acobham
5eb4426d30
- README: rewrite index to reflect actual files (STACK/CONTENT/OPTIMIZATION); remove 15 dead links to old numbered SOPs; add subdirectory table; update image gen to Google Imagen as default - STACK: fix wp-divi-pipeline script paths; genericize vibrantyou/domain examples; strip pre-existing em dashes throughout - CONTENT: update image generation default to Google Imagen API with allotted quota - image-gen-workflow: remove client-specific cobhamtech data; generalize brand palette step; update date - wp-divi-pipeline-to-am-stack: remove vibrantyou.yoga client data block; fix Related SOPs links to current files
1.9 KiB
1.9 KiB
Local Image Generation: SOPs
Complete reference for generating site images locally using ComfyUI. No cloud API required. No per-image cost. Runs on the Arising Media workstation.
Index
- 01-comfyui-setup.md: Installing ComfyUI, venv, GGUF node
- 02-flux-images.md: FLUX.1 Schnell image generation pipeline
- 03-wan-video.md: Wan 2.2 image-to-video pipeline
- 04-prompt-guide.md: Prompt patterns for interior/carpet photography
- 05-quality-levers.md: Prompt, steps, model size: what to adjust and when
Quick start (images already set up)
# 1. Start ComfyUI
tmux new-session -d -s comfyui \
"cd ~/ComfyUI && venv/bin/python main.py --listen 0.0.0.0 --port 8188 --cpu 2>&1 | tee ~/comfyui.log"
# 2. Wait ~30s, then generate images
cd /home/sirdrez/arisingmedia-websites/{domain}
python3 tools/gen-images-flux.py 2>&1 | tee tools/flux-gen.log
# 3. Convert to WebP and deploy
python3 tools/convert-to-webp.py
rm assets/images/**/*.jpg
docker compose build --no-cache web && docker compose up -d
Model files (installed at ~/ComfyUI/models/)
| Purpose | File | Size | Location |
|---|---|---|---|
| FLUX image UNet | flux1-schnell-Q8_0.gguf | 12GB | models/unet/ |
| FLUX T5 encoder | t5xxl_fp8_e4m3fn.safetensors | 4.6GB | models/clip/ |
| FLUX CLIP-L | clip_l.safetensors | 235MB | models/clip/ |
| FLUX VAE | ae.safetensors | 108MB | models/vae/ |
| Wan 2.2 video | Wan2.2-TI2V-5B-Q4_K_M.gguf | 3.2GB | models/diffusion_models/ |
| Wan UMT5 encoder | umt5_xxl_fp8_e4m3fn_scaled.safetensors | 6.3GB | models/clip/ |
| Wan VAE | wan_2.1_vae.safetensors | 243MB | models/vae/ |
Reference project
lahrcarpetcleaning.com: first project using this full pipeline.
Scripts: tools/gen-images-flux.py, tools/gen-video-wan.py, tools/convert-to-webp.py