mold

name	mold
description	Generate AI images and video locally using the mold CLI. Use when asked to generate images from text prompts, create video clips, transform existing images (img2img), or manage local AI models.
argument-hint	["prompt or command"]
allowed-tools	Bash, Read, Glob, Grep

mold — Local AI Image Generation CLI

Generate images and video from text prompts using FLUX, SD1.5, SDXL, SD3.5, Z-Image, Flux.2 Klein, Qwen-Image, LTX Video, LTX-2 / LTX-2.3, and Wuerstchen diffusion models running on local GPU hardware.

Quick Reference

mold run "a cat on a skateboard"                    # Generate with default model
mold run flux-dev:q4 "a sunset over mountains"      # Specific model
mold run "a portrait" -o portrait.png               # Custom output path
mold run "a dog" --seed 42 --steps 20               # Reproducible generation
mold run "watercolor" --image photo.png --strength 0.7  # img2img
mold run qwen-image-edit-2511:q4 "make the chair red leather" --image chair.png --image swatch.png --qwen2-variant q4
mold run qwen-image:q2 "a poster" --qwen2-variant q6    # Qwen-Image quantized text encoder
mold run flux-dev:bf16 "portrait" --lora style.safetensors --lora-scale 0.8  # LoRA adapter
mold mcp --host http://localhost:7680                # Stdio MCP bridge for LM Studio
mold lambda deploy --instance-type gpu_1x_a10 --region us-west-1  # Private Lambda Cloud web UI

mold mcp exposes synchronous image generation, async generation with status polling, gallery search/fetch, model and LoRA listing, and server status tools.

How to Use This Skill

Parse $ARGUMENTS to determine the action:

• If arguments look like a prompt (natural language), run mold run "<prompt>" with sensible defaults
• If arguments start with a subcommand (pull, list, default, config, serve, server, mcp, info, ps, rm, unload, update, stats, clean, tui, completions, version, runpod, lambda, jobs), run that subcommand
• If arguments include flags (--model, --image, --steps, etc.), pass them through

Generating Images

Basic Usage

# Default model (flux2-klein:q8 — fast 4-step, Apache 2.0, fully ungated)
mold run "a red apple on a wooden table"

# Explicit model selection
mold run flux-dev:q4 "a photorealistic landscape at golden hour"

# With output path
mold run "cyberpunk cityscape" -o cityscape.png

# Reproducible output (same seed = same image)
mold run "a cat" --seed 42

# Custom dimensions (must be multiples of 16)
mold run "a banner" --width 1024 --height 512

# Batch generation (multiple images)
mold run "abstract art" --batch 4 --seed 100 -o art.png

# JPEG output
mold run "a sunset" --format jpeg -o sunset.jpg

# Disable PNG metadata embedding
mold run "a cat" --no-metadata

# Display image inline in terminal after generation (requires `preview` feature)
mold run "a cat" --preview

# Negative prompt (CFG-based models: SD1.5, SDXL, SD3, Wuerstchen)
mold run sd15:fp16 "a portrait" -n "blurry, watermark, ugly, bad anatomy"
mold run sdxl:fp16 "a landscape" --negative-prompt "low quality, jpeg artifacts"
mold run sd15:fp16 "a cat" --no-negative  # suppress config default

Prompt Expansion

Expand short prompts into detailed image generation prompts using a local LLM (Qwen3-1.7B). The expansion model auto-downloads on first use (~1.8GB).

# Preview expanded prompt without generating
mold expand "a cat"

# Expand with multiple variations
mold expand "cyberpunk city" --variations 5

# Expand as JSON
mold expand "a cat" --variations 3 --json

# Generate with expansion (short prompt -> detailed prompt -> image)
mold run "a cat" --expand

# Batch + expand: each image gets a unique expanded prompt
mold run "a sunset" --expand --batch 4

# Use a specific expansion backend (OpenAI-compatible API)
mold run "a cat" --expand --expand-backend http://localhost:11434/v1

# Disable expansion (overrides config/env default)
mold run "a cat" --no-expand

The expansion model is dropped from memory before diffusion begins, so it doesn't compete for VRAM.

LoRA Adapters

Apply LoRA (Low-Rank Adaptation) fine-tuned adapters on top of FLUX BF16 base models:

# Basic LoRA usage
mold run flux-dev:bf16 "a portrait" --lora /path/to/adapter.safetensors

# Adjust LoRA strength (0.0 = no effect, 1.0 = full, up to 2.0)
mold run flux-dev:bf16 "anime style" --lora style.safetensors --lora-scale 0.7

# LoRA with other options (img2img, seed, etc.)
mold run flux-dev:bf16 "oil painting" --lora art.safetensors --image photo.png --strength 0.6

# Stack multiple LoRAs (deltas merge additively: W' = W + Σ scale_i · B_i @ A_i)
mold run flux-dev:bf16 "epic shot" \
  --lora cinematic.safetensors --lora-scale 0.8 \
  --lora dramatic-lighting.safetensors --lora-scale 0.4

Catalog browse: the web UI's catalog tab proxies live HF + Civitai searches (filter by LoRAs to narrow). Install with mold pull cv:<id> / mold pull hf:<author>/<repo> or the catalog tab's Download button. Once installed, the LoRA appears in the Generate → Settings → LoRA dropdown for any compatible model family. The CLI no longer ships a mold catalog subcommand — every read is live, no scan to run.

MCP / REST discovery: GET /api/loras?model=<name> and the MCP list_loras tool return installed compatible LoRAs with ids and server-side paths. MCP generation accepts ids, paths, or objects like { "id": "cv:827325" } and { "path": "...", "scale": 0.8 }; omitted scale defaults to 1.0.

Web UI multi-LoRA + trigger words: the LoRA picker stacks up to 4 adapters per generation (each with its own scale slider) and supports drag reordering with the request preserving visual stack order. Civitai LoRAs ship trigger phrases (trainedWords); they render as click-to-insert chips beside the selected LoRA — clicking a chip appends the phrase to the active prompt.

Generate web UI: the Generate tab only lists downloaded standalone generation models; the Catalog tab is the install/repair surface for missing models and companions. The controls rail mirrors placement controls, exposes family-specific schedulers, uses installed upscaler dropdowns, shows server-derived peak-memory estimates, reports component readiness via GET /api/models/:model/components, and can save/load named web-local generation templates. The running strip consumes GET /api/queue and PATCH /api/queue/:id to render queued/running work in GPU lanes.

Requirements:

• FLUX (BF16 + GGUF), Flux.2, LTX-2, SD1.5, SD3, SDXL, Qwen-Image (+ Qwen-Image-Edit), and Z-Image families support LoRAs. Wuerstchen and LTX-Video are not yet wired — the server returns a 400 with the list of supported families for any other family. (The message text is centralised in mold-core::validation::require_lora_capable_family and will list whichever families are wired at the time it's surfaced.)
• LoRA file must be .safetensors format. Z-Image / FLUX accept diffusers (PEFT canonical), Kohya/sd-scripts, OneTrainer, and PEFT default-adapter naming. Z-Image fused-QKV LoRAs (cv:2904324) splat across the split attention.to_q/to_k/to_v candle tensors automatically.
• FLUX, Flux.2, Z-Image, Qwen-Image, and LTX-2 CUDA offload use mold-owned block streaming. FLUX / Flux.2 / Z-Image / Qwen-Image keep fitting blocks resident and stream overflow blocks; LTX-2 defaults to adaptive residency and MOLD_OFFLOAD=1 forces full streaming. SD3 offload still streams every MMDiT block.
• GGUF Q4/Q6 work at 1024x1024; Q8 works at 512x512 (Q8 + LoRA at 1024x1024 is tight on 24GB, see #95)

Per-model config defaults (config.toml):

[models."flux-dev:bf16"]
# ... other fields ...
lora = "/path/to/default-adapter.safetensors"
lora_scale = 0.8

Video Generation

Generate video clips with LTX Video models. Output defaults to APNG (lossless, with metadata).

# Basic video generation (25 frames, APNG output)
mold run ltx-video-0.9.6-distilled:bf16 "a cat walking across a windowsill" --frames 25

# Custom frame count (must be 8n+1: 9, 17, 25, 33, 49, ...)
mold run ltx-video-0.9.8-2b-distilled:bf16 "ocean waves at sunset" --frames 49

# MP4 output (QuickTime compatible)
mold run ltx-video-0.9.6-distilled:bf16 "a campfire at night" --frames 17 --format mp4

# GIF for pipe-friendly output
mold run ltx-video-0.9.6-distilled:bf16 "a sunset" --format gif | mpv -

# WebP animated output
mold run ltx-video-0.9.6-distilled:bf16 "a waterfall" --frames 9 --format webp -o waterfall.webp

Constraints: Frame count must be 8n+1 (9, 17, 25, 33, 49, ...). Dimensions must be multiples of 32. Current LTX defaults are 1216x704, 25 frames, 30 fps. Distilled models use fewer steps.

Current status: ltx-video-0.9.6-distilled:bf16 is still the safest default, but the 0.9.8 models now run the full multiscale refinement path. mold pulls the required spatial upscaler asset explicitly, keeps the shared T5 assets under shared/flux/..., and intentionally continues using the compatible LTX-Video-0.9.5 VAE source until the newer VAE layout is ported. Legacy LTX-Video 13B BF16 still has no streaming transformer; CUDA runs preflight full-resident VRAM and fail before allocation when it cannot fit.

Output formats: apng (default, lossless, metadata), gif (256 colors), mp4 (H.264, requires mp4 feature), webp (requires webp feature).

Joint Audio-Video Generation (LTX-2 / LTX-2.3)

Generate synchronized MP4 clips with the LTX-2 family. This family defaults to MP4 output and exposes audio/video-specific controls.

# Fast default joint audio-video generation
mold run ltx-2-19b-distilled:fp8 "rain on a neon taxi window" --frames 97 --format mp4

# Audio-to-video
mold run ltx-2-19b-distilled:fp8 "paper sculpture reacting to music" --audio-file cello.wav

# Keyframe interpolation
mold run ltx-2-19b-distilled:fp8 "a canyon flyover" \
  --pipeline keyframe --frames 97 \
  --keyframe 0:start.png --keyframe 96:end.png

# Camera-control preset
mold run ltx-2-19b-distilled:fp8 "lantern-lit cave entrance" --camera-control dolly-in

Models: ltx-2-19b-dev:fp8, ltx-2-19b-distilled:fp8, ltx-2.3-22b-dev:fp8, ltx-2.3-22b-distilled:fp8

Important flags: --audio, --no-audio, --audio-file, --video, repeatable --keyframe, repeatable --lora, --pipeline, --retake, --camera-control, --spatial-upscale, --temporal-upscale, --clip-frames, --motion-tail

Chained (arbitrary-length) video output: for LTX-2 19B and 22B distilled models, --frames above the 97-frame per-clip cap automatically renders multiple clips with a motion-tail of latents carried across each clip boundary, then stitches them into a single MP4. The CLI picks this path transparently — mold run ltx-2-19b-distilled:fp8 "a cat walking" --frames 400 produces one 400-frame MP4 from 5 chained stages. Advanced callers can override the per-clip length via --clip-frames N (must be 8k+1, clamped to the model cap) and the overlap via --motion-tail N (default 4 pixel frames, 0 disables carryover). Legacy mold run returns only the final output, while durable job workflows use mold jobs / /api/chain-jobs for resume and retake. Other model families reject --frames > 97 with an actionable error.

Current constraints: x2 spatial upscaling is wired across the family, x1.5 spatial upscaling is wired for ltx-2.3-*, and x2 temporal upscaling is wired in the native runtime. Camera-control preset aliases currently auto-resolve the published LTX-2 19B LoRAs only. The family runs through the native Rust stack in mold-inference, with CUDA as the supported backend for real local generation, CPU as a correctness-only fallback, and Metal unsupported. On 24 GB Ada GPUs such as the RTX 4090, the validated path stays on the compatible fp8-cast mode rather than Hopper-only fp8-scaled-mm. The native CUDA matrix is validated across 19B/22B text+audio-video, image-to-video, audio-to-video, keyframe, retake, public IC-LoRA, spatial upscale (x1.5 / x2 where published), and temporal upscale (x2). Explicit LTX-2 unload drops the retained runtime before resetting the assigned CUDA primary context; CPU fallback unload remains a plain state clear. When requests go through mold serve, the built-in body limit is 64 MiB, which is enough for common inline source-video and source-audio workflows.

Multi-prompt Chain (v2)

Direct any-length video scene-by-scene with a TOML script or sugar flags.

# Canonical TOML script (schema: mold.chain.v1)
mold run --script shot.toml
mold run --script shot.toml --dry-run    # Print stage summary, don't submit

# Validate only
mold chain validate shot.toml

# Sugar: repeated --prompt (uniform smooth chains only)
mold run ltx-2-19b-distilled:fp8 \
  --prompt "a cat walks into the autumn forest" \
  --prompt "the forest opens to a clearing" \
  --frames-per-clip 97

Transitions

• smooth (default): motion-tail carryover, visual morph between scenes
• cut: fresh latent, no carryover; optional source_image for i2v seed
• fade: cut + post-stitch alpha blend of fade_frames (default 8)

API

• Chain endpoint: POST /api/generate/chain[/stream]
• Capabilities: GET /api/capabilities/chain-limits?model=<name>
• Max stages: 16. LTX-2 distilled cap: 97 frames/clip.

mold jobs CLI

Durable chain jobs can be inspected and controlled through mold jobs against a running server:

mold jobs list [--json]
mold jobs show <id> [--json]
mold jobs resume <id>
mold jobs retake <id> --stage <N> [--mode cascade|splice] [--seed-offset <U64>] [--prompt <TEXT>]
mold jobs cancel <id>
mold jobs delete <id> [--yes]
mold jobs gc

The commands use MOLD_HOST and MOLD_API_KEY like other remote CLI surfaces. mold jobs gc mirrors POST /api/chain-jobs/gc, pruning successful ephemeral shim jobs and completed non-ephemeral artifacts older than chain.jobs_artifact_ttl_days.

Model Selection Guide

Pick the right model for the task:

Model	Speed	Quality	Best For
flux-schnell:q8	Fast (4 steps)	Good	Quick iterations, drafts
flux-dev:q4	Slow (25 steps)	Excellent	Final quality, detailed
flux2-klein:q8	Fast (4 steps)	Good	Low VRAM, lightweight FLUX
flux2-klein-9b:q8	Fast (4 steps)	Excellent	Higher quality 9B, non-commercial
sdxl-turbo:fp16	Fast (4 steps)	Good	Quick SDXL generation
sd15:fp16	Medium (25 steps)	Good	ControlNet, 512x512
z-image-turbo:q8	Fast (9 steps)	Excellent	High quality, Qwen3 encoder
qwen-image:q4	Slow (50 steps)	Good	Stable base Qwen GGUF on 24 GB cards
qwen-image-2512:q4	Slow (50 steps)	Good	Stable 2512 GGUF on 24 GB cards
qwen-image:q8	Slow (50 steps)	Better	Best base GGUF quality, validated at 768x768 on 24 GB
ltx-video-0.9.6-distilled:bf16	Fast (8 steps)	Good	Text-to-video, 30fps
ltx-video-0.9.8-2b-distilled:bf16	Fast (7+3 steps)	Better	Newer checkpoint family with full multiscale refinement
ltx-2-19b-distilled:fp8	Slow (8 steps)	Better	Joint audio-video, recommended LTX-2 default
ltx-2.3-22b-distilled:fp8	Slow (8 steps)	Best	Larger joint audio-video path

Default model if none specified: flux2-klein:q8

Model Defaults

Model	Steps	Guidance	Resolution
flux-schnell	4	0.0	1024x1024
flux-dev	25	3.5	1024x1024
sdxl-base	25	7.5	1024x1024
sdxl-turbo	4	0.0	512x512
sd15	25	7.5	512x512
sd3.5-large	28	4.0	1024x1024
z-image-turbo	9	0.0	1024x1024
flux2-klein	4	0.0	1024x1024
flux2-klein-9b	4	1.0	1024x1024
qwen-image	50	4.0	1328x1328
qwen-image-2512	50	4.0	1328x1328
ltx-video-0.9.6-distilled	8	1.0	1216x704 (25 frames, 30fps)
ltx-video-0.9.8-2b-distilled	7+3	1.0	1216x704 (25 frames, 30fps, multiscale refine)
ltx-2-19b-distilled	8	3.0	1216x704 (97 frames, 24fps, mp4 default)
ltx-2.3-22b-distilled	8	3.0	1216x704 (97 frames, 24fps, mp4 default)

Available Models

FLUX.1: flux-schnell:q8, flux-schnell:q6, flux-schnell:q4, flux-schnell:bf16, flux-dev:q8, flux-dev:q6, flux-dev:q4, flux-dev:bf16, flux-krea:q8, flux-krea:q6, flux-krea:q4, flux-krea:fp8

FLUX.1 Fine-tunes: jibmix-flux:q4, jibmix-flux:q5, jibmix-flux:fp8, ultrareal-v4:q8, ultrareal-v4:q5, ultrareal-v4:q4, ultrareal-v3:q8, ultrareal-v3:q6, ultrareal-v3:q4, ultrareal-v2:bf16, iniverse-mix:fp8

SDXL: sdxl-base:fp16, sdxl-turbo:fp16, juggernaut-xl:fp16, realvis-xl:fp16, playground-v2.5:fp16, dreamshaper-xl:fp16, pony-v6:fp16, cyberrealistic-pony:fp16

SD 1.5: sd15:fp16, dreamshaper-v8:fp16, realistic-vision-v5:fp16

SD 3.5: sd3.5-large:q8, sd3.5-large:q4, sd3.5-large-turbo:q8, sd3.5-medium:q8

Z-Image: z-image-turbo:bf16, z-image-turbo:q8, z-image-turbo:q6, z-image-turbo:q4

Flux.2 Klein: flux2-klein:bf16, flux2-klein:q8, flux2-klein:q6, flux2-klein:q4

Flux.2 Klein-9B: flux2-klein-9b:bf16, flux2-klein-9b:q8, flux2-klein-9b:q6, flux2-klein-9b:q4

Wuerstchen: wuerstchen-v2:fp16

Qwen-Image: qwen-image:q8, qwen-image:q6, qwen-image:q5, qwen-image:q4, qwen-image:q3, qwen-image:q2, qwen-image:fp8, qwen-image:bf16

Qwen-Image-2512: qwen-image-2512:q8, qwen-image-2512:q6, qwen-image-2512:q5, qwen-image-2512:q4, qwen-image-2512:q3, qwen-image-2512:q2, qwen-image-lightning:fp8, qwen-image-lightning:fp8-8step, qwen-image-2512:bf16

LTX Video: ltx-video-0.9.6:bf16, ltx-video-0.9.6-distilled:bf16, ltx-video-0.9.8-2b-distilled:bf16, ltx-video-0.9.8-13b-dev:bf16, ltx-video-0.9.8-13b-distilled:bf16

LTX-2 / LTX-2.3: ltx-2-19b-dev:fp8, ltx-2-19b-distilled:fp8, ltx-2.3-22b-dev:fp8, ltx-2.3-22b-distilled:fp8 Qwen-Image text encoder controls:

• --qwen2-variant auto|bf16|q8|q6|q5|q4|q3|q2
• --qwen2-text-encoder-mode auto|gpu|cpu-stage|cpu
• On Apple Metal/MPS, auto prefers quantized Qwen2.5-VL GGUF text encoders (q6, then q4) to reduce memory pressure
• On CUDA, auto prefers BF16 when there is enough text-encoder headroom and falls back to quantized GGUF variants for local sequential, resident, and edit paths when BF16 would be too heavy
• Hot CUDA Qwen-Image may keep Qwen2.5 on GPU after a prompt-cache miss only when measured free VRAM still covers denoise and VAE decode reserves; cache hits and pressure cases drop/park before denoise
• qwen-image-edit-2511:* uses repeatable --image inputs and a distinct qwen-image-edit family. Local inference is implemented with the Qwen2.5-VL vision tower, packed edit latents, and true-CFG norm rescaling. Quantized --qwen2-variant values are supported for the edit family through a GGUF language path plus staged vision sidecar. ControlNet (SD1.5): controlnet-canny-sd15:fp16, controlnet-depth-sd15:fp16, controlnet-openpose-sd15:fp16

Utility (LLM): qwen3-expand:q8, qwen3-expand-small:q8

Name Resolution

Bare names auto-resolve: flux2-klein -> flux2-klein:q8, flux-dev -> flux-dev:q8, sdxl-base -> sdxl-base:fp16, sd15 -> sd15:fp16

FP8 safetensors models are automatically quantized to Q8 GGUF on first use (one-time conversion, cached at $MOLD_HOME/cache/).

img2img (Image-to-Image)

Transform an existing image with a text prompt:

# Basic img2img
mold run "oil painting style" --image photo.png --strength 0.7

# Low strength = subtle changes (close to original)
mold run "enhance details" --image photo.png --strength 0.3

# High strength = major transformation
mold run "anime style" --image photo.png --strength 0.9

# From stdin
cat photo.png | mold run "watercolor" --image - --strength 0.6

Strength guide: 0.0 = no change, 0.3 = subtle, 0.5 = balanced, 0.75 = strong (default), 1.0 = full txt2img

Inpainting

Repaint specific regions using a mask:

mold run "a golden retriever" --image park.png --mask mask.png
# mask: white = repaint, black = preserve

ControlNet (SD1.5 only)

mold run "a person" --control edges.png --control-model controlnet-canny-sd15:fp16
mold run "interior" --control depth.png --control-model controlnet-depth-sd15:fp16 --control-scale 0.8

Piping

mold is pipe-friendly. When stdout is not a TTY, image bytes go to stdout and status to stderr:

mold run "a cat" | viu -                           # Preview in terminal
mold run "a cat" | convert - output.webp           # Convert format
echo "a dog in space" | mold run flux2-klein        # Prompt from stdin
cat photo.png | mold run "style" --image - | viu - # Full pipeline

Force stdout in interactive mode: mold run "a cat" --output -

Upscaling

Upscale images to 2x or 4x resolution using Real-ESRGAN super-resolution models.

# Upscale with default model (real-esrgan-x4plus:fp16, auto-downloads ~32MB)
mold upscale photo.png

# Choose a specific model
mold upscale photo.png -m real-esrgan-x4plus-anime:fp16

# Custom output path
mold upscale photo.png -o photo_4x.png

# Display upscaled image inline
mold upscale photo.png --preview

# Pipe: generate then upscale
mold run "a cat" | mold upscale -

# Force local (skip server)
mold upscale photo.png --local

# Smaller tile size for limited VRAM
mold upscale large_photo.png --tile-size 256

Available Upscaler Models

Model	Scale	Size	Best For
real-esrgan-x4plus:fp16	4x	32 MB	General photos (default)
real-esrgan-x4plus:fp32	4x	64 MB	General photos (full precision)
real-esrgan-x2plus:fp16	2x	32 MB	Subtle 2x enhancement
real-esrgan-x4plus-anime:fp16	4x	8.5 MB	Anime/illustration
real-esrgan-anime-v3:fp32	4x	2.4 MB	Fast anime/video

Model Management

mold list                    # List downloaded + available models
mold pull flux-dev:q4        # Download a model
mold pull flux-dev:q4 --skip-verify  # Download, skip SHA-256 check
mold default                 # Show current default model and how it was resolved
mold default flux-dev:q4     # Set default model (validates name, warns if not downloaded)
mold info                    # Installation overview (paths, models, server status)
mold info flux-dev:q4        # Show model details and file sizes
mold rm flux-dev:q4          # Remove a downloaded model
mold rm flux-dev:q4 --force  # Remove without confirmation

Model discovery catalog

Browse: web UI /catalog route — sidebar, topbar, card grid, detail drawer. Every read is a live HF + Civitai proxy through GET /api/catalog/search with a 5-min in-process cache (no SQLite catalog table, no scanner, no scrape).

Pull catalog ids: mold pull hf:author/repo and mold pull cv:618692 hit the upstream APIs directly for the recipe. Phase-1 supports HF separated-bundling entries; phases 2 (SD1.5/SDXL), 3 (FLUX 1.x), 4 (Z-Image), and 5 (LTX-Video / LTX-2 / LTX-2.3) single-file Civitai checkpoints are supported — downloaded with companions and runnable via mold run cv:<id>. Z-Image fine-tunes pull z-image-te (Tongyi-MAI Qwen3 shards + tokenizer + fallback VAE; satisfied by an existing z-image-turbo install) and use recipe-provided text-encoder files when the Civitai version publishes them. Flux.2 fine-tunes pull flux2-vae (168 MB Klein VAE, ungated) and either flux2-te (Qwen3-4B, ungated, for sub_family=klein-4b) or flux2-te-9b (Qwen3-8B, HF_TOKEN required, for klein-9b / flux2-d). Phase-5 LTX-Video entries pull ltx-video-vae as a companion (Civitai fine-tunes are transformer-only); LTX-2/2.3 entries require a combined checkpoint with bundled VAE. Single-file format detection is key-based (reads safetensors header only).

Auth: HF_TOKEN for gated HF repos; CIVITAI_TOKEN for early-access / NSFW Civitai. Web Settings persists these to mold.db settings (huggingface.token, civitai.token).

Internals: mold-catalog::live is the proxy + cache; live::fetch_civitai_version and live::fetch_hf_repo resolve single ids to a CatalogEntry with a fully-rendered DownloadRecipe. Per-install mold-catalog.json sidecars sit next to each downloaded primary file and back the LoRA picker's "what's installed" list — sidecars travel with the model file, so a copy to another mold install retains trigger words.

Configuration Management

View and edit settings from the CLI using dot-notation keys. Settings are split between config.toml (paths, ports, credentials) and mold.db (user preferences — expand.*, generation defaults, per-model generation overrides). mold config routes by key prefix transparently.

mold config list                          # Show all settings grouped by section
mold config list --json                   # Machine-readable output
mold config get server_port               # Get a single value
mold config get server_port --raw         # Raw value for scripting
mold config set server_port 8080          # Bootstrap key → written to config.toml
mold config set expand.enabled true       # User-preference → written to mold.db
mold config set default_width 1024        # Generation default → written to mold.db
mold config set output_dir none           # Clear an optional field
mold config set models.flux-dev:q4.default_steps 30   # Per-model generation default → model_prefs (DB)
mold config where expand.enabled          # Print "db" or "file" so operators know the surface
mold config reset expand.enabled          # Drop the DB row; next read falls back to config.toml/env/default
mold config reset --all --yes             # Drop every DB row under the active profile
mold config --profile portrait set default_steps 30   # Scope a command to an explicit profile (v6)
mold config path                          # Show config file location
mold config edit                          # Open config.toml in $EDITOR

Keys use dot-notation matching the TOML / DB layout. Boolean values accept true/false, on/off, or 1/0. Use none to clear optional fields. Values are validated (port range, enum options, numeric bounds) before saving. Environment variable overrides are shown when active. mold config list output tags each row with its surface ([db] / [file] / [env]), and mold config set tags the surface it wrote to (e.g. Set expand.enabled = true [db]).

On first launch after upgrading from a pre-#265 release, mold imports the [expand]/generation-defaults slices of config.toml into the DB (gated by config.migrated_from_toml), renames the original config.toml to config.toml.migrated as a one-release downgrade safety net, and rewrites config.toml as a stripped bootstrap-only file (paths, ports, credentials, per-model file paths — nothing the DB now owns). Multi-profile scoping landed in schema v6: set MOLD_PROFILE=dev or pass --profile dev to any mold config subcommand.

Self-Update

mold update                       # Update to latest GitHub release
mold update --check               # Check for updates without installing
mold update --version v0.6.0      # Install a specific version
mold update --force               # Reinstall even if already up-to-date

Downloads the correct platform-specific binary from GitHub releases, verifies SHA-256 checksum, and replaces the running binary in-place. Detects Nix/Homebrew installations and suggests using the package manager instead. Respects GITHUB_TOKEN for API rate limits and MOLD_CUDA_ARCH for GPU architecture override on Linux.

RunPod Cloud GPUs

Manage RunPod pods end-to-end from mold. All subcommands use the REST API at https://rest.runpod.io/v1/ plus GraphQL for account info and GPU/datacenter discovery (those aren't exposed via REST).

One-time setup

# Get an API key at https://www.runpod.io/console/user/settings (Read/Write scope)
mold config set runpod.api_key <key>             # persist to config.toml
# or
export RUNPOD_API_KEY=<key>                      # env var (overrides config)

mold runpod doctor                               # verify auth + balance

Killer feature — mold runpod run

Creates a pod if needed, waits for the mold server inside to boot, generates via SSE (so it survives RunPod's 100s Cloudflare proxy timeout), saves the output, and leaves the pod warm for reuse.

mold runpod run "a cat on a skateboard"          # smart defaults
mold runpod run "a sunset" --model flux-dev:q4   # preload a model
mold runpod run "a cat" --gpu 5090               # force GPU family
mold runpod run "a cat" --dc US-IL-1             # pin datacenter
mold runpod run "a cat" --network-volume nv-abc123 # persistent /workspace
mold runpod run "a cat" --keep                   # don't park — leave running
mold runpod run "a cat" --steps 28 --seed 42     # forward standard flags
mold runpod run "a cat" --output-dir ./renders   # custom save path

Outputs save to ./mold-outputs/runpod-<pod-id>-<ts>.png (directory auto-created, .gitignore'd by default).

Full subcommand reference

# Discovery
mold runpod gpus                                 # table view, aggregate stock
mold runpod gpus --json                          # machine-readable
mold runpod gpus --all                           # include uncommon GPU families
mold runpod datacenters --gpu "RTX 5090"         # per-DC availability

# Lifecycle
mold runpod create --gpu 5090                    # smart defaults fill the rest
mold runpod create --dry-run                     # print plan, don't create
mold runpod create --cloud community             # secure is default
mold runpod create --hf-token                    # wire HF_TOKEN secret into pod env
mold runpod create --network-volume nv-abc123    # attach pre-created volume
mold runpod network-volume list                  # list persistent volumes
mold runpod network-volume get nv-abc123         # inspect one volume
mold runpod network-volume create --name models --size 100 --dc US-KS-2
mold runpod network-volume update nv-abc123 --size 200 # grow only
mold runpod network-volume update nv-abc123 --name shared-models
mold runpod network-volume delete nv-abc123      # permanently deletes data
mold runpod list
mold runpod list --json
mold runpod get <pod-id>
mold runpod stop <pod-id>                        # pause billing, keep storage
mold runpod start <pod-id>                       # resume
mold runpod delete <pod-id>                      # immediate, non-interactive teardown

# Connecting
mold runpod connect <pod-id>                     # print export MOLD_HOST=…
eval "$(mold runpod connect <pod-id>)"           # exec the export in your shell
mold runpod connect <pod-id> --check             # also probe the pod first

# Observability
mold runpod logs <pod-id>                        # validate pod + print console logs handoff
mold runpod usage                                # balance + active pods
mold runpod usage --since 7d                     # with historical spend window
mold runpod usage --json                         # machine-readable

Production network volumes accept 10–3999 GB. Current Pod list/get responses identify attachments with networkVolumeId and assigned GPUs with machine.gpuTypeId; callers must handle those shapes even when expanded networkVolume / gpu.displayName fields are absent. Volume-backed Pods use Secure Cloud in the volume's datacenter, request a 0 GB ordinary workspace disk, cannot be stopped, and must be deleted before the volume can be removed.

Config keys under [runpod]

Key	Description
api_key	API key (env RUNPOD_API_KEY wins). Redacted in config list.
default_gpu	Pin a GPU family (e.g. RTX 5090). Overrides smart-pick.
default_datacenter	Pin a datacenter (e.g. EUR-IS-2). Overrides smart-pick.
default_network_volume_id	Attach a network volume to every new pod.
auto_teardown	If true, delete pods after run instead of parking.
auto_teardown_idle_mins	Idle reap window (default 20). 0 disables.
cost_alert_usd	Abort a session that exceeds this many USD. 0 disables.
endpoint	Override REST base URL (mostly for testing).

All settable via mold config set runpod.<key> <value>. Clear with none.

Smart defaults

When --gpu/--dc aren't pinned:

1. Aggregate stock across datacenters per GPU family.
2. Pick cheapest family with High or Medium stock from: 4090 > 5090 > L40S > A100.
3. Image tag derived from GPU: Ada (4090/L40S) → :latest, Ampere (A100/3090) → :latest-sm80, Blackwell (5090) → :latest-sm120.
4. No datacenter pin — let RunPod's scheduler pick any machine.
5. If scheduling stalls (runtime still null + machine unassigned after 90s), delete the stuck pod and try the next stock-ranked DC.

Common failure modes

• "pod didn't schedule within 90s" — RunPod capacity signal (stockStatus) is optimistic. The scheduler couldn't actually place a machine. Retry fallback handles this; if all candidates fail, capacity genuinely isn't there.
• "value must be one of …" on /pods — you pinned a datacenter that isn't in RunPod's REST enum whitelist. GraphQL exposes more DCs than REST accepts. Omit --dc or pick from the REST-accepted list in the error message.
• Cloudflare 404 during boot — the mold server inside the pod hasn't started yet. wait_for_mold polls /api/status for valid JSON with a version field to distinguish proxy-404 from real readiness.

State persistence

$MOLD_HOME/ (default ~/.mold/) holds:

• runpod-state.json — warm-pod pointer used by run for reuse.
• runpod-history.jsonl — append-only log used by usage --since.

Safe to delete; they're caches, not sources of truth.

Server Mode

mold serve                           # Start foreground server on 0.0.0.0:7680
mold serve --port 8080               # Custom port

# Daemon management (background server)
mold server start                    # Start background server daemon
mold server start --port 8080        # Custom port
mold server start --bind 127.0.0.1   # Custom bind address
mold server start --models-dir /path # Custom models directory
mold server start --log-file         # Enable file logging
mold server status                   # Show managed server status (PID, port, uptime, models)
mold server stop                     # Graceful shutdown (HTTP → SIGTERM → SIGKILL)

mold ps                              # Check server status
mold unload                          # Free GPU memory

# LAN auto-discovery (mDNS/DNS-SD, `mdns` feature — on in release builds)
mold server discover                 # Browse the network for advertised mold servers (table)
mold server discover --json          # Machine-readable list
mold server discover --probe         # Add a /health latency column
mold serve --no-mdns                 # Don't advertise this server (also MOLD_MDNS=0)

# Connect from another machine
MOLD_HOST=http://gpu-host:7680 mold run "a cat"

# Custom image output directory (default: ~/.mold/output/)
MOLD_OUTPUT_DIR=/srv/mold/output mold serve

HTTP API Endpoints

Core endpoints exposed by mold serve (full list + schemas at /api/docs):

• POST /api/generate — image/video generation, raw bytes response
• POST /api/generate/stream — SSE progress + base64 complete event
• POST /api/generate/chain — chained arbitrary-length video (LTX-2 distilled); body is mold_core::chain::ChainRequest (canonical stages[] or auto-expand prompt+total_frames+clip_frames); executes through the durable chain-job runner while keeping the legacy response shape
• POST /api/generate/chain/stream — same as above, SSE progress with per-stage denoise_step events and additive job_id fields on progress frames
• Durable chain jobs:
  • POST /api/chain-jobs · GET /api/chain-jobs · GET /api/chain-jobs/:id
  • GET /api/chain-jobs/:id/events — SSE snapshot + live job events
  • POST /api/chain-jobs/:id/resume · POST /api/chain-jobs/:id/retake · POST /api/chain-jobs/:id/cancel
  • DELETE /api/chain-jobs/:id · POST /api/chain-jobs/gc · GET /api/chain-jobs/:id/stages/:idx/preview
• POST /api/expand — LLM prompt expansion
• GET /api/models · GET /api/loras · POST /api/models/load · POST /api/models/pull · DELETE /api/models/unload
• DELETE /api/models/:model — remove a downloaded model (HTTP mold rm): deletes only exclusively-owned files, keeps shared components, returns { removed, kept, freed_bytes }; 409 while loaded
• GET /api/gallery · GET /api/gallery/image/:name · GET /api/gallery/thumbnail/:name · DELETE /api/gallery/image/:name
• GET/POST /api/downloads · DELETE /api/downloads/:id · GET /api/downloads/stream — bounded-parallel model pulls (two active); listings expose active_jobs plus legacy first-job active; cancel works for queued and active jobs
• POST /api/upscale · POST /api/upscale/stream
• GET /api/queue — authoritative server-side job listing for SPA reconciliation (queued + running jobs with UUIDv4 ids)
• DELETE /api/queue/:id — cancel a still-queued generation job (204; 404 unknown; 409 once running)
• GET /api/history?query=&limit= · DELETE /api/history[?keep=N] — prompt history (newest first, substring filter, limit ≤ 500; 503 when the metadata DB is disabled)
• GET /api/config · GET/PUT/DELETE /api/config/:key — the mold config verbs over HTTP: rows are { key, value, source: db|file|env, env_var? }; PUT routes by surface like config set (403 on env-overridden keys), DELETE resets DB-backed keys like config reset
• GET /api/config/profiles · PUT /api/config/profile — list/switch the active settings profile (503 when the metadata DB is disabled)
• GET /api/status · GET /health · GET /api/capabilities

Prometheus Metrics

When built with the metrics feature flag (included in Docker images and Nix builds), the server exposes a GET /metrics endpoint in Prometheus text exposition format. This endpoint is excluded from auth and rate limiting for monitoring scrapers.

Metrics include: HTTP request rates/latency, generation duration, queue depth, model load tracking, GPU memory usage, and server uptime.

Key Environment Variables

Variable	Default	Purpose
MOLD_HOME	~/.mold	Base directory for config, cache, and default models
MOLD_DEFAULT_MODEL	flux2-klein:q8	Default model (smart fallback to only downloaded model)
MOLD_HOST	http://localhost:7680	Remote server URL
MOLD_MODELS_DIR	$MOLD_HOME/models	Model storage path
MOLD_OUTPUT_DIR	~/.mold/output	Image output directory (set empty to disable)
MOLD_THUMBNAIL_WARMUP	unset	Set 1 to prebuild gallery thumbnails at server startup
MOLD_PORT	7680	Server port
MOLD_LOG	warn	Log level (trace/debug/info/warn/error)
MOLD_EAGER	unset	Set 1 to keep all components loaded
MOLD_OFFLOAD	unset	Set 1 to force block offload for FLUX, Flux.2, Z-Image, Qwen-Image, LTX-2, and SD3 BF16/FP8 paths where implemented. FLUX/Flux.2/Z-Image/Qwen keep fitting blocks GPU-resident; LTX-2 full-streams; SD3 still full-streams.
MOLD_RESERVE_VRAM_MB	400 (Linux), 600 (Win), 0 (macOS)	OS / cuBLAS workspace reserve subtracted from free_vram_bytes before any budget decision. 0 disables
MOLD_KEEP_TE_RAM	unset	Set 1 to park text encoders on CPU between requests instead of dropping them (FP16/BF16 only; GGUF falls through to drop+reload). Disabled on Metal.
MOLD_LORA_BYPASS	auto	FLUX LoRA application path: auto (bypass when LoRAs present, covers offload AND GGUF/quantized via quantized_transformer.rs), on (always bypass), off (legacy merge / gguf_lora_var_builder)
MOLD_STEP_PREVIEW	1	Live denoise previews on /api/generate/stream (preview SSE events, FLUX.1/Flux.2/Z-Image): latent-resolution PNG per step from the x0 estimate via linear latent→RGB. 0 disables.
MOLD_VAE_TILED	auto	Tiled VAE decode for FLUX/FLUX2/SDXL/SD3: auto (retry on OOM), force (always tile), off (disable). Saves VRAM when transformer + LoRAs are still resident.
MOLD_LONG_PROMPTS	unset	Set 1 to enable ComfyUI-style chunked CLIP encoding (75-token windows; pooled outputs averaged into FLUX's 768-dim vector_in). Default off — pre-Tier-2 truncation at 77 preserved.
MOLD_ATTN	math	Attention backend: math (hand-rolled SDP, default) or flash (candle-flash-attn v2; needs --features cuda,flash-attn + RUSTFLAGS='--cfg mold_flash_attn_real' — falls back to math otherwise)
MOLD_ATTN_CHUNK	auto	Override math-attention query chunk size. Positive integers below sequence length enable chunking; 0 / off disables. CUDA auto-chunks long queries at 512.
MOLD_FLUX_DELTA_CACHE	1	Set 0 to disable FLUX LoRA delta caching, reducing standing host RAM during GGUF + LoRA rebuilds at the cost of recompute on the next rebuild.
MOLD_FLUX_KEEP_TRANSFORMER	0	Set 1 to keep the FLUX transformer loaded through VAE decode when enough VRAM headroom remains; mold force-drops per request when decode headroom is too low.
MOLD_OFFLOAD_PREFETCH	on	FLUX offload async H2D prefetch stream (off reverts to synchronous)
MOLD_PINNED_VRAM_MAX_GB	RAM × 0.5 (Linux)	Cap on cumulative pinned host memory used by the FLUX offload path
MOLD_EMBED_METADATA	1	Set 0 to disable PNG metadata
MOLD_MEDIA_ROOTS	unset	Platform path-list of allow roots for trusted server-local LTX-2 audio_file_path / source_video_path API requests. Canonical target files must stay under one configured root.
MOLD_PREVIEW	unset	Set 1 to display generated images inline in the terminal
MOLD_T5_VARIANT	auto	T5 encoder: auto/fp16/q8/q6/q5/q4/q3
MOLD_QWEN3_VARIANT	auto	Qwen3 encoder: auto/bf16/q8/q6/iq4/q3
MOLD_SCHEDULER	unset	SD1.5/SDXL: ddim/euler-ancestral/uni-pc
MOLD_CFG_PLUS	unset	Set 1 to enable CFG++ (manifold-projection guidance). Drops usable CFG to ~1.5–2.5, removes guidance artifacts. Per-request --cfg-plus overrides. Supported on SD3, SDXL, and SD1.5 (DDIM scheduler only — Euler-A / UniPC fall back to standard CFG with a warn). Ignored by guidance-distilled families (FLUX, Z-Image, Flux.2) and at cfg ≈ 1.0.
MOLD_VAE_DTYPE	auto	Override VAE precision: auto (per-pipeline default), bf16, fp16, fp32. Use fp32 to fix banding artifacts on FLUX/SD3 finetuned VAEs (~2× decode VRAM; tiled VAE absorbs OOM). Wired into FLUX, FLUX2, SD3, SDXL, SD1.5; no-op for Z-Image CPU VAE / Wuerstchen / Qwen-Image (already F32).
MOLD_NVFP4_BACKEND	auto	NVFP4 backend for Flux.2 and LTX-2: auto / portable use CPU BF16 streaming dequant; native is reserved for validated sm_120/Blackwell tensor-core execution and fails clearly on non-Blackwell hosts.
MOLD_LTX2_GEMMA_DEVICE	auto	LTX-2 Gemma 3 12B prompt encoder placement: auto (active GPU → sibling GPUs → CPU based on free VRAM, threshold 24 GB), cpu (force system RAM, ~30–60 s encode vs ~1–3 s on GPU but no VRAM contention), gpu (force the active GPU, surface OOM rather than auto-offload). The deprecated MOLD_LTX2_DEBUG_FORCE_CPU_PROMPT_ENCODER=1 is a one-shot-warn alias for cpu. Server-side preflight uses the same resolver so cv:2752735 admits/rejects in lockstep with what the runtime will do.
MOLD_LTX2_GEMMA_VARIANT	auto	LTX-2 Gemma 3 12B weight format: auto (BF16 if both formats present, GGUF if only GGUF), q4 (force Q4 GGUF — google/gemma-3-12b-it-qat-q4_0-gguf, ~7 GB; fits comfortably on a 24 GB card alongside the streaming transformer), bf16 (force BF16 split — google/gemma-3-12b-it-qat-q4_0-unquantized, ~23 GB; historical default). Auto-detection scans the gemma_root for *.gguf and model*.safetensors. Place the Q4 GGUF manually in your gemma_root for V1 — manifest auto-fetch is deferred.
MOLD_LTX2_VAE_FORCE_FULL_DECODE	unset	Set 1 to disable adaptive temporal chunked LTX-2 VAE decode and force one full decode pass. Useful for debugging/comparison; long or high-resolution clips may OOM.
MOLD_LTX2_VAE_FORCE_FRAMEWISE	unset	Set 1 to force temporal-chunk LTX-2 VAE decode even when a full decode would fit. Reduces peak VRAM at a small decode-time cost.
MOLD_LTX2_VAE_DECODE_CHUNK_FRAMES	4 latent frames	Positive integer number of latent frames per LTX-2 VAE decode chunk when chunked decode is active.
MOLD_LTX2_VAE_DECODE_CONTEXT_FRAMES	auto	Positive integer latent-frame overlap/context around each LTX-2 decode chunk. Default derives from the decoder causal-conv receptive field.
MOLD_MAX_CACHED_MODELS	3	Maximum cached engines, including the GPU-resident model and parked entries. Range: 1-16.
MOLD_CACHE_IDLE_TTL_SECS	1800	Idle TTL for parked cache entries before background eviction. Range: 60-86400; the GPU-resident model is never evicted for age.
MOLD_QUEUE_LOOKAHEAD_BUFFER	8	Number of queued jobs considered for same-loaded-model locality reordering. Range: 1-64.
MOLD_QUEUE_MAX_DEFERRALS	3	Maximum times the head job can be deferred for locality before force-dispatch. Range: 0-32; 0 disables deferral.
MOLD_MALLOC_TRIM	1	Linux/glibc only: set 0 to skip malloc_trim(0) after generation.
MOLD_API_KEY	unset	API key for server auth (single, comma-separated, or @/path/to/keys.txt)
MOLD_RATE_LIMIT	unset	Per-IP rate limit for generation endpoints (e.g., 10/min)
MOLD_RATE_LIMIT_BURST	unset	Burst allowance override (defaults to 2x rate)
MOLD_CORS_ORIGIN	unset	Restrict server CORS to specific origin
MOLD_UPSCALE_MODEL	unset	Default upscaler model for mold upscale
MOLD_UPSCALE_TILE_SIZE	unset	Tile size for memory-efficient upscaling (0 to disable)
MOLD_EXPAND	unset	Set 1 to enable prompt expansion by default
MOLD_EXPAND_BACKEND	local	Expansion backend: local or OpenAI-compatible API URL
MOLD_EXPAND_MODEL	qwen3-expand:q8	LLM model for local expansion
MOLD_EXPAND_TEMPERATURE	0.7	Sampling temperature for expansion
MOLD_EXPAND_THINKING	unset	Set 1 to enable thinking mode in expansion LLM
MOLD_EXPAND_SYSTEM_PROMPT	unset	Custom single-expansion system prompt template
MOLD_EXPAND_BATCH_PROMPT	unset	Custom batch-variation system prompt template
HF_TOKEN	unset	HuggingFace token for gated models

Inference Modes

1. Remote (default): connects to mold serve via HTTP
2. Local fallback: if server unreachable, auto-falls back to local GPU
3. Local forced (--local): skip server, run on local GPU directly

Models auto-pull if not downloaded: mold run flux2-klein "a cat" will download the model first if needed.

Practical Tips

• Use flux2-klein:q8 for fast iterations (4 steps, ~10s on RTX 4090)
• Use flux-dev:q4 for final quality images (25 steps)
• Use --seed for reproducibility — same seed + same prompt = same image
• Quantized models (q4/q6/q8) use less VRAM than fp16/bf16
• FP8 safetensors models auto-convert to Q8 GGUF on first use (fits 24GB cards)
• --eager trades VRAM for speed (keeps encoders loaded between generations)
• Dimensions must be multiples of 16; total pixels capped at ~1.1 megapixels
• For img2img, source images auto-resize to fit the model's native resolution (preserving aspect ratio). A 1024x1024 source with SD1.5 (512x512 native) generates at 512x512; a 1920x1080 source generates at 512x288. Use --width/--height to override
• Set MOLD_HOME to relocate all mold data (config, cache, models)
• LoRA adapters require FLUX BF16 models; use --lora-scale 0.5-0.8 for subtle effects
• On 24GB cards, use --offload with BF16 FLUX / Flux.2 / Z-Image / Qwen-Image / SD3 when quantization is not acceptable, and with LTX-2 when you want the conservative full-streaming path. FLUX / Flux.2 / Z-Image / Qwen-Image keep fitting blocks resident; LTX-2 and SD3 full-stream.

Discord Bot

Mold includes an optional Discord bot that bridges Discord slash commands to a running mold serve instance. The bot depends only on mold-core (HTTP client) — no GPU needed on the bot host.

Running

# Run server + bot in one process
MOLD_DISCORD_TOKEN="your-bot-token" mold serve --discord

# Or run the bot separately (connects to a remote server)
export MOLD_DISCORD_TOKEN="your-bot-token"
export MOLD_HOST="http://gpu-host:7680"  # optional, defaults to localhost
mold discord

Slash Commands

• /generate <prompt> [model] [width] [height] [steps] [guidance] [seed] — generate an image
• /expand <prompt> [model_family] [variations] — expand a short prompt into detailed image generation prompts
• /models — list available models with status
• /status — show server health, GPU info, uptime
• /quota — check remaining daily generation quota
• /admin reset-quota @user — reset a user's daily quota (requires Manage Server)
• /admin block @user — temporarily block a user from generating (requires Manage Server)
• /admin unblock @user — unblock a previously blocked user (requires Manage Server)

Environment Variables

Variable	Default	Description
MOLD_DISCORD_TOKEN	—	Bot token (falls back to DISCORD_TOKEN)
MOLD_HOST	http://localhost:7680	mold server URL
MOLD_DISCORD_COOLDOWN	10	Per-user cooldown (seconds)
MOLD_DISCORD_ALLOWED_ROLES	—	Comma-separated role names/IDs for access control (unset = all)
MOLD_DISCORD_DAILY_QUOTA	—	Max generations per user per UTC day (unset = unlimited)

NixOS

services.mold.discord = {
  enable = true;
  package = inputs.mold.packages.${system}.mold-discord;
  tokenFile = config.age.secrets.discord-token.path;
};

Desktop App

An experimental native macOS desktop app (Tauri 2 + Vue 3) lives in desktop/. It embeds mold serve in-process on Metal, auto-detects a running server on localhost:7680, and can point at a remote host. It covers the generation workspace (live "Develop" progress), local and remote galleries, parallel cancellable model pulls, the chains editing bench, prompt history, full RunPod pod and network-volume lifecycle management, full-resolution image clipboard copy, persistent 80–130% whole-app scaling (⌘+/⌘−/⌘0), and provenance-tagged settings, with a ⌘K command palette. RunPod volume selection persists; volume-backed launches force Secure Cloud in the volume's datacenter and omit the redundant workspace disk.

Devshell commands (run inside nix develop):

desktop-dev        # Tauri app with hot reload (Vite on :1430)
desktop-build      # build the Mold.app bundle
desktop-check      # CI gate: rustfmt, clippy, vue-tsc, prettier
desktop-test       # cargo test + vitest
desktop-ui         # frontend-only Vite server (pair with a running `serve`)
desktop-bun-lock   # regenerate desktop/bun.nix from bun.lock

Updating This Skill

This skill is maintained in the mold repository on GitHub. To pull the latest version:

# Source repository
https://github.com/utensils/mold

# Skill file location within the repo
.claude/skills/mold/SKILL.md

# Fetch the latest skill directly
curl -sL https://raw.githubusercontent.com/utensils/mold/main/.claude/skills/mold/SKILL.md \
  -o ~/.claude/skills/mold/SKILL.md

When copying this skill to other workspaces or agents, always pull from main to get the latest model support, CLI flags, and environment variables.