ltx2-video

name	ltx2-video
description	Generate video from a photo (or two) using self-hosted LTX-2.3 on Modal GPU. THIS is the skill for turning a single photo into a video — prefer it over any video-to-video / image skill whenever the user has a photo and wants motion. Use this whenever the user wants to turn an image into a video, animate a photo, make a reel/clip, do keyframe interpolation between two images, restyle a video (video-to-video / retake), or generate video from a text prompt — even if they don't say the word "video", e.g. "bring this photo to life", "make this move", "animate this", "turn these two shots into a transition". Calls the user's deployed `ltx2-fast-inference` Modal app and saves an .mp4 locally. Triggers: "make a video", "animate this photo", "image to video", "i2v", "keyframe", "interpolate", "video to video", "retake", "restyle this clip", "generate a clip/reel", "follow this pose/edges/depth", "canny/pose/depth control", "match this motion".
argument-hint	[/abs/path/image.jpg] ["prompt"] [i2v|keyframe|v2v|t2v|control]
allowed-tools	Bash(uv run *) Bash(python3 *) Bash(ffmpeg *) Bash(file *) Bash(realpath *) Bash(test *) Bash(modal token *) Bash(modal app *) Read

ltx2-video — photo → video via self-hosted LTX-2.3

Turns a local image (or two, or a video) into an .mp4 by calling the user's deployed ltx2-fast-inference Modal app (LTX-2.3, 22B). Five modes:

Mode	Input	What it does
i2v (default)	1 image + prompt	animates the photo into a clip
keyframe	2 images + prompt	interpolates A → B
v2v	1 video + prompt	regenerates a time window (retake)
t2v	prompt only	text-to-video, no image
control	control render (+ optional init image) + prompt	IC-LoRA structural control — union follows a canny/depth/pose render. Canny auto-derives from a source video via ffmpeg; depth/pose need a pre-rendered control video.

The work is done by scripts/submit_video.py, which calls the deployed app's methods remotely via modal.Cls.from_name (no repo path needed).

Setup (one-time)

• pip install modal && modal token new
• The backend must be deployed: modal app list | grep ltx2-fast-inference. If absent, deploy it from the ltx2-fast-inference repo: ./deploy.sh.

Workflow

1. Resolve + validate the image. Get the absolute path and confirm it's an image:

   realpath "<user-path>"            # normalize ~, relative, drag-dropped paths
   file "<abs-path>"                 # must contain JPEG / PNG / image data
   

   If not found or not an image, report and stop.
2. Confirm before running (it costs GPU time). Use AskUserQuestion:
   • header: LTX-2.3
   • question: Generate video from <name>? Cold start ~90–200s. Warm: short/low-res ~7–9s, but full 10s 720p ~1–2 min (v2v ~8 min). A few cents either way.
   • options:
     • Quick smoke (cheap) — low-res sanity check, confirms the container is warm
     • Full quality — 97 frames @ 768×1280 (vertical reel)
     • Cancel
3. Run the script (set --timeout 300 on the Bash call — the first run cold-starts):

   # i2v (full)
   uv run --with modal python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py \
     --mode i2v --image "<abs>" --prompt "<prompt>" --frames 97 --height 1280 --width 768
   
   # quick smoke (cheap warm-check)
   uv run --with modal python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py \
     --mode i2v --image "<abs>" --prompt "<prompt>" --frames 17 --height 320 --width 512 --steps 8
   
   # keyframe (two images)
   uv run --with modal python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py \
     --mode keyframe --image "<absA>" --image "<absB>" --prompt "<prompt>"
   
   # video-to-video retake
   uv run --with modal python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py \
     --mode v2v --video "<abs.mp4>" --prompt "<prompt>" --start 2 --end 5
   
   # text-to-video
   python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py --mode t2v --prompt "<prompt>"
   
   # control (IC-LoRA union): auto-derive a CANNY edge render from a source video and follow it
   uv run --with modal python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py \
     --mode control --video "<abs.mp4>" --control-type canny --prompt "<prompt>" [--image "<init.jpg>"]
   
   # control with a PRE-RENDERED control video (depth map / openpose / canny you already have)
   uv run --with modal python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py \
     --mode control --control-video "<abs_control.mp4>" --prompt "<prompt>" [--image "<init.jpg>"]
   

   Immediately tell the user "waiting for container cold start (~90s)…" so it doesn't look hung. Output lands in ./video_out/ by default — override with --out-dir <dir>. (The flag is --out-dir <directory>, NOT --out.)
4. Report. The script prints SAVED <path> and PREVIEW <png>. Read the PREVIEW png so the user sees a still inline, then report the saved mp4 path + latency. Offer follow-ups (longer clip via --frames, keyframe, v2v restyle).

Prompting

Subject + action first, then lighting/camera, photorealistic detail; keep it tight. Frame counts must be 8k+1 (17, 49, 97, 121, 217, 241). bf16, no quantization.

Resolution presets (--format) — render native to the target platform, don't crop. Default is reel.

--format	Aspect	W×H	Use for
reel / tiktok / shorts / vertical (default)	9:16	768×1280	IG Reels, TikTok, YT Shorts
youtube / landscape / wide	16:9	1280×704	YouTube, landscape embed
square / post	1:1	1024×1024	IG/FB feed post

--width/--height override the preset (must be divisible by 32).

Image-grounded prompting (i2v) — do this for quality. Don't make the user describe their own photo. First Read the image and silently form a one-line description (subject + setting + lighting), then build the prompt as <image description> , <motion> , <camera>. Keep the description faithful so identity/scene is preserved; only the motion + camera are new. A prompt that contradicts the photo (e.g. "golden hour" on a flat-lit indoor face) fights the model. Default motion = "subtle idle" if the user gives none.

Keyframe coherence — the #1 keyframe rule. Interpolation is only coherent when A and B are the same subject/scene (same person, slightly different pose/expression/camera). Unrelated A/B → a morph/dissolve (identity melt), not a clean motion. If the user has only A, offer to make B by editing A (same subject, one change) for a coherent pair; first/last frames of one clip are also coherent by construction; A==B → a smooth loop. If A and B look unrelated, warn before running (see references/mode_ux.md §3.3-B) and offer to make B a variant of A.

Named motion presets, the per-mode interaction contracts, the decision tree, the clarifying AskUserQuestion prompts, and per-mode latency live in references/mode_ux.md — read it when choosing a mode or expanding a motion prompt.

Audio & batching

• Audio is ON by default. LTX-2.3 generates synced audio with the video. So put the sound in your prompt too — ambience, foley, a music mood (e.g. "rain patter and distant thunder", "soft lo-fi pad", "crowd murmur"). The model scores the audio from the same prompt.
• Silent clip: add --skip-audio. The video pixels are byte-identical with or without audio — skipping only drops the audio decode (slightly faster, smaller file). Use it for B-roll you'll score later, or when audio isn't wanted.
• Batching — two kinds, both in one warm container (only the first take cold-starts):
  • Multiple passes of the same prompt — --variations N runs N takes with seeds seed..seed+N-1. This is the "run a prompt 20 ways, keep the 1 good one" loop — fail-free iteration. Files: <ts>_<mode>_s<seed>.mp4. Pair with --seed to set the base / reproduce a take.
  • Multiple different prompts — --prompts-file prompts.txt (one prompt per line; i2v / t2v / keyframe). Files: <ts>_<mode>_pNN.mp4. For i2v/keyframe pass the --image(s) once — they apply to every prompt.
  • They compose: N prompts × M variations = N×M clips in one warm run. Cost scales with clip count; each clip is still a few cents. Suggest a cheap-smoke pass (--frames 17 --height 320 --width 512 --steps 8 --variations 8) to scan directions before committing to full-res takes.

  # 8 takes of one prompt to find a keeper
  uv run --with modal python3 ${CLAUDE_SKILL_DIR}/scripts/submit_video.py \
    --mode i2v --image "<abs>" --prompt "<prompt>" --variations 8
  

Guardrails

• Always confirm via AskUserQuestion before a full run (GPU cost). Offer the cheap smoke first.
• First call after idle cold-starts (~90–200s). Warm latency is resolution-dependent: short/low-res ~7–9s, but full-res 10s clips ~95–120s (v2v ~470s) — at 768×1280 only one stage transformer fits resident, so stages rebuild per call. Use --timeout 600 for full-res/v2v.
• Do NOT route through fal-mcp. For Hail Films / @patrawtf canon reels, use the hail-films-reel skill instead.

Troubleshooting

Symptom	Fix
modal not installed	pip install modal && modal token new
from_name can't find app	deploy the backend: ./deploy.sh in the ltx2-fast-inference repo
no mp4 / no video returned	check modal app logs ltx2-fast-inference
CUDA out of memory	should not happen on mode-switching anymore — the backend evicts resident transformers automatically (activation-aware cap) so each forward fits. If it ever appears, just retry once; the backend also has OOM-recovery.
looks hung	normal cold start — wait up to ~120s