ZImage DPO “AGILE” Now Uploading 08/03/2026 女神节祝福🌸
On this Day, may every woman feel the strength, grace, and boundless potential that lives within her. Thank you for your courage, your kindness, your resilience, and the countless ways you make the world brighter and better. Here's to equality, empowerment, joy, and endless possibilities—today and every day. Happy International Women's Day🌸
ZIDistilled FUN “AGILE” Now Released
Special thanks to the VideoXFUN team for releasing the groundbreaking Zimage Distilled Adapter 2603. By incorporating this latest update, AGILE achieves a refined balance between speed, diversity, and visual richness — unlocking more creative freedom while maintaining exceptional efficiency.
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Agility in Motion, Diversity in Depth
The brand-new ZImage FUN “AGILE” is built upon the cutting-edge ZIB acceleration framework. We deliberately reduced DPO & Distilled weight to preserve greater stochastic freedom, combined with the most recent training datasets, resulting in dramatically increased output variety, richer content details, and more imaginative compositions without sacrificing core stability.
For the first time, AGILE reaches a true quality parity challenge against the flagship “ZImage TURBO” in terms of overall image fidelity and sharpness — even in complex scenes — while delivering faster iteration and superior responsiveness.
Key highlights:
True ZIT-level unlocked: Photorealistic lighting, textures, and material rendering that now rivals or approaches ZImage TURBO quality, even at standard step counts.
Enhanced diversity & content richness:DPO + Newest datasets = more varied poses, styles, atmospheres, intricate details, and unexpected creative sparks in every generation.
ZIB ecosystem ignition: Exceptional native compatibility with ZIB-series LoRAs; your existing and future LoRAs now align faster, reproduce more faithfully, and shine brighter than ever before — officially kicking off the full ZIB LoRA era.
Agile workflows: Seamless hybrid use with Klein 9B for refinement, ensemble boosting, or rapid prototyping; near-instant LoRA response with preserved high-entropy creativity.
Every generation is a step toward freer, bolder imagination.
Z-Image-Distilled DPO “Veris” 02/26/2026
ZImage DPO “Veris” Now Released
Special thanks to @Fok for providing the Flow-DPO technical adaptation. By skillfully integrating the training philosophy of Direct Preference Optimization (DPO) into the distillation weights, the Zimage distilled model achieves a major leap in lighting, color fidelity, and material authenticity — more natural light & shadow, more believable colors, and details that hold up under scrutiny.
more case in: RedCraft | 红潮 | RedZDX⚡️Distilled [Civitai ]
The following example shows a comparison between ZIT and Flow DPO, intended to illustrate the effect of DPO, rather than a direct demonstration of ZIB Distilled
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Speed of Truth, Fidelity of Flow
真实且极速，用忠诚在流动
The all-new ZI DPO “Veris” is powered by the latest-generation ZIB acceleration engine. Building on the RedZDX training data, we further distilled a more efficient, more refined Zimage-based model.
Now — solid, highly realistic generations in just 8 steps.（Better LoRAs alignment）
仅需8步即可生成更有层次感、高度逼真的图像。（LoRa对齐效果更佳）
Key highlights:
Realism-first prototyping — near-zero latency for LoRAs, with lighting and color already very close to final training targets
Hybrid realism workflows — seamless integration with Klein 9B for cascaded refinement or ensemble boosting, pushing visual fidelity and consistency even higher
Every step toward truth deserves full commitment.
欢迎体验 ZI DPO “Veris” ——您的LoRa训练结果不再只是“相似”，而是真正得到“复现”。
Welcome to experience ZImage DPO “Veris” — where your LoRAs generations are no longer just “similar”, but truly are.
Additionally, I've exported Redcraft DX3 ZIB Distilled LoRA in Rank-256 format. The LoRA weight can be adjusted to adapt it to various ZIB fine-tune models, fully compatible with the Z-Image(non-turbo) base model.
Speed of Light, Power of Flow: The new ZID v3 "Lucis" is powered by the latest ZIB acceleration. Building on ZID v2 trainning sets, we've distilled a more efficient Zimage-based RedDX3. Now, in just 5 steps, you get solid results.
Rapid Prototyping: Test LoRA training hypotheses instantly with 'near-zero' latency.
Stochastic Pre-sampling: Serve as a high-speed, high-entropy source for ZiTurbo pipelines.
Hybrid Workflows: Pair seamlessly with Klein 9B for cascaded refinement or ensemble generation.
inference cfg: 1.0-1.5（建议1.0）
inference steps: 5（5-15步）
sampler / scheduler: Euler / simple
Preview images generated by Z-Image Distilled V3+Moody MIX V7(ZIT finetune) Hybrid Workflow，Just for showing the style difference between ZID(RedZDX3) and ZIT(fine-tunning),
Welcome to the era of instant creativity. Welcome to 'Lucis'.
Z-Image-Distilled V2 2026/2/05
To a certain extent, the problem of ZImage color deviation has been reduced, but it is recommended to adjust the color appropriately according to the art style
Also available in NVFP4 quantized format, optimized for acceleration on Blackwell architecture GPUs.Double speed, Half resources.（ like RTX50XX, PRO6000, B200, and others ）
Also supports non-50 series GPUs (automatic 16-bit operation)
The art style leans towards realism
Retains ZIB's creative ability and reduces the collapse of Human anatomy.
Thanks to @anyMODE（Civitai） for exporting ZID LoRAs
Z-Image-Distilled V1 2026/1/30
This model is a direct distillation-accelerated version based on the original Z-Image (non-Turbo) source. Its purpose is to test LoRA training effects on the Z-Image (non-turbo) version while significantly improving inference/test speed. The model does not incorporate any weights or style from Z-Image-Turbo at all — it is a pure-blood version based purely on Z-Image, effectively retaining the original Z-Image's adaptability, random diversity in outputs, and overall image style.
Compared to the official Z-Image, inference is much faster (good results achievable in just 10–20 steps); compared to the official Z-Image-Turbo, this model preserves stronger diversity, better LoRA compatibility, and greater fine-tuning potential, though it is slightly slower than Turbo (still far faster than the original Z-Image's 28–50 steps).
The model is mainly suitable for:
Users who want to train/test LoRAs on the Z-Image non-Turbo base
Scenarios needing faster generation than the original without sacrificing too much diversity and stylistic freedom
Artistic, illustration, concept design, and other generation tasks that require a certain level of randomness and style variety
Compatible with ComfyUI inference (layer prefix == model.diffusion_model)
Usage Instructions:
Basic workflow: please refer to the Z-Image-Turbo official workflow (fully compatible with the official Z-Image-Turbo workflow)
The distillation process causes some damage to text (especially very small-sized text), with rendering clarity and completeness inferior to the original Z-Image
Overall color tone remains consistent with the original ZI, but certain samplers can produce color cast issues (particularly noticeable excessive blue tint)
Next optimization directions:
Further stabilize generation quality under CFG=1 within 10 steps or fewer, striving to achieve more usable results that are closer to the original style even at very low step counts
Optimize negative prompt adherence when CFG > 1, improving control over negative descriptions and reducing interference from unwanted elements
Continue improving clarity and readability in small text areas while maintaining the speed advantages brought by distillation
We welcome feedback and generated examples from all users — let's collaborate to advance this pure-blood acceleration direction!
Model License:
Please follow the Apache-2.0 license of the Z-Image model.
Please follow the Apache-2.0 open source license for the Z-Image model.
