This is a very simple ComfyUI beginner friendly Image-to-Image workflow that will work with Z-Image Base model or it's GGUF model equivalents. I made this workflow to use Z-Image Base's awesome AI photograpic capabilities to convert flat vector or anime images to AI photos. It currently does not transfer poses just concepts based on slightly altered prompts out of original with denoise value between 0.70 to 0.90 (but I will create a Controlnet pose tranfer version once ControlNet Union will be available for ZiB). I used BOTH full non-distilled base model and a Q6 Model to test this workflow and both work well with my 8GB AMD GPU. But if you have a weaker GPU you can find other low grade hardware supported GGUF models from these HuggingFace Repos -Jayn7's HuggingFace repo for Z-Image Base GGUF models ---------------------------------------------------------<a target="_blank" rel="ugc" href="https://huggingface.co/jayn7/Z-Image-GGUF/tree/main">https://huggingface.co/jayn7/Z-Image-GGUF/tree/main</a>Unsloth's QWEN3 GGUF Text Encoders for Z-Image Base GGUF models -------------------------------------------------------------------<a target="_blank" rel="ugc" href="https://huggingface.co/unsloth/Qwen3-4B-GGUF/tree/main">https://huggingface.co/unsloth/Qwen3-4B-GGUF/tree/main</a>The newly released Z-Image Base model, developed by Tongyi-MAI (Alibaba's AI lab), is the non-distilled foundation of the Z-Image family, designed to serve as the "raw" starting point for the open-source community. Unlike Z-Image Turbo, which is a distilled version optimized for ultra-fast performance and sub-second inference using as few as 8 steps, the Base model is a full-weight checkpoint that requires more steps (typically 28 to 50) but offers significantly higher diversity in poses, compositions, and artistic styles. While Z-Image Turbo is specifically tuned for immediate photorealism and consumer-grade GPU efficiency, Z-Image Base is built for researchers and creators who need a more flexible model for fine-tuning, training LoRAs, or achieving complex prompt adherence that the faster, more "opinionated" Turbo variant might miss. Compared to other specialized versions like Z-Image Edit, which is fine-tuned specifically for instruction-based image-to-image tasks, the Base model provides the most neutral and unrefined data distribution, making it the superior choice for those looking to push the 6-billion parameter S3-DiT architecture into new, creative territories beyond just speed.You need a Hugging Face account to download your necessary files (Details are mentioned below). Make sure you install GGUF addon for ComfyUI using ComfyUI manager or any other missing nodes you may have and place the correct files in correct places. Also check out my other workflows for SD 1.5 + SDXL 1.0, WAN 2.1, WAN 2.2, MagicWAN Image v2, QWEN, Flux.2 Klein, HunyuanImage-2.1, HiDream, KREA, Chroma, AuraFlow, Z-Image Turbo and Flux. Feel free to toss some yellow Buzz on stuffs you like.How to use this -#1. Just select your desired Z-Image Base (or swap it with GGUF model files) model files first and now#2. now select a Reference Image for Image-to-Image process#3. then input your positive and negative prompts.#4. select how many images you want (Change the number besides the "Run" button)#5. select image sampling methods, CFG, steps etc. settings#6. finally press the run button to generate. That's it..** LORA usage for this workflow is optional you can use it without any LORAs, use with 1 or 2 or any other number of LORAs, too add new LORAs press the L button on top to lunch LORA Manager on a new tab find your LORA and if you want to use that LORA just click the upward Kite button. For both example Images I used this Z IMAGE PAWG LORA for Z-Image Base from here - <a target="_blank" rel="ugc" href="https://civarchive.com/models/2262743/z-image-pawg?modelVersionId=2639737">https://civarchive.com/models/2262743/z-image-pawg?modelVersionId=2639737</a>Required Files==================### Download Link for Z-Image Base full non-distilled Checkpoint --------------------------------------------------------------------<a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/z_image/resolve/main/split_files/diffusion_models/z_image_bf16.safetensors">https://huggingface.co/Comfy-Org/z_image/resolve/main/split_files/diffusion_models/z_image_bf16.safetensors</a>### Download Link for Text Encoder of Z-Image Base full non-distilled Checkpoint ------------------------------------------------------------------------------------<a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/z_image/resolve/main/split_files/text_encoders/qwen_3_4b_fp8_mixed.safetensors">https://huggingface.co/Comfy-Org/z_image/resolve/main/split_files/text_encoders/qwen_3_4b_fp8_mixed.safetensors</a>### Download Link for VAE of Z-Image Base (or Z-Image Base GGUF) Checkpoint -------------------------------------------------------------------------------<a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/z_image/resolve/main/split_files/vae/ae.safetensors">https://huggingface.co/Comfy-Org/z_image/resolve/main/split_files/vae/ae.safetensors</a>### Download Link for Z-Image Base Q6 GGUF Checkpoint ---------------------------------------------------------<a target="_blank" rel="ugc" href="https://huggingface.co/jayn7/Z-Image-GGUF/resolve/main/z_image-Q6_K.gguf">https://huggingface.co/jayn7/Z-Image-GGUF/resolve/main/z_image-Q6_K.gguf</a>### Download Link for Text Encoder of Z-Image Base Q6 GGUF Checkpoint -------------------------------------------------------------------------<a target="_blank" rel="ugc" href="https://huggingface.co/unsloth/Qwen3-4B-GGUF/resolve/main/Qwen3-4B-Q6_K.gguf">https://huggingface.co/unsloth/Qwen3-4B-GGUF/resolve/main/Qwen3-4B-Q6_K.gguf</a>

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