# Visual Asset Generator > Executes AI visual asset generation using prompts from visual-asset-prompt-generator. Calls fal.ai (Nano Banana/Flux 1.1 Pro) for images and Hunyuan3D (via Blender MCP or HuggingFace) for 3D models. Handles async polling and asset post-processing. Use when this capability is needed. - Skill: `tomevault-io/visual-asset-generator` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/visual-asset-generator` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/visual-asset-generator/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/visual-asset-generator --- # Visual Asset Generator Takes structured prompts from visual-asset-prompt-generator and generates actual visual assets via AI APIs. Manages async 3D generation, image downloads, and asset organization. ## When to Use - After visual-asset-prompt-generator has produced prompts JSON - User says "generate the images", "create the 3D models", "run the asset pipeline" - As Step 2 of the brand-visual-pipeline ## Input Variables - `[BRAND_NAME]` — Brand name (for directory paths) - `[PROMPTS_FILE]` — Path to prompts JSON from Skill 1 (default: `brands/{BRAND_NAME}/visual-prompts.json`) - `[OUTPUT_DIR]` — Asset output directory (default: `assets/{BRAND_NAME}/`) ## Required Environment ```bash FAL_KEY=xxx # fal.ai API key (for Nano Banana / Flux) HF_TOKEN=hf_xxx # HuggingFace Pro token (for Hunyuan3D fallback) ``` ## The Protocol ### Phase 1: Load Prompts Read the prompts JSON generated by visual-asset-prompt-generator. Group by service: - `fal-flux` prompts → fal.ai pipeline - `hunyuan3d` prompts → Blender MCP or HuggingFace pipeline - `polyhaven` prompts → Blender MCP PolyHaven search ### Phase 2: Image Generation via fal.ai Use `scripts/fal_client.py` for each image prompt: ```bash python scripts/fal_client.py \ --prompt "the generation prompt" \ --negative-prompt "things to avoid" \ --width 1920 --height 1080 \ --guidance-scale 7.5 \ --steps 50 \ --output "assets/{brand}/images/hero-shots/{asset_id}.png" ``` **fal.ai API Pattern:** ```python import fal_client # For Flux 1.1 Pro (high quality) result = fal_client.subscribe( "fal-ai/flux-pro/v1.1", arguments={ "prompt": prompt_text, "image_size": {"width": width, "height": height}, "num_inference_steps": steps, "guidance_scale": guidance_scale, "safety_tolerance": "2" } ) image_url = result["images"][0]["url"] # For Nano Banana (image cleanup/enhancement) result = fal_client.subscribe( "fal-ai/nano-banana", arguments={ "prompt": prompt_text, "image_url": input_image_url, # if enhancing existing image "image_size": {"width": width, "height": height} } ) ``` **Post-processing for each generated image:** 1. Download from result URL 2. Save as PNG to `assets/{brand}/images/{type}/{asset_id}.png` 3. Convert to WebP for web use 4. Generate 800px and 400px thumbnails ### Phase 3: 3D Model Generation via Hunyuan3D **Option A: fal.ai (preferred — no Blender required)** Use `scripts/fal_3d_client.py` for each 3D prompt: ```bash # Text-to-3D python scripts/fal_3d_client.py \ --mode text-to-3d \ --prompt "the 3D generation prompt" \ --enable-pbr \ --face-count 100000 \ --output "assets/{brand}/3d-models/{asset_id}/" # Image-to-3D (from generated product image) python scripts/fal_3d_client.py \ --mode image-to-3d \ --image-url "https://fal.media/files/..." \ --output "assets/{brand}/3d-models/{asset_id}/" ``` Output: GLB + OBJ + preview.png in the output directory. **Option B: Blender MCP (when Blender is running)** ```python # Step 1: Check availability status = mcp__blender__get_hunyuan3d_status(user_prompt="Generate 3D for {brand}") # Step 2: Generate model result = mcp__blender__generate_hunyuan3d_model( text_prompt=prompt_data["prompt"], user_prompt="Generate 3D model for {brand}" ) # Returns: {"job_id": "job_xxx"} # Step 3: Poll for completion (30-second intervals) while True: status = mcp__blender__poll_hunyuan_job_status(job_id=job_id) if status["status"] == "DONE": zip_url = status["ResultFile3Ds"] break elif status["status"] in ["FAILED", "CANCELED"]: # Fall back to Option B break # Wait 30 seconds between polls # Step 4: Import into Blender mcp__blender__import_generated_asset_hunyuan( name=f"{brand}_{asset_id}", zip_file_url=zip_url ) # Step 5: Capture viewport render mcp__blender__get_viewport_screenshot(max_size=1920, user_prompt="Render {brand} model") # Step 6: Export GLB mcp__blender__execute_blender_code( code=f""" import bpy bpy.ops.export_scene.gltf( filepath='{output_dir}/3d-models/{asset_id}/model.glb', export_format='GLB' ) """, user_prompt="Export model" ) ``` **Option C: HuggingFace API (fallback when fal.ai and Blender are unavailable)** Use `scripts/hunyuan_generate.py`: ```bash python scripts/hunyuan_generate.py \ --prompt "the 3D generation prompt" \ --output "assets/{brand}/3d-models/{asset_id}/" ``` ### Phase 4: Texture Search via PolyHaven For texture prompts: ```python results = mcp__blender__search_polyhaven_assets( asset_type="textures", categories=prompt_data["categories"], user_prompt="Find textures for {brand}" ) mcp__blender__download_polyhaven_asset( asset_id=chosen_id, asset_type="textures", resolution="4k", user_prompt="Download texture" ) ``` ### Phase 5: Generate Asset Manifest Create `assets/{BRAND_NAME}/manifest.json`: ```json { "brand": "[BRAND_NAME]", "generated_at": "ISO_DATE", "assets": [ { "asset_id": "hero-001", "type": "hero-image", "service": "fal-flux", "status": "completed", "files": { "primary": "images/hero-shots/hero-001.png", "webp": "images/hero-shots/hero-001.webp", "thumb_800": "images/hero-shots/hero-001-800.png", "thumb_400": "images/hero-shots/hero-001-400.png" }, "dimensions": { "width": 1920, "height": 1080 }, "prompt_used": "the prompt that was used", "generation_params": { ... } }, { "asset_id": "model-001", "type": "3d-model", "service": "hunyuan3d", "status": "completed", "files": { "glb": "3d-models/model-001/model.glb", "render": "3d-models/model-001/render.png" }, "job_id": "job_xxx" } ] } ``` ## Error Handling | Error | Action | |-------|--------| | fal.ai 401 | Check FAL_KEY env var, report to user | | fal.ai 429 | Retry with exponential backoff (max 3 retries) | | Hunyuan3D timeout (>10 min) | Fall back to HF API or report | | Blender not connected | Use HF API fallback | | Image download fails | Retry once, then skip with warning | ## Scripts ### `scripts/fal_client.py` fal.ai API client for Flux 1.1 Pro and Nano Banana image generation. ### `scripts/fal_3d_client.py` fal.ai 3D model generation client. Supports four modes: ```bash # Text to 3D (Hunyuan3D V3) — $0.375/gen, ~2 min, PBR materials python scripts/fal_3d_client.py --mode text-to-3d --prompt "a leather journal" --output ./output/ # Image to 3D (Hunyuan3D V3) — $0.375/gen, ~2 min python scripts/fal_3d_client.py --mode image-to-3d --image-url "https://..." --output ./output/ # Image to 3D (TripoSR) — $0.07/gen, <1s, lower quality python scripts/fal_3d_client.py --mode triposr --image-url "https://..." --output ./output/ # Image to 3D (Hunyuan3D V2) — $0.16/gen python scripts/fal_3d_client.py --mode hunyuan-v2 --image-url "https://..." --output ./output/ ``` **Hunyuan3D V3 extra options:** - `--enable-pbr` — PBR materials (default: true) - `--face-count 100000` — Target polygon count - `--generate-type Normal|LowPoly|Geometry` **Output:** GLB, OBJ, and preview PNG files in the specified output directory. ### `scripts/hunyuan_generate.py` HuggingFace API client for Hunyuan3D when Blender MCP and fal.ai are unavailable. ## Integration - **Upstream**: visual-asset-prompt-generator (consumes prompts JSON) - **Downstream**: visual-asset-integrator (consumes asset manifest) - **External APIs**: fal.ai, HuggingFace, Blender MCP - **Part of**: brand-visual-pipeline orchestrator --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/sheshiyer) — claim your Tome and manage your conversions.