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AI image generation toolkit — generate images, edit photos, train LoRAs, manage models, analyze images, upscale, remove backgrounds, and more via the modl CLI.
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
| name | modl |
| description | AI image generation toolkit — generate images, edit photos, train LoRAs, manage models, analyze images, upscale, remove backgrounds, and more via the modl CLI. |
| triggers | ["/modl","generate an image","generate a photo","create an image","make an image","txt2img","img2img","inpaint","image generation","edit an image","edit this photo","image editing","train a lora","train a model","lora training","fine-tune","fine tune","pull a model","download a model","install a model","list models","search models","what models","describe this image","caption this image","score this image","image quality","detect faces","ground","upscale","remove background","segment","compose images","composite","layer images","enhance prompt","improve prompt","modl generate","modl edit","modl train","modl pull","modl search","modl vision","modl process","modl serve","modl dataset","modl gpu","modl run","modl status","run a workflow","batch generate","multi-step workflow","generate then edit","fire and forget"] |
| invocable | true |
| argument-hint | [action] [args...] |
modl is a local-first CLI for AI image generation: pull models, generate images, edit photos, train LoRAs, manage outputs. Like Ollama for image gen.
--json output when available. Parse JSON for file paths, status, and structured data. Fall back to raw stdout only when --json isn't supported.modl ls before generating. If the user's requested model isn't installed, offer to modl pull it.modl generate, modl edit, modl train, and modl pull can take seconds to hours. For training, use modl train status --watch to monitor.~/.modl/outputs/ by default. Always return full paths to the user.~/.modl/outputs/ is the user's generation library, not a cache. Never remove files from it.modl info <model> for the canonical prompting guide before writing prompts (prose vs tags, negative-prompt support, text-in-quotes). See Per-Model Prompting below.modl run (with --dry-run first to validate) instead of looping individual commands. Use seeds: [...] for variations, then modl status <run-id> to collect results.| Task | Command |
|---|---|
| Generate image | modl generate "prompt" --base flux-schnell --json |
| Generate with LoRA | modl generate "prompt" --lora my-style --json |
| img2img | modl generate "prompt" --init-image photo.jpg --strength 0.7 --json |
| Inpaint | modl generate "prompt" --init-image photo.jpg --mask mask.png --json |
| Edit image | modl edit "make the sky sunset" --image photo.jpg --json |
| Fast generation | modl generate "prompt" --fast --json |
| Train LoRA | modl train --base flux-dev --lora-type style --dataset my-photos --name my-style |
| Check training | modl train status --json |
| Pull model | modl pull flux-dev |
| List installed | modl ls |
| Search registry | modl search "anime" --json |
| Search CivitAI | modl search "realistic" --civitai --json |
| Describe image | modl vision describe photo.jpg --json |
| Score quality | modl vision score photo.jpg --json |
| Detect faces | modl vision detect photo.jpg --json |
| Ground objects | modl vision ground "the red car" photo.jpg --json |
| Upscale 4x | modl process upscale photo.jpg --json |
| Remove background | modl process remove-bg photo.jpg --json |
| Segment object | modl process segment photo.jpg --bbox 100,100,400,400 --json |
| Compose layers | modl process compose --background bg.png --layer subject.png --position 0.5,0.7 --scale 0.3 |
| Enhance prompt | modl enhance "a cat" --json |
| Serve web UI | modl serve |
| GPU status | modl worker status |
For quick iterations / drafts:
flux-schnell — 4 steps, fast, good quality. The default.z-image-turbo — 8 steps, good for stylized images.ernie-image-turbo — 8 steps, best for structured layouts and text-heavy images.For high quality finals:
flux-dev — 28 steps, excellent quality, most LoRA support.z-image — 20 steps, strong on realistic/artistic styles.chroma — 20 steps, Apache 2.0 Flux fork, supports negative prompts.qwen-image — 25 steps, best text rendering in images.ernie-image — 50 steps, best for complex structured content (posters, infographics, multi-panel, character sheets). Needs long, detailed prompts.For editing (instruction-based):
qwen-image-edit-2511 — Best edit quality, 40 steps, needs 30GB+ VRAM. The default for modl edit.klein-4b — Fast edits, 4 steps, fits in 10GB VRAM.klein-9b — Better edit quality, 4 steps, needs 16GB VRAM.For inpainting (mask-based):
flux-fill-dev — Purpose-built for inpainting, best quality, 50 steps.flux-dev, flux-schnell, z-image, sdxl — All support inpainting via --mask.For training LoRAs:
flux-dev — Best ecosystem, most LoRA recipes.flux-schnell — Fast iteration on LoRA experiments.z-image — Good alternative for stylized LoRAs.sdxl — Lower VRAM, mature ecosystem.Low VRAM (< 12GB):
sdxl (5-7 GB), sd-1.5 (3-4 GB), klein-4b (10 GB fp8)| Model | Steps | Guidance | VRAM (fp8) | txt2img | img2img | inpaint | edit | train |
|---|---|---|---|---|---|---|---|---|
| flux-schnell | 4 | 1.0 | 20 GB | Y | Y | Y | Y | |
| flux-dev | 28 | 3.5 | 20 GB | Y | Y | Y | Y | |
| flux-fill-dev | 50 | 30.0 | 20 GB | Y | ||||
| flux2-dev | 28 | 4.0 | 35 GB | Y | Y | |||
| klein-4b | 4 | 1.0 | 10 GB | Y | Y | Y | ||
| klein-9b | 4 | 1.0 | 16 GB | Y | Y | Y | ||
| chroma | 20 | 4.0 | 16 GB | Y | Y | Y | Y | |
| z-image | 20 | 4.0 | 14 GB | Y | Y | Y | Y | |
| z-image-turbo | 8 | 0.0 | 14 GB | Y | Y | Y | Y | |
| qwen-image | 25 | 3.0 | 30 GB | Y | Y | |||
| qwen-image-edit-2511 | 40 | 4.0 | 30 GB | Y | ||||
| ernie-image | 50 | 4.0 | 14 GB | Y | Y | Y | ||
| ernie-image-turbo | 8 | 1.0 | 14 GB | Y | Y | Y | ||
| sdxl | 30 | 7.5 | 5 GB | Y | Y | Y | Y | |
| sd-1.5 | 30 | 7.5 | 3 GB | Y | Y | Y | Y |
Default steps and guidance are shown — override with --steps and --guidance.
| Preset | Resolution |
|---|---|
1:1 | 1024x1024 (default) |
16:9 | 1344x768 |
9:16 | 768x1344 |
4:3 | 1152x896 |
3:4 | 896x1152 |
| Custom | WxH (e.g. 1920x1080) |
Each model wants a different prompting style. modl info <model> prints the canonical prompting guide for any model (and list_models over MCP includes them) — check it before writing prompts for a model you haven't used in this session. Summary:
| Model | Style | Negative prompts |
|---|---|---|
| flux-dev, flux2-dev | Long descriptive prose: subject, setting, lighting, camera, style | no effect |
| flux-schnell, klein-4b/9b | Short focused prose, subject first (4-step distilled) | no effect |
| chroma | Descriptive prose + aesthetic keywords | works — use them |
| z-image | Prose with style/lighting detail, EN/ZH | works |
| z-image-turbo | Short-to-medium prose (guidance 0.0) | no effect |
| qwen-image, ernie-image | Long structured prompts; quote exact text to render, EN/ZH | works |
| sdxl, sd-1.5 | Comma-separated tags, key tokens first; SD 1.5 truncates at ~75 tokens | essential |
Editing models (klein-4b/9b, qwen-image-edit-2511): state the change as one imperative command per edit ("change the background to a white studio"), chain multiple edit steps in a workflow instead of stacking changes in one prompt, and say what to preserve ("keep the bracelet exactly identical: same texture, same pattern, same proportions"). For text edits with qwen-image-edit-2511, quote the exact replacement text.
ERNIE-Image (ernie-image, ernie-image-turbo) is an 8B DiT model from Baidu. #1 open-weights model on GenEval, #2 on LongTextBench. Requires a different prompting style than Flux or Z-Image — long, structured prompts (200-600 words) produce dramatically better results than short ones.
When to choose ERNIE over other models:
When NOT to use ERNIE:
ERNIE prompting rules (critical — these differ from other models):
Write long, structured prompts. ERNIE thrives on 200-600 word prompts that read like detailed art direction briefs. Short prompts produce mediocre results. Describe composition, spatial layout, materials, lighting, and typography explicitly.
Specify spatial layout explicitly. Use phrases like "upper-left corner contains X", "the lower-center shows Y", "right side features Z". ERNIE places elements where told.
Embed exact text strings in quotes. For text rendering, include literal strings: reading 'CAFE & ROASTERY', with the title 'ECO-CITY HORIZON', labeled '#C84B31'. ERNIE renders quoted text faithfully.
Use specific technique vocabulary. Not "anime style" but "high-quality cel-shading flat coloring with clean outlines and crisp shadow boundaries." Not "old painting" but "gongbi brushwork with iron-wire line drawing (tiexianmiao)."
Use camera/lens language as precise instructions. "85mm lens", "f/2.8 macro", "tilt-shift", "direct frontal flash" — ERNIE treats these as structural directives, not vibes.
State exclusions explicitly. "No gradients, no 3D effects, no atmospheric perspective" — ERNIE respects negative constraints better than most models.
Describe each panel/section for multi-panel work. For sticker sheets, expression grids, or multi-panel compositions, describe every panel individually: "First sticker: character waves hello with text 'Hi!' beside her. Second sticker: character sits crying with text 'Uwaaa'."
Example — ERNIE-style prompt for a character expression sheet:
modl generate "An illustration presenting a collection of 8 expression stickers arranged in a 2-row by 4-column grid layout. The background is light yellow. The protagonist of all stickers is the same chibi-style girl with pink twin-tail hair, large emerald-green eyes, light blue sailor uniform with a red bow. Each sticker has a thick white outline and faint gray drop shadow. First row from left to right: (1) bright smile with eyes closed, waving, yellow stars around head, pink text 'Hi!'; (2) sitting on ground crying, fists rubbing eyes, blue text 'Uwaaa'; (3) arms crossed, cheeks puffed in anger, red vein mark, red text 'Hmph!'; (4) heart gesture with both hands, blushing, pink hearts floating, text 'Love'. Second row: (5) shocked expression, white dot eyes, hands clutching head, yellow lightning bolts, text 'What?!'; (6) winking, thumbs up, green text 'Good!'; (7) lying face down sleeping, sleep bubble with 'Zzz'; (8) chin on hand thinking, furrowed brows, black question mark above head. Bright saturated colors, cute art style, smooth rounded linework, flat coloring, even lighting." --base ernie-image-turbo --size 16:9
Generate images from text prompts. Supports txt2img, img2img, inpainting, ControlNet, style transfer.
modl generate <prompt> [OPTIONS]
Core:
--base <MODEL> Base model (default: flux-schnell)
--seed <SEED> Random seed for reproducibility
--size <PRESET|WxH> Output size (default: 1:1)
--steps <N> Inference steps (model default if omitted)
--guidance <SCALE> Guidance scale (model default if omitted)
--count <N> Number of images (default: 1)
--json JSON output with file paths
LoRA:
--lora <NAME_OR_PATH> LoRA to apply
--lora-strength <0.0-1.0> LoRA weight (default: 1.0)
img2img:
--init-image <PATH> Source image
--strength <0.0-1.0> Denoising strength (default: 0.75)
Inpainting:
--init-image <PATH> Source image
--mask <PATH> Mask (white=regenerate, black=keep)
--inpaint <METHOD> auto, lanpaint, standard (default: auto)
ControlNet:
--controlnet <PATH> Control image (can use 2x)
--cn-type <TYPE> canny, depth, pose, softedge, scribble, hed, mlsd, gray, normal
--cn-strength <0.0-1.0> Conditioning strength (default: 0.75)
--cn-end <0.0-1.0> Stop at fraction of steps (default: 0.8)
Style Reference:
--style-ref <PATH> Style reference image (can repeat)
--style-strength <0.0-1.0> Style weight (default: 0.6)
--style-type <TYPE> style, face, content
Speed:
--fast [STEPS] Lightning LoRA (4 or 8 steps, default 4)
--no-worker Skip persistent worker, use subprocess
Cloud:
--cloud Run on cloud GPU
--provider <PROVIDER> modal, replicate, runpod
JSON output format:
{"status": "completed", "images": ["/home/user/.modl/outputs/2026-03-25/img_001.png"]}
Edit images using natural language instructions. Different from inpainting — no mask needed.
modl edit <prompt> --image <PATH> [OPTIONS]
--image <PATH> Source image (required, repeatable)
--base <MODEL> Edit model (default: qwen-image-edit-2511)
--seed <SEED> Random seed
--steps <N> Inference steps
--guidance <SCALE> Guidance scale
--count <N> Number of outputs (default: 1)
--size <PRESET|WxH> Output size (for outpainting: larger than source)
--fast [STEPS] Lightning LoRA
--json JSON output
Edit vs Inpainting:
modl edit — instruction-based ("make the sky sunset"), no maskmodl generate --mask — region-based (paint white where you want changes)Train LoRA adapters on your images.
modl train [OPTIONS]
Required:
--base <MODEL> Base model to train on
--lora-type <TYPE> style, character, or object
Dataset:
--dataset <PATH> Dataset name or directory
--name <NAME> Output LoRA name
--trigger <WORD> Trigger word for activation
Hyperparameters:
--preset <PRESET> quick (500 steps), standard (1500), advanced (3000)
--steps <N> Override step count
--rank <N> LoRA rank/dimensionality
--lr <RATE> Learning rate (e.g. 1e-4)
--batch-size <N> Batch size
--resolution <PX> Training resolution
--optimizer <TYPE> adamw8bit, prodigy, adamw, adafactor, sgd
--repeats <N> Dataset repetitions per epoch
--caption-dropout <0-1> Caption dropout rate
--class-word <WORD> Class word (e.g. "man", "woman", "dog")
--resume <PATH> Resume from checkpoint
Control:
--dry-run Preview spec without executing
--config <YAML> Load full TrainJobSpec from file
--cloud Train on cloud GPU
Subcommands:
modl train status [NAME] Show training progress (--watch for live, --json)
modl train ls List training runs
modl train rm <NAME> Delete a training run
modl train setup Install training dependencies (--reinstall)
Training workflow:
modl dataset prepare my-photos --from ./photos/ — resize + auto-captionmodl train --base flux-dev --lora-type character --dataset my-photos --name my-face --trigger "ohwx"modl train status --watch — monitor progressmodl generate "ohwx person in a garden" --lora my-face --jsonModel management commands.
# Download
modl pull <ID> [--variant fp16|fp8|bf16|gguf-q4] [--dry-run] [--force]
ID formats: flux-dev, hf:owner/model, civitai:12345, user/slug
# List installed
modl ls [-t checkpoint|lora|vae|text_encoder|...] [--summary] [-a]
# Remove
modl rm <ID> [--force]
# Search registry
modl search [QUERY] [-t TYPE] [--for MODEL] [--popular] [--civitai] [--json]
# Show details
modl info <ID>
Image understanding and analysis.
# Caption/describe
modl vision describe <PATHS> [--detail brief|detailed|verbose] [--model qwen3-vl-2b|qwen3-vl-8b] [--json]
# Quality scoring
modl vision score <PATHS> [--json]
# Face/object detection
modl vision detect <PATHS> [--type face] [--embeddings] [--json]
# Visual grounding (find objects by description)
modl vision ground <QUERY> <PATHS> [--threshold VALUE] [--model qwen3-vl-2b|qwen3-vl-8b] [--json]
# Image similarity
modl vision compare <PATHS> [--reference PATH] [--json]
Image processing and enhancement.
# Super-resolution
modl process upscale <PATHS> [--scale 2|4] [-o DIR] [--json]
# Background removal (outputs transparent PNG)
modl process remove-bg <PATHS> [-o DIR] [--json]
# Segmentation (create masks)
modl process segment <IMAGE> [--method bbox|background|sam] [--bbox X1,Y1,X2,Y2] [--point X,Y] [--expand PX] [-o PATH] [--json]
# Extract control images
modl process preprocess <METHOD> <IMAGE>
Methods: canny, depth, pose, softedge, scribble, hed, mlsd, lineart
# Compose layers onto a canvas (CPU-only, no GPU needed)
modl process compose --background <PATH|transparent|white|black> --layer <PATH> [OPTIONS]
--position <X,Y> Fractional 0.0-1.0 coordinates (center of layer, default: 0.5,0.5)
--scale <SCALE> Layer scale (>0, default: 1.0)
--opacity <0.0-1.0> Layer opacity (default: 1.0)
--canvas-size <WxH> Required for solid color backgrounds
-o <DIR> Output directory
--json JSON output
# Repeat --layer/--position/--scale/--opacity for multiple layers (order = bottom to top)
Prepare training datasets.
modl dataset prepare <NAME> --from <DIR> Auto pipeline: create + resize + caption
modl dataset create <NAME> --from <DIR> Import images only
modl dataset caption <NAME> Auto-caption with VL model
modl dataset validate <NAME> Check for issues
modl dataset resize <NAME> [--resolution PX]
modl dataset list
modl dataset rm <NAME>
Improve prompts for better generation results.
modl enhance <PROMPT> [--model sdxl|flux|sd3] [--intensity subtle|moderate|aggressive] [--json]
Web UI and GPU worker management.
# Web UI
modl serve [--port 3939] [--no-open] [--foreground]
modl serve --install-service Install as system service
modl serve --remove-service
# Persistent GPU worker (keeps models in VRAM)
modl worker start [--timeout 600]
modl worker stop
modl worker status
Remote GPU session management.
modl gpu attach <SPEC> [--idle 30m|1h] Attach remote GPU
modl gpu detach Shut down GPU instance
modl gpu status Show running instances
modl gpu ssh Shell into GPU
Maintenance and utilities.
modl doctor [--verify-hashes] [--repair] Check installation health
modl system gc Clean unreferenced store files
modl system update Fetch latest registry
modl system link --comfyui <PATH> Symlink models for ComfyUI/A1111
modl config [KEY] [VALUE] View/set configuration
modl upgrade Update modl to latest
modl init [--defaults] First-run setup
Authentication for model sources and hub.
modl auth login Login to modl hub
modl auth logout
modl auth whoami
modl auth add huggingface Configure HuggingFace token
modl auth add civitai Configure CivitAI token
Share LoRAs and datasets.
modl push lora <FILE> --name <SLUG> [--visibility public|private] [--base MODEL] [--trigger WORD]
modl push dataset <DIR> --name <SLUG> [--visibility public|private]
Backup and restore.
modl export <output.tar.zst> [--no-outputs] [--since YYYY-MM-DD]
modl import <backup.tar.zst> [--dry-run] [--overwrite]
Run a multi-step workflow from a YAML spec.
modl run <SPEC.yaml> # execute
modl run a.yaml b.yaml c.yaml # sequential multi-spec
modl run spec.yaml --dry-run # validate + show plan, no execution
modl run spec.yaml --dry-run --json # machine-readable plan
modl run spec.yaml --skip-existing # resume — skip already-generated sub-jobs
YAML format (flat fields — generate: takes the prompt directly; edit: takes the image ref with a separate prompt:):
name: my-workflow
model: flux2-klein-9b # workflow-level default (required)
lora: my-lora # optional, inherited by all steps
defaults: # optional, applied to every step
width: 1024
height: 1024
# Named image variables — define once, reference anywhere as $name.
# Values: base64 data URI (works over remote MCP) or server-side path.
images:
product: "/server/refs/product.png"
style: "data:image/png;base64,iVBORw0..."
steps:
- id: base
generate: "a woven gold bracelet on white studio background"
seeds: [42, 7, 99] # one output per seed (variation exploration)
- id: refine
edit: "$base.outputs[0]" # step-output ref: $step-id.outputs[N]
prompt: "warm morning daylight, soft shadows"
- id: hero
edit: "$product" # image variable ref: $name (no dot)
prompt: "place on travertine stone surface"
model: qwen-image-edit-2511 # per-step model override (disables workflow lora)
Image refs in edit: — three forms: $step-id.outputs[N] (prior step output), $name (from images: map), or a bare server-side path.
Run IDs look like {timestamp}-{workflow-name}. Logs: ~/.modl/run-logs/<run-id>.log.
Check aggregate state of a workflow run.
modl status <run_id> [--json]
MODL_BASE_URL=http://server:3939 modl status <run_id> --json # artifact HTTP URLs
Aggregate values: pending, running, completed, partial_failure, cancelled. partial_failure = all steps terminated, at least one errored.
modl outputs export <run_id> --dest ./out # copy local artifacts
modl outputs export <run_id> --server http://s:3939 --dest ./out # fetch ZIP remotely
modl exposes 15 tools over stdio JSON-RPC 2.0. Activate via modl mcp (configured in Claude Code's MCP settings).
Workflow tools (fire-and-forget pattern):
run_workflow — submit YAML spec, returns run_id immediatelyjob_status — poll aggregate status for a runlist_run_outputs — fetch artifact paths/URLs for a completed runSubmit, then poll job_status later — the run survives client disconnects. Set MODL_BASE_URL=http://server:3939 on the server so returned URLs are reachable from the client.
Image refs over remote MCP: client-side file paths don't exist on the server. Use the workflow images: map with base64 data URIs — encode each reference image once (base64 -i photo.png | tr -d '\n' on Mac), define it under images:, reference it as $name in any edit step. $step-id.outputs[N] chain refs and pre-copied server paths also work.
A pod is a Vast.ai instance rented with your API key (~/.vast_api_key). The pod gets its own modl install (binary + worker from the release tarball), so it runs the exact same code as a local machine: models are modl pulled into the pod's store, the workflow engine runs pod-side, and artifacts sync back when the run finishes. Pods bill until destroyed — modl pod rm is the habit.
modl pod up rtx4090 --model flux-schnell # rent + bootstrap + warm the store
modl generate "a red apple" --base flux-schnell --pod
modl edit --image photo.png "make it golden" --pod
modl run workflow.yaml --pod # whole workflow executes on the pod
modl run workflow.yaml --pod --dry-run # validate + list models, no pod needed
modl pod ls # every instance billing on your account
modl pod rm <id> # destroy — billing stops
--pod on generate needs an explicit --base <model-id> (the model is pulled pod-side).nohup + status polling) — a dropped connection never kills it. If the watching command died (closed laptop), fetch the finished run with modl pod pull <run-id>../pod-outputs/<run-id>/ locally, moved directly over SSH (no cloud storage in between).--pod: --lora, --init-image/--mask, --controlnet, --style-ref, multi-image edit.modl train --pod (separate path — ships the worker directly, syncs the LoRA back, auto-destroys unless --keep-pod).# Draft with fast model
modl generate "portrait of a woman in golden light" --base flux-schnell --json
# Upscale the best result
modl process upscale /path/to/output.png --scale 4 --json
# Enhance faces if needed
modl process face-restore /path/to/upscaled.png --json
# Create a mask from bounding box
modl process segment photo.jpg --bbox 100,50,300,250 --json
# Inpaint the masked region
modl generate "a beautiful garden" --init-image photo.jpg --mask photo_mask.png --base flux-fill-dev --json
# 1. Prepare dataset (resize + caption)
modl dataset prepare my-character --from ./photos/
# 2. Validate
modl dataset validate my-character
# 3. Train
modl train --base flux-dev --lora-type character --dataset my-character \
--name my-face --trigger "ohwx" --preset standard
# 4. Monitor
modl train status my-face --watch
# 5. Use the trained LoRA
modl generate "ohwx person as an astronaut" --lora my-face --base flux-dev --json
# 1. Remove background from subject
modl process remove-bg subject.jpg --json
# 2. Compose subject onto a background with precise placement
modl process compose --background forest.png --layer subject_nobg.png \
--position 0.5,0.7 --scale 0.3 --json
# 3. Edit for photorealistic integration (blends lighting, shadows, edges)
modl edit "photorealistic integration, unified lighting, preserve subject" \
--image composite.png --base klein-9b --json
# Build a scene with multiple subjects on a transparent canvas
modl process compose --background transparent --canvas-size 1024x1024 \
--layer cup.png --position 0.3,0.5 --scale 0.35 \
--layer book.png --position 0.7,0.55 --scale 0.4 --json
# 1. Write the spec (see modl run YAML format), validate it
modl run batch.yaml --dry-run
# 2. Execute — returns a run_id; the run is tracked in SQLite
modl run batch.yaml
# 3. Check later (survives disconnects; resume partial runs with --skip-existing)
modl status <run-id> --json
# 4. Collect artifacts
modl outputs export <run-id> --dest ./results
# Score a batch of images
modl vision score ./outputs/*.png --json
# Get detailed captions
modl vision describe photo.jpg --detail verbose --json
# Find specific objects
modl vision ground "the red car on the left" photo.jpg --json
flux-schnell (4 steps)flux-dev (28 steps)qwen-image (25 steps)chroma (20 steps)sdxl (5 GB) or sd-1.5 (3 GB)modl edit with klein-9b (fast) or qwen-image-edit-2511 (best)modl generate --mask with flux-fill-dev--lora-type character --trigger "ohwx" --class-word "man" (or woman/person)--lora-type style --trigger "in the style of sks"--lora-type object --trigger "sks object" --class-word "object"--preset quick (500 steps)--preset standard (1500 steps)flux-dev (most ecosystem support)modl generate fails with "model not found", run modl pull <model> first--base with a smaller model or use --fast modemodl train status --json for error detailsmodl doctor to diagnose installation issuesmodl worker status to check if GPU worker is running and what's loaded