ai-toolkit-trainer
Train custom LoRAs with ostris AI-Toolkit. Covers WAN 2.2/2.1 (people, styles, video motion) and Z-Image (Turbo & Base, low-VRAM image LoRAs). Use when the…
Extend / continue a video temporally with Pusa 2.2 in ComfyUI. Temporal flowmatching (the flowmatch_pusa scheduler + WanVideoAddPusaNoise) on the WanVideoWrapper stack with WAN 2.2 T2V A14B (HIGH/LOW) models and the Pusa V1 LoRAs, conditioning on the loaded clip via
$ npx -y skills add artokun/comfyui-mcp --skill video-extend --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/video-extendContext preview
The summary Claude sees to decide when to auto-load this skill.
Extend / continue a video temporally with Pusa 2.2 in ComfyUI. Temporal flowmatching (the flowmatch_pusa scheduler + WanVideoAddPusaNoise) on the WanVideoWrapper stack with WAN 2.2 T2V A14B (HIGH/LOW) models and the Pusa V1 LoRAs, conditioning on the loaded clip via
name: video-extend description: Extend / continue a video temporally with Pusa 2.2 in ComfyUI. Temporal flowmatching (the flowmatch_pusa scheduler + WanVideoAddPusaNoise) on the WanVideoWrapper stack with WAN 2.2 T2V A14B (HIGH/LOW) models and the Pusa V1 LoRAs, conditioning on the loaded clip via WanVideoEncode so the existing motion carries into the continuation. Covers the kijai wanvideo_2_2_14B_Pusa_extension graph, model/LoRA slots + downloads, noise/length/scheduler settings, chaining multiple extensions, VRAM tiers, gotchas, and the extend→upscale handoff. globs: - "**/*.json" - "**/packs/**"
Pusa extends a video temporally. It continues and lengthens an existing clip rather than regenerating it from scratch. It does this on the ComfyUI-WanVideoWrapper stack (kijai) using the WAN 2.2 T2V A14B dual HIGH/LOW models you already have for `wan-t2v-video`, plus the small Pusa V1 LoRAs and a Pusa-specific sampling path: the `flowmatch_pusa` scheduler and the `WanVideoAddPusaNoise` node. The input clip is encoded with `WanVideoEncode` and injected as the first latents of the generation, which is what carries the existing motion and content into the continuation.
The official reference graph is kijai's `wanvideo_2_2_14B_Pusa_extension_example_01.json` (in `ComfyUI-WanVideoWrapper/example_workflows/`). This skill is built directly from that workflow plus the live node schemas.
> Relationship to `wan-t2v-video`: Pusa rides on the exact same WanVideoWrapper > stack. Same T2V A14B HIGH/LOW fp8 models, same UMT5 text encoder, same WAN > VAE, same block-swap/torch-compile machinery. The only new downloads are the > two Pusa V1 LoRAs (~1.9 GB total). Read `wan-t2v-video` first for the base > stack; this skill is the temporal-extension delta on top of it.
> Verification note: every node, model, LoRA filename and setting below was > confirmed against the live ComfyUI `/object_info` (WanVideoWrapper installed) > and against kijai's example workflow JSON + HF repo (June 2026). Where a value > is a starting recommendation rather than a hard requirement it's flagged. Don't > substitute a node you can't confirm with `install_custom_node` (`action: "list"`) / > `create_workflow (action:"node_info")`.
---
WAN is a flow-matching video model: sampling integrates a velocity field from noise to a clean latent, and every frame normally shares the same denoising timestep. Pusa's contribution (Vectorized Timestep Adaptation) is to make the timestep per-frame. The frames you already have can be held at (or near) *t = 0 (clean)* while the new frames start from *t = 1 (noise)*, and the model flow-matches the noisy tail conditioned on the clean head.
Concretely in the graph:
1. `WanVideoEncode` turns the tail of your loaded clip into a clean latent. 2. That latent is placed at the front of an otherwise-empty embed (`WanVideoEmptyEmbeds` + `WanVideoAddExtraLatent`), so the generation's first latents ARE your real footage. 3. `WanVideoAddPusaNoise` assigns small, ramping per-latent noise multipliers to those conditioning latents (so they stay mostly clean) and full noise to the new latents. This per-frame noise schedule is the "vectorized timestep." 4. `flowmatch_pusa` on `WanVideoSampler` integrates that mixed-timestep field.
Because the conditioning latents are real (not a single start image like I2V), the continuation inherits the existing motion, subject, camera and color, then keeps going. That's the difference from plain T2V (no memory of any clip) and from I2V (conditions on one still frame only).
---
VHS_LoadVideo (your clip)
│ IMAGE (all frames)
▼
ImageResizeKJv2 ◄── resize to 832×480 (divisible by 16), get W/H
│
├─► GetImageRangeFromBatch (tail N frames) ─► WanVideoEncode (vae, image)
│ │ LATENT = clean
│ ▼ conditioning latents
│ GetLatentSizeAndCount ─► count
│ │
WanVideoEmptyEmbeds (W,H, total_frames=81) ▼
│ WANVIDIMAGE_EMBEDS CreateScheduleFloatList
└────────► WanVideoAddExtraLatent ◄────────┘ (per-latent noise multipliers,
│ (encoded clip latent at front) ramp e.g. 0→0.2)
▼ WANVIDIMAGE_EMBEDS
WanVideoAddPusaNoise ◄── noise_multipliers (list), noisy_steps
│
┌──────────────┴───────────────┐
▼ (pass 1, HIGH) ▼ (pass 2, LOW)
WanVideoSampler (HIGH model WanVideoSampler (LOW model
+ Pusa HIGH LoRA + distill, + Pusa LOW LoRA + distill,
flowmatch_pusa, steps 6, cfg 1, flowmatch_pusa, steps 6, cfg 1,
shift 5, start 0 / end 3) shift 5, start 3 / end -1)
└──────────────┬───────────────┘
▼ LATENT
WanVideoDecode (WAN VAE)
│ IMAGE
▼
VHS_VideoCombine ─► MP4 (16 fps)(installed). `VHS_VideoCombine` is preferred for the encode (audio passthrough).
`CreateScheduleFloatList` are ComfyUI-KJNodes (installed alongside the wrapper). They're convenience nodes; see "Minimal wiring" if you want fewer.
`WanVideoAddPusaNoise`: *"Adds latent and timestep noise multipliers when using flowmatch_pusa."*
| Input | Type | Meaning | |---|---|---| | `embeds` | `WANVIDIMAGE_
This project is no longer maintained. ComfyUI now ships official agent and MCP tooling — Comfy Agent and Comfy MCP — built and supported by the Comfy-Org team with deeper integration than a community project can match.
Repo: artokun/comfyui-mcp
Train custom LoRAs with ostris AI-Toolkit. Covers WAN 2.2/2.1 (people, styles, video motion) and Z-Image (Turbo & Base, low-VRAM image LoRAs). Use when the…
Anime/illustration text-to-image (ANIMA 1.0, ~2B Cosmos DiT). Use for anime, manga, illustrated characters; accepts Danbooru tags + natural language;…
Train a custom anime LoRA on the ANIMA base model with Citron's local Gradio trainer (kohya sd-scripts), <6GB VRAM, character/style LoRAs; covers setup,…
Discover Civitai models with the BUILT-IN download_model action:"search_civitai" and install/generate them locally. Find a checkpoint/LoRA/embedding on…
Diagnose and fix video/image color OBJECTIVELY with the get_image (action:"analyze_color") tool (scopes/stats such as black/white points, contrast, saturation,…
Core ComfyUI knowledge covering workflow format, node types, pipeline patterns, and MCP tool usage