushed ice at an indoor market early in the morning. Cold daylight enters through the open market doors and mixes with the warm bulbs above the counter. Wet concrete floor, weathered hands, faded rubber apron, handheld 35mm documentary photograph with subtle grain and natural color.
Low-light snapshot
A young woman standing alone beside a vending machine outside a convenience store at two in the morning, photographed with direct on-camera flash. Dark parking lot behind her, slightly messy hair, casual oversized jacket, realistic skin texture, hard flash shadows, muted colors and the imperfect look of a spontaneous late-night photograph.
A few last things
Short prompts are completely valid.
One of the advantages of Turbo is that you can generate several directions quickly, choose the seed or composition you like, and then add more camera, lighting and material detail.
That often works better than trying to write the perfect 150-word prompt before generating anything.
Also keep in mind that Z-Image Turbo is distilled for speed. Part of that tradeoff is lower variation than a large non-distilled foundation model. If you keep seeing the same interpretation, change the wording more substantially rather than adding another five quality tags.
CyberRealistic Z-Image Turbo is released for people who enjoy generating, experimenting, benchmarking and finding the edges of a model.
Feedback is especially useful for difficult poses, multiple people, hands and feet, unusual lighting, text rendering and prompts where the model behaves differently from the original Z-Image Turbo.
If you find something interesting — good or bad — let me know.
Credits
CyberRealistic Z-Image Turbo is based on Z-Image Turbo by Tongyi-MAI.
Z-Image Turbo is released under the Apache 2.0 License. Please follow the applicable upstream license when using or redistributing derived models.
Description
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V5 refines what V4.0 started. Better details, sharper colours, more consistent output, and stronger NSFW content across the board.
This release adds 110+ new sample images, covering a wider range of styles and scenarios. All images were generated with the Cyber Z-Image Turbo Workflow v4.1, available in the Optional Files section.
FAQ
Comments (47)
Try Qwen3-4b-Z-Image-Engineer-V4-F16 by BennyDaBall: https://huggingface.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4/tree/main/safetensors
@vicaut looks promising. Will test it.
thanks. will test it, too.
what's this for? what does it do? how does it compare to joshepe?
how does one make the two files work? i has 1 of 2 and 2 of 2.
@Melodic_Possible_582589 use one of these https://huggingface.co/ApacheOne/Qwen3-4b-Z-Image-Engineer-V4-NVFP4/tree/main
@Melodic_Possible_582589 i used the gguf Q8 version https://huggingface.co/BennyDaBall/Qwen3-4b-Z-Image-Engineer-V4/tree/main
Thank you for your contribution to our community. hands on
@Melodic_Possible_582589 Join them with https://github.com/soursilver/safetensors-merger
@Cyberdelia Try also Euler ancestral combined with FlowMatch Euler Discrete Scheduler. This is my all time favoutite combination for z image turbo.
@vicaut Ah, I thought it was a text encoder. I use a custom text encoder for this myself. And for creating Z-Image prompts, I also use a different system. This is fine in itself - I was just a bit confused about what it actually was. It was also very late last night :)
@Cyberdelia Well, it is? Z-Engineer V4 is a fully fine-tuned version of the text encoder from Tongyi-MAI/Z-Image-Turbo. It's been specifically trained to understand the nuances of AI Image Generation workflows.
It excels at:
Expanding Concepts: Turn "sad robot in rain" into a cinematic fever dream with chromatic aberration, shallow depth of field, and a melancholic color grade that would make Blade Runner jealous.
Technical Precision: It knows the difference between an 85mm portrait lens and a 24mm wide—and will use them appropriately. Lighting? Rembrandt, split, volumetric fog? It's got opinions.
Stylistic Consistency: It writes with a creative voice, not that robotic "hyperrealistic, 8k, trending on artstation" energy.
@Melodic_Possible_582589 It is a better, fune-tuned, text encoder.
@vicaut Aha, so it is a text encoder after all :) Then I’ll compare it with my own text encoder.
@vicaut thanks for the info. I will have to try it again. When I compared it to the josiefied version the z-engineer couldn't do penetration in some prompts. I also used both the both the 8 and 16 bit version of z-engineer in LM studio and it couldn't really produce good zimage style prompts. I was able to reach the realism generations because I used Cyberdelia's prompt on chatgpt, but that doesn't allow nsfw, so I describe the character on cyberdelia's program and describe the nsfw stuff using LM studio. I have not used lm studio for awhile now.
@vicaut @Beezer79 @Melodic_Possible_582589 I've updated my workflow and created a new ComfyUI node based on BennyDaBall930's original ComfyUI-Z-Engineer.
https://civitai.red/models/2532359/cyberrealistic-z-image-turbo-comfyui-workflow?modelVersionId=2957140
any GGUF version 🥲?
@Tofu080 I made an open source easy-to-use program with low RAM usage so you can make your own GGUF conversions: https://github.com/qskousen/ggufy
@ferretduck yes, works perfect!
Pretend i am an imbecile -not with too much enthusiasm though!- but why do you want gguf?
@dillion1920 good question and I have no idea!
@dillion1920 because i dont have enought vram for it, in order for it to work i need to offload to my system Ram. i found this tool and it did great job https://github.com/SlaveOfGod1/ggufy
@Tofu080 Ah got it.
@Tofu080 wow, interesting! i didn't know anyone had forked ggufy. it seems somewhat limited so far, but interesting that they tried to rewrite it in python
@ferretduck What are the minimum specs for doing this?
@dsanatlar you just need a CPU and a couple gigabytes of RAM, depending on the model you want to convert.
Any difference in input betwen fb8 and bf16?
bf16 has higher quality because it's less compressed.
Check please the checkpoint on Civit generator, it gives only digital noise for some reason.
I didn't know that it was possible to use it in the generator. This is a new thing, and I don't know why it doesn't work. Let me check with Civitai about this.
same
FP16 needed.
from SDXL, cyberrealistic is a details machine, but anything else? it depends..
Kissing your hands, maestro. Works brilliant (currently testing on ref images, gonna try with my custom lora once trained) 🤌🤌🤌
V5 is absolute CINEMA
This version 5 is the best z-image-turbo model so far.
This is a massive improvement over the default model (which already looked great). Really nice composition and characters.
It does not work right on civitai, FYI
Something is ruining many pictures across all ZIT models, until a resourceful creator can fix it with a LoRA. Whenever a woman stands full frontal to the camera, her pussy crack reaches too high. A few seconds watching real photos (in sites like www.metarthunter.com or similar) will very quickly make you see the problem as I see it.
Another weak point of ZIT is nipples that disappear behind even a light blouse or t-shirt, instead of being partially noticed.
What about non-turbo version ? I can't live without negative ..
Haha, I know you like to stay on the negative side of life. 😉
There is a Base version, but honestly I haven’t had great results with it so far. That’s why I’m mainly focusing on Turbo right now. For me it gives better results and is much more fun to work with.
Is there already a good way to train a LoRA specifically for this? When using LoRAs that I trained for z-image base, the results are underwhelming. I have also tested inference with the catalyst model and I have not found it to be performing much better.
Maybe someone made a training-adapter so we can train LoRAs directly on this checkpoint?
Other than difficulties with LoRAs the V5 model is absolutely insane. Thank you for your work. I greatly appreciate what you do.
It works, but not on Civitai for some reason. A pity, because the model is very nice.
The model is really good, but there are exaggerations of color in it, I mean that the color is greatly overestimated for realism, in general, I liked the model, and the textures and understands the norm of promt. I would like to see a little less bright contrasting colors in the next update so that the model can draw a little more realistically. I use dpmpp_2s_ancestral+ (beta57/bong_tangent) or res_2s+ (beta57/bong_tangent) for realism.
Maybe you like my other ZIT model more:
https://civitai.red/models/2513307/cyberrealistic-z-image-turbo-catalyst
@Cyberdelia Unfortunately, I didn't like the Catalyst model.
@soyv4 This weekend I will release V6.0 -> The color volume has been slightly toned down with some other improvements
Best nsfw/female model by far 10/10
Details
Files
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ae.safetensors
ae.safetensors
Vae-flux.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
zImageBase_vae.safetensors
ae.safetensors
ae.safetensors
z-image-vae.safetensors
diffusion_pytorch_model.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
z_image_vae.safetensors
vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
fluxvae.safetensors
ae.safetensors
zimage.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux-vae-fp32.safetensors
variational_encoder_primary.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
zImageTurbo_vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
zImage_vae.safetensors
TEXT_TO_IMAGE_ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux1-vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae (1).safetensors
flux_vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux_kontext_ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux_vae.safetensors
zimageae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
Zimage-vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
variational_encoder_primary.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae_zimgturbo.safetensors
flux_vae.safetensors
ae.safetensors
ae.safetensors
flux_vae.safetensors
z_image_vae.safetensors
ae.safetensors
ae.safetensors
FLUX.1-dev-vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
variational_encoder_primary.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
z_image_vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux1-dev-ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
vae.safetensors
alexa.flux.safetensors
ae.safetensors
ae.safetensors
flux1vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
zit_vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux1dev-ae.safetensors
ae.safetensors
ae.safetensors
flux1-dev-ae.safetensors
VAE_ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae z image turbo.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux1-dev-ae.safetensors
ae.safetensors
flux2vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
zImage_vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
vae.safetensors
ae.safetensors
zimage_vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
z_image_base_vae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux_kontext_ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux_vae.safetensors
zImageBase_vae.safetensors
zImageTurbo_vae.safetensors
flux1d_vae.safetensors
ae.safetensors
ae.safetensors
zImageTurbo_vae.safetensors
flux1-dev-ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux-vae-dev.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
hidreami1.1vae_ae.safetensors
hidreami1vae_ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
vae_ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux-vae.safetensors
ae.safetensors
ae.safetensors
ae (1).safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
flux_ae.safetensors
ae.safetensors
ae.safetensors
flux1-ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae_zimgturbo_fp16.safetensors
aeSft_v10.safetensors
ae_zimgturbo.safetensors
ae_zimgturbo_2868325.safetensors
ae.safetensors
ae.safetensors
fluxVaeSft_aeSft.sft
vae.safetensors
ae.safetensors
ae.safetensors
aeSft_v10.sft
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae_zimgturbo.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
ae.safetensors
cyberrealisticZImage_v50_txt.safetensors
Mirrors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen3-4b.safetensors
qwen.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b_bf16.safetensors
qwen_3_4b_bf16.safetensors
qwen_3_4b_bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_merged_text_encoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b.safetensors
Z-Image_qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
model.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImage_textEncoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
text_encoder-qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
Z-Image_qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b (1).safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b.safetensors
TEXT_TO_IMAGE_qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
text_encoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
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qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
model-00003-of-00003.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_turbo_txt.safetensors
qwen_3_4b.safetensors
qwen3-4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
zImageTurbo_turbo_txt.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
qwen_3_4b-bf16.safetensors
qwen_3_4b.safetensors
cyberrealisticZImage_v50_txt.safetensors
qwen_3_4b.safetensors
cyberrealisticZImage_v50_txt.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen3-4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
Text_encoders_qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
zImageTurbo_textEncoder.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen_3_4b.safetensors
qwen3_4b_2964436.safetensors
cyberrealisticZImage_v10_txt.safetensors
cyberrealisticZImage_v20NSFW_txt.safetensors
unrealvisionZITPhotoreal_universal_txt.safetensors
926Custom3JustAZIT_v10_txt.safetensors
zImageTurbo_turbo_txt.safetensors
cyberrealisticZImage_v40_txt.safetensors
cyberrealisticZImage_v50_txt.safetensors
cyberrealisticZImage_v60_txt.safetensors
cyberrealisticZImage_v70_txt.safetensors
cielbleuZIT_v2_txt.safetensors
juggernautZ_v10ByRundiffusion_txt.safetensors
cielbleuZIT_v1_txt.safetensors
zAnime_textEncoder_full_bf16.safetensors












