--- license: mit library_name: diffusers pipeline_tag: text-to-image base_model: Tongyi-MAI/Z-Image tags: - z-image - diffusers - lora - text-to-image - svg - vector-graphics - image-to-vector - flow-matching --- # SVG Generator ## Generative Neural Models for Scalable Vector Graphics


_{a simple lighthouse icon}	_{A minimal rocket icon}	_{A cute cat face icon}	_{A magic potion bottle with bubbles, sparkles, cork, and label}	_{A cozy cabin in the woods with a chimney, pine trees, and a crescent moon}	_{A steampunk hot air balloon with gears, ropes, basket, and brass details}

This repository contains the published artifacts for a two-stage text-to-SVG pipeline. Stage 1 uses Z-Image with an SVG-domain LoRA to generate a raster illustration. Stage 2 converts the raster to SVG, either with `vtracer` or with the included flow-matching vectorizer. Code, training scripts, and end-to-end inference are available in the GitHub repository: ## Quick Start Clone the project repository and install dependencies: ```bash git clone https://github.com/JosefKuchar/svg-generator.git cd svg-generator uv sync ``` For faster transformer attention on compatible CUDA systems, the project README contains an optional FlashAttention wheel install command. For the default SVG conversion path, install `vtracer` and make sure it is available on `PATH`. Run text-to-SVG inference: ```bash uv run python infer.py "a simple lighthouse icon" \ --output-svg lighthouse.svg \ --output-png lighthouse.png \ --preview-png lighthouse-preview.png ``` The default prompt prefix is: ```text SVG illustration with white background. ``` It can be changed with `--prompt-prefix`. ## Inference Options Stage 1 can use either Z-Image Base or Turbo: ```bash # Default: Tongyi-MAI/Z-Image, 50 steps, guidance 4.0 uv run python infer.py "A minimal rocket icon" --z-image base # Faster: Tongyi-MAI/Z-Image-Turbo, 9 steps, guidance 0.0 uv run python infer.py "A minimal rocket icon" --z-image turbo ``` The default stage-2 vectorizer is `vtracer`: ```bash uv run python infer.py "A cute cat face icon" \ --vectorizer vtracer \ --output-svg cat.svg ``` The neural flow-matching vectorizer can be selected explicitly: ```bash uv run python infer.py "A cute cat face icon" \ --vectorizer flow-matching \ --flow-steps 50 \ --flow-cfg-scale 1.0 \ --max-segments 256 \ --output-svg cat.svg ``` ## Direct LoRA Loading The Z-Image SVG LoRA can be loaded with the standard Diffusers `ZImagePipeline`: ```python import torch from diffusers import ZImagePipeline repo_id = "JosefKuchar/svg-generator" base_model = "Tongyi-MAI/Z-Image" pipe = ZImagePipeline.from_pretrained( base_model, torch_dtype=torch.bfloat16, ) pipe.load_lora_weights(repo_id, weight_name="zimage-svg-lora.safetensors") pipe.to("cuda") prompt = ( "SVG illustration with white background. " "A simple icon of a mountain cabin surrounded by pine trees." ) image = pipe( prompt=prompt, height=1024, width=1024, num_inference_steps=50, guidance_scale=4.0, ).images[0] ``` ## Artifacts - `zimage-svg-lora.safetensors` - LoRA adapter for Z-Image. - `flow-matching/config.json` - configuration for the neural vectorizer. - `flow-matching/model.safetensors` - flow-matching vectorizer weights. - `assets/*.svg` - example outputs shown above. The flow-matching vectorizer is conditioned on `facebook/dinov3-vits16-pretrain-lvd1689m`. The DINOv3 encoder is loaded separately from its original Hugging Face repository and is not stored here. ## Intended Use Use these artifacts as an end-to-end text-to-SVG pipeline for icon-like and illustration-style prompts. The recommended path is to run `infer.py` from the GitHub repository, which applies the Z-Image LoRA, saves the intermediate raster image, and vectorizes it into SVG. The model is best suited for clean drawings with simple shapes, flat colors, white backgrounds, and compact compositions. It is not intended as a general SVG editor or a replacement for manual vector design work. The LoRA itself only generates raster images; SVG output is produced by the second-stage vectorizer.