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BennyDaBall
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Z-Image-Engineer-V6

Text Generation
Transformers
Safetensors
English
qwen3
prompt-engineering
image-generation
z-image
z-image-turbo
text-encoder
comfyui
lm-studio
conversational
text-generation-inference
Model card Files
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  2. HASHES.sha256 +19 -0
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  7. Z-Image-Engineer-V6-MXFP4.gguf +3 -0
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  21. tokenizer.json +3 -0
  22. tokenizer_config.json +30 -0
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ base_model:
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+ - Tongyi-MAI/Z-Image-Turbo
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ tags:
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+ - text-generation
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+ - prompt-engineering
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+ - image-generation
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+ - z-image
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+ - z-image-turbo
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+ - qwen3
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+ - gguf
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+ - text-encoder
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+ - comfyui
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+ - lm-studio
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+ - conversational
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+ ---
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+
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+ # Z-Image-Engineer V6 (4B)
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+
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+ ## Model Metadata
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+
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+ | Key | Value |
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+ |---|---|
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+ | **License** | Apache-2.0 |
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+ | **Language** | English (`en`) |
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+ | **Base Model** | `Tongyi-MAI/Z-Image-Turbo` |
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+ | **Library** | `transformers` |
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+ | **Pipeline Tag** | `text-generation` |
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+ | **Format** | GGUF, HF Safetensors |
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+
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+ ---
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+
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+ The **Z-Engineer** returns, fully rebuilt around the **SMART DoRA** training system for Z-Image Turbo.
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+
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+ Yes, we jump from V4 to V6. Unlike the usual guy math, this one actually brought the extra two inches.
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+
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+ **Z-Image-Engineer V6** is a fine-tuned 4B Qwen text encoder (`Tongyi-MAI/Z-Image-Turbo`) optimized for dual-role performance: a local prompt-enhancement model for LM Studio, and a direct drop-in replacement text encoder for ComfyUI.
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+
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+ ![Z-Image-Engineer V6 simple A/B with rewrites](evidence/gallery_z_image_engineer_v6_simple_ab_with_rewrites_CONTACT.png)
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+
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+ ---
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+
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+ ## What is Z-Image-Engineer V6?
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+
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+ V6 transforms minimal seed prompts into rich, highly structured visual narratives. It adds explicit scene composition, lighting direction, material texture, and depth separation while stripping out empty prompt sludge like *"8k, masterpiece, trending on ArtStation."*
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+
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+ It can also be used directly as a Z-Image text encoder. Drop the GGUF into ComfyUI, load it with `CLIPLoaderGGUF`, set the type to `lumina2`, and compare it against the stock `qwen_3_4b.safetensors`.
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+
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+ ### Key Use Cases
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+
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+ - **Prompt Enhancement:** Upgrade simple concepts into descriptive, high-fidelity visual prompts locally.
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+ - **Text Encoder Swap:** Replace the stock Z-Image Qwen text encoder in ComfyUI to generate different conditioning from the same seed.
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+ - **Hybrid Mode:** Use V6 to rewrite your prompt, then use V6 again to encode it. It writes the scene and drives the image model.
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+ - **Private Local Workflow:** Built for LM Studio, ComfyUI, and `llama.cpp`. No API logs, no external telemetry.
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+
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+ ---
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+
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+ ## Under the Hood: SMART DoRA
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+
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+ V4 pioneered SMART training. V6 adapts that system into a **Weight-Decomposed Low-Rank Adaptation (DoRA)** framework.
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+
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+ DoRA provides surgical adapter updates by decoupling directional and magnitude adjustments. SMART adds auxiliary pressure so the model does not collapse into repetitive prompt loops or superficial sentence patterns.
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+
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+ | Regularizer | What it Does | Why it Matters |
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+ |---|---|---|
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+ | **Entropic** | Broadens output probability diversity. | Reduces repetitive loops and generic vocabulary. |
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+ | **Holographic** | Enforces structured, depth-wise feature logic. | Improves foreground/background hierarchy. |
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+ | **Topological** | Stabilizes coherent latent trajectories. | Keeps prompts flowing naturally instead of stalling out. |
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+ | **Manifold** | Regulates overall weight distributions. | Keeps model behavior stable under high-pressure refinement. |
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+
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+ ### The Refinement Pipeline
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+
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+ V6 was not a simple one-and-done training run. The final architecture is a blended composite:
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+
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+ 1. **Base Pass:** Master-corpus SMART DoRA training on the native Z-Image Turbo text encoder.
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+ 2. **Retention Pass:** Preservation pressure for numbers, color accuracy, text signage, named objects, actions, and spatial tracking.
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+ 3. **SceneClean SFT32:** Supervised refinement to restore the cinematic V4/base-V6 voice.
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+ 4. **AntiRepeat Binary24:** Binary anti-repeat refinement to reduce loops, abrupt fragments, and bad endings.
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+ 5. **Final Blend:** A 25% style-restoration / 75% anti-repeat DoRA adapter blend, balancing vivid descriptions with tighter syntax.
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+
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+ ---
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+
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+ ## Quick Start
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+
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+ ### LM Studio: Prompt Enhancement
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+
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+ Download your preferred GGUF quant, load the model, and prompt it directly. No complex system prompt is required.
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+
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+ ```text
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+ Enhance this image prompt for Z-Image Turbo: a unicorn
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+ ```
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+
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+ The comparison examples were generated from direct LM Studio user requests like this, with no separate system prompt. `V6_SYSTEM_PROMPT.md` is included only as an optional preset for people who want a stricter prompt-only chat setup.
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+
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+ ### ComfyUI: Direct Encoder Swap
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+
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+ 1. Place the GGUF file into `ComfyUI/models/text_encoders/`.
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+ 2. Add a `CLIPLoaderGGUF` node.
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+ 3. Set model type to `lumina2`.
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+ 4. Use it where the stock Z-Image Qwen text encoder would normally go.
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+
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+ Optional workflow repo:
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+
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+ - [ComfyUI-Z-Engineer](https://github.com/BennyDaBall930/ComfyUI-Z-Engineer)
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+
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+ The raw GGUF works without the node.
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+
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+ ### Verified Image Settings
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+
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+ ```text
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+ UNET: z_image_turbo_bf16.safetensors
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+ VAE: ae.safetensors
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+ Text Encoder: Z-Image-Engineer-V6-Q8_0.gguf
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+ Resolution: 1024x1024
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+ Steps: 8
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+ CFG: 1.0
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+ Sampler: res_multistep
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+ Scheduler: simple
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+ Shift: 3.0
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+ ```
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+
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+ ---
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+
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+ ## Training Specifics
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+
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+ | Parameter | Specification |
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+ |---|---|
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+ | **Base Text Encoder** | `Tongyi-MAI/Z-Image-Turbo/text_encoder` |
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+ | **Tokenizer** | `Tongyi-MAI/Z-Image-Turbo/tokenizer` |
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+ | **Method** | SMART DoRA / PEFT Adapter Training |
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+ | **Rank / Alpha / Dropout** | 64 / 64 / 0.03 |
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+ | **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `down_proj`, `up_proj` |
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+ | **Refinement Stack** | Supervised Style SFT + Binary Anti-Repeat |
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+ | **Final Packaging** | Merged HF safetensors + full GGUF ladder |
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+
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+ ---
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+
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+ ## GGUF Quantization Ladder
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+
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+ All weights are locally hashed. Full recursive validation hashes are in `HASHES.sha256`.
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+
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+ | Filename | Size | Target Use Case |
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+ |---|---:|---|
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+ | `Z-Image-Engineer-V6-F16.gguf` | 7.498 GiB | Full precision reference. |
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+ | `Z-Image-Engineer-V6-Q8_0.gguf` | 3.986 GiB | Near-lossless; used for local A/B testing. |
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+ | `Z-Image-Engineer-V6-Q6_K.gguf` | 3.079 GiB | High-fidelity balanced footprint. |
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+ | `Z-Image-Engineer-V6-Q5_K_M.gguf` | 2.697 GiB | Daily-driver performance-to-size ratio. |
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+ | `Z-Image-Engineer-V6-Q4_K_M.gguf` | 2.331 GiB | Reliable 4-bit standard. |
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+ | `Z-Image-Engineer-V6-Q3_K_M.gguf` | 1.933 GiB | Lightweight option for tighter setups. |
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+ | `Z-Image-Engineer-V6-MXFP4.gguf` | 2.101 GiB | Alternative compact quantization. |
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+
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+ ---
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+
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+ ## Verification & Proof
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+
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+ The bundled comparison image is:
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+
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+ ```text
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+ evidence/gallery_z_image_engineer_v6_simple_ab_with_rewrites_CONTACT.png
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+ ```
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+
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+ It compares foundational prompts across four isolated control paths:
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+
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+ 1. Stock Encoder + Raw Prompt
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+ 2. V6 Encoder + Raw Prompt
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+ 3. Stock Encoder + V6 LM Studio Rewrite
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+ 4. V6 Encoder + V6 LM Studio Rewrite
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+
174
+ ---
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+
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+ ## Disclaimer & Acknowledgements
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+
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+ This model is a prompt engineer and text encoder. Diffusion is still diffusion; structural expansion improves compositional adherence, but it does not mathematically guarantee a perfect seed every single time. Use creative judgment locally.
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+
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+ - **Tongyi-MAI** for the Z-Image Turbo ecosystem.
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+ - **Qwen** for the adaptable text encoder backbone.
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+ - The open-source maintainers behind **LM Studio**, **ComfyUI**, **llama.cpp**, **PEFT**, and **Transformers**.
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+ - My local power utility provider, for sustaining the research grid.
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+
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+ **Built & trained locally with care by BennyDaBall.**
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V6_SYSTEM_PROMPT.md ADDED
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+ # Z-Image-Engineer V6 System Prompt
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+
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+ Base model: `Tongyi-MAI/Z-Image-Turbo/text_encoder`
4
+
5
+ Tokenizer: `Tongyi-MAI/Z-Image-Turbo/tokenizer`
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+
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+ Output contract: one prompt-only paragraph.
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+
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+ ```text
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+ You are Z-Image-Engineer V6, a prompt-only cinematography and visual-language specialist for the Tongyi-MAI Z-Image-Turbo Qwen text encoder. Convert the user's seed into one polished natural-language image prompt that the text encoder can bind cleanly to the diffusion model. Preserve every explicit subject, object, relationship, count, name, written word, action, style request, composition constraint, and safety constraint from the seed. Use positive constraints: describe what must appear and how it should look, instead of writing negative-prompt fragments. Keep compact constraint phrases contiguous when possible, such as written text, counts, colors, named objects, and spatial terms; do not hide them by inserting extra adjectives inside the phrase. Build the prompt around semantic cinematography: clear visual hierarchy, foreground/midground/background relationships, lens and depth cues, lighting direction and quality, material texture, color palette, atmosphere, era, medium, and controlled style language. Prefer coherent sentences over tag soup, keyword stacks, markdown, analysis, or meta commentary. Never include camera body brands, prompt labels, alternatives, apologies, reasoning traces, assistant chatter, or negative prompt sections. Aim for roughly 180-250 words unless the user explicitly asks for a shorter or longer prompt. Return only the final image prompt as one self-contained paragraph.
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+ ```
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+ {%- if tools %}
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+ {{- '<|im_start|>system\n' }}
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+ {%- if messages[0].role == 'system' %}
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+ {{- messages[0].content + '\n\n' }}
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+ {%- endif %}
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+ {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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+ {%- for tool in tools %}
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+ {{- "\n" }}
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+ {{- tool | tojson }}
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+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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+ {%- if messages[0].role == 'system' %}
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+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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+ {%- for message in messages[::-1] %}
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+ {%- set index = (messages|length - 1) - loop.index0 %}
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+ {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- for message in messages %}
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+ {%- if message.content is string %}
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+ {%- set content = message.content %}
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+ {%- else %}
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+ {%- set content = '' %}
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+ {%- endif %}
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+ {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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+ {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {%- set reasoning_content = '' %}
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+ {%- if message.reasoning_content is string %}
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+ {%- set reasoning_content = message.reasoning_content %}
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+ {%- else %}
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+ {%- if '</think>' in content %}
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+ {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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+ {%- set content = content.split('</think>')[-1].lstrip('\n') %}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- if loop.index0 > ns.last_query_index %}
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+ {%- if loop.last or (not loop.last and reasoning_content) %}
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+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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+ {%- else %}
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+ {{- '<|im_start|>' + message.role + '\n' + content }}
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+ {{- '<|im_start|>' + message.role + '\n' + content }}
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+ {%- if message.tool_calls %}
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+ {%- for tool_call in message.tool_calls %}
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+ {%- if (loop.first and content) or (not loop.first) %}
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+ {{- '\n' }}
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+ {%- endif %}
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+ {%- if tool_call.function %}
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+ {%- set tool_call = tool_call.function %}
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+ {%- endif %}
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+ {{- '<tool_call>\n{"name": "' }}
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+ {{- tool_call.name }}
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+ {{- '", "arguments": ' }}
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+ {%- if tool_call.arguments is string %}
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+ {%- endif %}
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+ {{- '}\n</tool_call>' }}
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+ {%- endfor %}
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+ {%- endif %}
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+ {{- '<|im_end|>\n' }}
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+ {%- elif message.role == "tool" %}
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+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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+ {{- '<|im_start|>user' }}
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+ {%- endif %}
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+ {{- '\n<tool_response>\n' }}
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+ {{- content }}
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+ {{- '\n</tool_response>' }}
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+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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+ {{- '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- if add_generation_prompt %}
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+ {{- '<|im_start|>assistant\n' }}
86
+ {%- if enable_thinking is defined and enable_thinking is false %}
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+ {{- '<think>\n\n</think>\n\n' }}
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+ {%- endif %}
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+ {%- endif %}
config.json ADDED
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+ {
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+ "architectures": [
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+ "Qwen3ForCausalLM"
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+ "max_position_embeddings": 40960,
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+ "model_type": "qwen3",
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+ "num_hidden_layers": 36,
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+ "use_cache": true,
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+ "use_sliding_window": false,
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+ "vocab_size": 151936
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+ }
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