Optimized Low-VRAM Workflow for Z-Image-Base (GGUF) Refined version of my ZIT workflow, modified to work with ZIBThis workflow delivers the full power of Z-Image-Base in GGUF format, specially optimized for GPUs with less than 8 GB VRAM, like my RTX 4050 with only 6 GB. The pre-selected Z-Image-Fun-LoRA provides a noticeable speed boost with almost no quality loss. Two intelligent upscaling stages, optional automatic trigger-word integration via the Super LoRA Loader, and an extended save node complete this elegant setup.Version 1.5 (previous versions were only my personal use) brings significant improvements in speed, usability, and upscaling quality — while remaining extremely VRAM-efficient (tested on RTX 4050 with only 6 GB).What’s new in v1.5:<ul><li>selectLatentSizePlus — intuitive aspect-ratio and resolution selector with beautiful presets (including 7:12 Tall Vista and other golden-ratio-friendly options) plus easy orientation swap</li><li>Full SEEDVR2 Video Upscaler Subgraph — powerful DiT-based (.safetensors or GGUF) high-end upscaler that delivers stunning 4K+ results with intelligent resolution handling, Lab color correction, and temporal settings. Works exceptionally well on still images too, producing superior detail and coherence</li><li>CLIP switched to the abliterated Qwen3-4B-Instruct-2507.Q5_K_S.gguf (lumina2 type)</li><li>Improved workflow organization, expanded notes, and more robust saving options</li></ul><hr /><h3 id="required-custom-nodes-(updated-for-v1.5):">Required Custom Nodes (updated for v1.5):</h3><ul><li>ComfyUI-GGUF - <a target="_blank" rel="ugc" href="https://github.com/city96/ComfyUI-GGUF">https://github.com/city96/ComfyUI-GGUF</a> - UnetLoaderGGUF + CLIPLoaderGGUF</li><li>nd-super-nodes - <a target="_blank" rel="ugc" href="https://github.com/HenkDz/nd-super-nodes">https://github.com/HenkDz/nd-super-nodes</a> - NdSuperLoraLoader with tags, trigger words &amp; beautiful UI</li><li>save-image-extended-comfyui - <a target="_blank" rel="ugc" href="https://github.com/thedyze/save-image-extended-comfyui">https://github.com/thedyze/save-image-extended-comfyui</a> - Advanced saving with metadata &amp; dynamic filenames</li><li>ComfyUi-MzMaXaM - <a target="_blank" rel="ugc" href="https://github.com/MzMaXaM/ComfyUi-MzMaXaM">https://github.com/MzMaXaM/ComfyUi-MzMaXaM</a> - selectLatentSizePlus</li><li>ComfyUI-SeedVR2_VideoUpscaler - <a target="_blank" rel="ugc" href="https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler">https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler</a> - SEEDVR2 Video Upscaler Subgraph</li></ul><hr /><h3 id="models-and-downloads-(exact-paths)">Models &amp; Downloads (exact paths)</h3>The following list explains the base models I am most frequently using with this workflow. The list as well explains where to put each file after you downloaded it.1. Main Model (Z-Image-Base GGUF):<ul><li>File: Juggernaut_Z_V1_by_RunDiffusion_q6_k-004.gguf</li><li>Download: <a target="_blank" rel="ugc" href="https://huggingface.co/RunDiffusion/Juggernaut-Z-Image/resolve/main/Juggernaut_Z_V1_by_RunDiffusion_q6_k-004.gguf">https://huggingface.co/RunDiffusion/Juggernaut-Z-Image/resolve/main/Juggernaut_Z_V1_by_RunDiffusion_q6_k-004.gguf</a></li><li>Target folder: ComfyUI/models/diffusion_models/</li></ul>2. Text Encoder (CLIP)<ul><li>File: Qwen3-4B-Instruct-2507-abliterated.Q5_K_S.gguf</li><li>Download: <a target="_blank" rel="ugc" href="https://huggingface.co/mradermacher/Huihui-Qwen3-4B-Instruct-2507-abliterated-GGUF/resolve/main/Huihui-Qwen3-4B-Instruct-2507-abliterated.Q5_K_S.gguf">https://huggingface.co/mradermacher/Huihui-Qwen3-4B-Instruct-2507-abliterated-GGUF/resolve/main/Huihui-Qwen3-4B-Instruct-2507-abliterated.Q5_K_S.gguf</a></li><li>Target folder: ComfyUI/models/text_encoders/ (or clip/)</li></ul>3. VAE<ul><li>File: ae.safetensors (~335 MB)</li><li>Download: Usually included with Z-Image-Turbo setups or available here: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors?download=true">https://huggingface.co/Comfy-Org/z_image_turbo/resolve/main/split_files/vae/ae.safetensors</a></li><li>Target folder: ComfyUI/models/vae/</li></ul>4. Upscalers<ul><li>4× Upscaler: 4xLSDIRplusN.pth (variant of 4x-UltraSharp) → <a target="_blank" rel="ugc" href="https://civarchive.com/models/116225/4x-ultrasharp">https://civarchive.com/models/116225/4x-ultrasharp</a></li><li>1× Skin-Contrast Upscaler: 1xSkinContrast-High-SuperUltraCompact.pth Download: <a target="_blank" rel="ugc" href="https://huggingface.co/notkenski/upscalers/blob/main/1xSkinContrast-High-SuperUltraCompact.pth">https://huggingface.co/notkenski/upscalers/blob/main/1xSkinContrast-High-SuperUltraCompact.pth</a></li><li>Target folder: ComfyUI/models/upscale_models/</li></ul>5. Pre-loaded LoRA (included in workflow):<ul><li>File: Z-Image-Fun-Lora-Distill-2603_UDCAI_ComfyUI.safetensors</li><li>Download: <a target="_blank" rel="ugc" href="https://civarchive.com/models/2362961/z-image-fun-distill-lora">https://civarchive.com/models/2362961/z-image-fun-distill-lora</a> (UDCAI Edit version)</li><li>Target folder: ComfyUI/models/loras/</li></ul>6. SEEDVR2 Models (for the new high-end upscaler – optional but recommended):<ul><li>DiT Model: seedvr2_ema_3b-Q8_0.gguf</li><li>VAE: ema_vae_fp16.safetensors</li><li>Download from the official ComfyUI-SeedVR2_VideoUpscaler repository or Hugging Face and place in the folders required by the custom node.</li></ul><hr /><h3 id="key-nodes-and-their-functions">Key Nodes &amp; Their Functions</h3><ul><li>NdSuperLoraLoader with automatic trigger-word detection and clean tag interface</li><li>selectLatentSizePlus → effortless aspect-ratio and resolution control</li><li>KSamplerAdvanced with proven settings</li><li>Two-stage classic upscaler (4× LSDIR + 1× Skin-Contrast) in its own subgraph, or</li><li>New SEEDVR2 Video Upscaler Subgraph → for ultimate quality (optional, easily bypassed)</li><li>SaveImageExtended with full metadata and dynamic filenames</li></ul><hr /><h3 id="recommended-settings-(already-set-in-the-workflow)">Recommended Settings (already set in the workflow)</h3><ul><li>Sampler: euler, dpmpp_sde or res_multistep</li><li>Scheduler: simple, normal, beta or ddim_uniform</li><li>Steps: 8–12 (with Z-Image-Fun-LoRA)</li><li>CFG Scale: 1.0–1.5</li><li>Shift: 4–7</li><li>Resolution: 1024×1536 (portrait) – perfectly balanced for the golden ratio and typical ZIB outputs - the upscaler will automatically upscale by a factor of 4</li></ul><hr /><h3 id="how-to-use-the-workflow">How to Use the Workflow</h3><ol><li>Install all required custom nodes</li><li>Load the workflow</li><li>Enter your positive prompt (the pre-loaded Fun-LoRA trigger words are handled automatically)</li><li>Adjust negative prompt if needed</li><li>Choose aspect ratio and resolution via the Size Selector</li><li>Generate (Euler + Simple recommended)</li><li>Optionally upscale with SEEDVR2 for cinematic results</li><li>Done — beautifully saved with metadata</li></ol>Special thanks to @LumaRift who provided the SeedVR2 subworkflow and some good advise on optimizing my setup.

Files with SHA256: 852f7ee31f55ebde152f5df503e8b4c8a713e376ab70c06f176b0bcf87f503e3