Comfyui

TL;DR Cover art was the gateway drug. The same local ComfyUI install that generates this blog’s headers also strips the cluttered background off a photo of hardware on my bench, upscales a small generation to retina resolution, and batch-produces a consistent set of illustrations from a prompt template. Two Mac Studios mean I can fire a batch at one box and keep working on the other. It’s all driven from scripts and agents, and it all costs $0 per image because it never leaves the house. ...

TL;DR I don’t pay for stock photos and I don’t open Canva. Every raster image on this blog is generated on a Mac Studio sitting three feet from me, by asking Claude Code to call a generate_image MCP tool that wraps ComfyUI. The pipeline is: prompt → ComfyUI (MPS) → PNG on disk → upload_media.py → S3 → CloudFront → a Markdown reference in the post. It costs $0 per image, takes ~15 seconds, and the whole thing is repeatable because the prompt and settings live in the commit history. ...

TL;DR Stable Diffusion 3.5 Large is 15 GB. RealVisXL is 6.5 GB. Throw in a few LoRAs and a VAE, and your SSD hits the wall fast. I run a MinIO bucket as the long-tail model store, sync it to a local overflow directory on a 30-minute schedule via rclone, and register both the hot (SSD) and cold (synced overflow) paths in ComfyUI’s extra_model_paths.yaml. Models appear transparently; the loader searches both tiers. A fresh model lands in MinIO, appears locally within 30 minutes, and ComfyUI finds it without any manual shuffling. ...

TL;DR A Mac Studio M3 Ultra costs the same as a single 4090 but comes with 256 GB of unified memory and 60-core GPU, all running at 100–200 W under inference. I stopped trying to pass MPS into containers and instead run Ollama and ComfyUI natively on macOS, then proxy them back into k3s as simple Kubernetes Services with manual Endpoints. Two Mac Studios connected via Thunderbolt 5 split the load: one handles hot-path LLM inference and embeddings, the other runs the heavy forge for diffusion and long-horizon reasoning. Both are cheaper to run than a single-socket A100 and require no special driver stacks. ...

TL;DR I deployed ComfyUI natively on my Mac Studio M3 Ultra using Apple’s MPS GPU backend, proxied it through k3s Traefik ingress with Authentik SSO, wired it into Open WebUI as the image generation backend (replacing $0.04/image Bedrock calls), and built an MCP server so AI agents can generate images programmatically. The whole pipeline is Ansible-managed and generates images for free on local hardware. Why native instead of containerized ComfyUI needs GPU access. On Linux, that’s straightforward — pass through the GPU via device plugins. On macOS, there’s no container runtime that exposes MPS (Metal Performance Shaders) to containers. Docker Desktop on Mac runs a Linux VM — no Metal, no MPS. ...