菜单
Build WAN 2.2 First-Last-Frame video workflows — native dual hi-lo (required), and WanVideoWrapper VACE approaches
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Drive the LTX Director (Timeline) node — its Add Image/Text/Audio buttons are DOM-only and cannot be clicked by an agent; edit the hidden timeline_data JSON widget instead. Load when a workflow contains LTXDirector / LTXDirectorGuide / PromptRelayEncodeTimeline, or when asked to add, move, retime, or remove timeline segments (image / text / audio / motion).
Train a character/identity LoRA locally on FLUX.1-dev via the comfyui-mcp train_* tools (GPU Docker + ostris ai-toolkit). Use when the user wants to train a LoRA of a person/character from their photos on the local GPU — covers dataset prep, launch, monitoring, and using the result in ComfyUI. For WAN/Z-Image training via the ai-toolkit UI see ai-toolkit-trainer.
Core ComfyUI knowledge — workflow format, node types, pipeline patterns, and MCP tool usage
Discover Civitai models with the BUILT-IN search_civitai_models tool and install/generate them locally — find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).
Run the ComfyUI agent locally for FREE — no subscription, no API key, fully offline — using our gemma4 models fine-tuned on the comfyui-mcp tool suite via Ollama. Use when the user asks about running locally, running for free, offline use, avoiding API costs, Ollama setup, or which local model to pick.
Debug a WRONG or imperfect render (not a hard error) by inspecting inputs and intermediate steps with run-to-node — render one branch up to an output, preview-tap latents/masks/preprocessor maps, localize the first bad stage, then fix. Use when a final image/video comes out wrong — artifacts, wrong subject/pose/composition/color, blur, a ControlNet/IPAdapter/mask/LoRA not taking, a two-stage refiner or upscale degrading the result — rather than the run failing with an error (for errors/OOM/missing nodes use the troubleshooting skill).
| name | wan-flf-video |
| description | Build WAN 2.2 First-Last-Frame video workflows — native dual hi-lo (required), and WanVideoWrapper VACE approaches |
| globs | ["**/*.json"] |
First-Last-Frame (FLF) video generation takes a start image and an end image and generates a smooth video transition between them. WAN 2.2 I2V (Image-to-Video) 14B model excels at this.
WAN 2.2 I2V uses a split-noise architecture. Unlike WAN 2.1, the 2.2 model was trained with separate HighNoise and LowNoise components that handle different denoising ranges. You MUST use both models in a two-pass KSamplerAdvanced setup. Using a single model produces low-quality, broken output.
WanFirstLastFrameToVideoNEVER use a single KSampler with only one model for WAN 2.2 I2V.
Two native approaches are available:
WanFirstLastFrameToVideo + dual KSamplerAdvanced two-passWanVideoVACEStartToEndFrame + WanVideoVACEEncode + WanVideoSampler (VACE, caching, context windows)Remix NSFW (Recommended — built-in lightning, fp16):
| Model | Loader | Notes |
|---|---|---|
Wan2.2_Remix_NSFW_i2v_14b_high_lighting_fp16_v2.1.safetensors | UNETLoader | HighNoise, built-in lightning acceleration |
Wan2.2_Remix_NSFW_i2v_14b_low_lighting_fp16_v2.1.safetensors | UNETLoader | LowNoise, built-in lightning acceleration |
GGUF Q8 (Alternative — needs external lightning LoRAs):
| Model | Loader | Notes |
|---|---|---|
Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf | UnetLoaderGGUF | HighNoise, quantized |
Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf | UnetLoaderGGUF | LowNoise, quantized |
Official fp8:
| Model | Loader | Notes |
|---|---|---|
wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors | UNETLoader | HighNoise, needs lightning LoRA |
wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors | UNETLoader | LowNoise, needs lightning LoRA |
| Model | Node | Notes |
|---|---|---|
nsfw_wan_umt5-xxl_bf16_fixed.safetensors | CLIPLoaderGGUF (type=wan) | NSFW-tuned, pair with Remix models |
umt5_xxl_fp8_e4m3fn_scaled.safetensors | CLIPLoader (type=wan) | Standard UMT5-XXL fp8 |
| Component | Node | Model |
|---|---|---|
| CLIP Vision | CLIPVisionLoader | clip_vision_h.safetensors |
| VAE | VAELoader | wan_2.1_vae.safetensors |
WAN 2.2 uses flow matching and requires ModelSamplingSD3 applied to each UNET:
{"class_type": "ModelSamplingSD3", "inputs": {"model": ["<unet>", 0], "shift": 5}}
shift=5 for lightning/Remix models. shift=8 for standard (non-lightning) models.
Remix NSFW models have lightning baked in — no external LoRA needed.
For GGUF/fp8 models, use paired hi/lo lightning LoRAs:
wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensors → HighNoise UNETwan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors → LowNoise UNETEach model path has two stacked loaders (Common + Specific), each supporting 4 LoRA slots:
Hi path: UNETLoader(HN) → ModelSamplingSD3(shift=5) → Hi Common Stack → Hi Lora Stack → MODEL_HI
Lo path: UNETLoader(LN) → ModelSamplingSD3(shift=5) → Lo Common Stack → Lo Lora Stack → MODEL_LO
Common stacks hold shared LoRAs (quality/style). Specific stacks hold model-variant LoRAs. Set slots to "None" when unused. Even with no LoRAs, include the stacks — they pass CLIP through for text encoding.
Input frames MUST be resized to the target video resolution before FLF and CLIPVisionEncode. The end frame inherits width/height from the start frame's resize to ensure matching dimensions.
{"class_type": "ImageResizeKJv2", "inputs": {
"image": ["<load_image>", 0], "width": 480, "height": 720,
"upscale_method": "nearest-exact", "keep_proportion": "crop",
"pad_color": "0, 0, 0", "crop_position": "center", "divisible_by": 2
}}
| Parameter | Pass 1 (Hi) | Pass 2 (Lo) |
|---|---|---|
| model | Hi LoRA stack output | Lo LoRA stack output |
| add_noise | enable | disable |
| steps | 4 | 4 |
| cfg | 1 | 1 |
| sampler_name | uni_pc | uni_pc |
| scheduler | beta | beta |
| start_at_step | 0 | 2 |
| end_at_step | 2 | 4 |
| return_with_leftover_noise | enable | disable |
| latent_image | WanFLF output[2] | Pass 1 output[0] |
Both passes share the same positive/negative conditioning from WanFirstLastFrameToVideo outputs [0] and [1].
For standard (non-lightning) models: steps=20, split at step 10, cfg=4, sampler=euler, scheduler=simple, shift=8.
Always include a quality negative prompt:
The tones are vibrant, overexposed, static, details are unclear, subtitles, style, work, painting, image, still, overall grayish, worst quality, low quality, JPEG compression artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, distorted limbs, merged fingers, motionless image, cluttered background, three legs, many people in the background, walking backwards
Required Inputs:
- positive: CONDITIONING (from CLIPTextEncode)
- negative: CONDITIONING (from CLIPTextEncode with negative prompt)
- vae: VAE
- width: INT (from ImageResizeKJv2 end frame output[1])
- height: INT (from ImageResizeKJv2 end frame output[2])
- length: INT (default 81, step 4) — number of frames
- batch_size: INT (default 1)
Optional Inputs:
- clip_vision_start_image: CLIP_VISION_OUTPUT (from CLIPVisionEncode)
- clip_vision_end_image: CLIP_VISION_OUTPUT (from CLIPVisionEncode)
- start_image: IMAGE (resized start frame)
- end_image: IMAGE (resized end frame)
Outputs:
- [0] positive: CONDITIONING → feed to BOTH Hi and Lo KSamplerAdvanced
- [1] negative: CONDITIONING → feed to BOTH Hi and Lo KSamplerAdvanced
- [2] latent: LATENT → feed to Hi Pass only (Lo Pass gets Hi Pass output)
UNETLoader (HighNoise) → ModelSamplingSD3 (shift=5) → Hi Common Stack → Hi Lora Stack → MODEL_HI
UNETLoader (LowNoise) → ModelSamplingSD3 (shift=5) → Lo Common Stack → Lo Lora Stack → MODEL_LO
CLIPLoaderGGUF (wan) → CLIP
├─ CLIPTextEncode (positive) → CONDITIONING
└─ CLIPTextEncode (negative) → CONDITIONING
CLIPVisionLoader → CLIPVisionEncode (start) + CLIPVisionEncode (end)
VAELoader → VAE
LoadImage (start) → ImageResizeKJv2 (480x720) → resized start
LoadImage (end) → ImageResizeKJv2 (match dims) → resized end
WanFirstLastFrameToVideo (positive, negative, vae, clip_vision_start, clip_vision_end,
start_image, end_image, width/height from resize)
→ modified positive [0], modified negative [1], latent [2]
KSamplerAdvanced (Hi: MODEL_HI, steps 0→2, add_noise=enable, return_leftover=enable)
→ noisy LATENT
KSamplerAdvanced (Lo: MODEL_LO, steps 2→4, add_noise=disable, return_leftover=disable)
→ final LATENT
VAEDecode → IMAGE → VHS_VideoCombine (raw output)
→ VRAM_Debug → SeedVR2VideoUpscaler (1080p) → VHS_VideoCombine (upscaled)
{
"1": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2.2_Remix_NSFW_i2v_14b_high_lighting_fp16_v2.1.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET HighNoise" }},
"2": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2.2_Remix_NSFW_i2v_14b_low_lighting_fp16_v2.1.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET LowNoise" }},
"3": { "class_type": "CLIPLoaderGGUF", "inputs": { "clip_name": "nsfw_wan_umt5-xxl_bf16_fixed.safetensors", "type": "wan" }},
"4": { "class_type": "CLIPVisionLoader", "inputs": { "clip_name": "clip_vision_h.safetensors" }},
"5": { "class_type": "VAELoader", "inputs": { "vae_name": "wan_2.1_vae.safetensors" }},
"6": { "class_type": "LoadImage", "inputs": { "image": "<start_image.png>" }, "_meta": { "title": "Start Frame" }},
"7": { "class_type": "LoadImage", "inputs": { "image": "<end_image.png>" }, "_meta": { "title": "End Frame" }},
"8": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["1", 0], "shift": 5 }, "_meta": { "title": "Hi Shift" }},
"9": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["2", 0], "shift": 5 }, "_meta": { "title": "Lo Shift" }},
"10": { "class_type": "Lora Loader Stack (rgthree)", "inputs": {
"model": ["8", 0], "clip": ["3", 0],
"lora_01": "None", "strength_01": 1, "lora_02": "None", "strength_02": 1,
"lora_03": "None", "strength_03": 1, "lora_04": "None", "strength_04": 1
}, "_meta": { "title": "Hi Common" }},
"11": { "class_type": "Lora Loader Stack (rgthree)", "inputs": {
"model": ["10", 0], "clip": ["10", 1],
"lora_01": "None", "strength_01": 1, "lora_02": "None", "strength_02": 1,
"lora_03": "None", "strength_03": 1, "lora_04": "None", "strength_04": 1
}, "_meta": { "title": "Hi Lora" }},
"12": { "class_type": "Lora Loader Stack (rgthree)", "inputs": {
"model": ["9", 0], "clip": ["3", 0],
"lora_01": "None", "strength_01": 1, "lora_02": "None", "strength_02": 1,
"lora_03": "None", "strength_03": 1, "lora_04": "None", "strength_04": 1
}, "_meta": { "title": "Low Common" }},
"13": { "class_type": "Lora Loader Stack (rgthree)", "inputs": {
"model": ["12", 0], "clip": ["12", 1],
"lora_01": "None", "strength_01": 1, "lora_02": "None", "strength_02": 1,
"lora_03": "None", "strength_03": 1, "lora_04": "None", "strength_04": 1
}, "_meta": { "title": "Low Lora" }},
"14": { "class_type": "ImageResizeKJv2", "inputs": {
"image": ["6", 0], "width": 480, "height": 720,
"upscale_method": "nearest-exact", "keep_proportion": "crop",
"pad_color": "0, 0, 0", "crop_position": "center", "divisible_by": 2
}, "_meta": { "title": "Resize Start" }},
"15": { "class_type": "ImageResizeKJv2", "inputs": {
"image": ["7", 0], "width": ["14", 1], "height": ["14", 2],
"upscale_method": "nearest-exact", "keep_proportion": "crop",
"pad_color": "0, 0, 0", "crop_position": "center", "divisible_by": 2
}, "_meta": { "title": "Resize End" }},
"16": { "class_type": "CLIPVisionEncode", "inputs": { "clip_vision": ["4", 0], "image": ["14", 0], "crop": "center" }},
"17": { "class_type": "CLIPVisionEncode", "inputs": { "clip_vision": ["4", 0], "image": ["15", 0], "crop": "center" }},
"18": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["11", 1], "text": "<positive prompt>" }, "_meta": { "title": "Positive" }},
"19": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["11", 1], "text": "The tones are vibrant, overexposed, static, details are unclear, subtitles, style, work, painting, image, still, overall grayish, worst quality, low quality, JPEG compression artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, distorted limbs, merged fingers, motionless image, cluttered background, three legs, many people in the background, walking backwards" }, "_meta": { "title": "Negative" }},
"20": { "class_type": "WanFirstLastFrameToVideo", "inputs": {
"positive": ["18", 0], "negative": ["19", 0], "vae": ["5", 0],
"clip_vision_start_image": ["16", 0], "clip_vision_end_image": ["17", 0],
"start_image": ["14", 0], "end_image": ["15", 0],
"width": ["15", 1], "height": ["15", 2], "length": 81, "batch_size": 1
}},
"21": { "class_type": "KSamplerAdvanced", "inputs": {
"model": ["11", 0], "positive": ["20", 0], "negative": ["20", 1], "latent_image": ["20", 2],
"add_noise": "enable", "noise_seed": 0, "steps": 4, "cfg": 1,
"sampler_name": "uni_pc", "scheduler": "beta",
"start_at_step": 0, "end_at_step": 2, "return_with_leftover_noise": "enable"
}, "_meta": { "title": "Hi Pass" }},
"22": { "class_type": "KSamplerAdvanced", "inputs": {
"model": ["13", 0], "positive": ["20", 0], "negative": ["20", 1], "latent_image": ["21", 0],
"add_noise": "disable", "noise_seed": 0, "steps": 4, "cfg": 1,
"sampler_name": "uni_pc", "scheduler": "beta",
"start_at_step": 2, "end_at_step": 4, "return_with_leftover_noise": "disable"
}, "_meta": { "title": "Lo Pass" }},
"23": { "class_type": "VAEDecode", "inputs": { "samples": ["22", 0], "vae": ["5", 0] }},
"24": { "class_type": "VHS_VideoCombine", "inputs": {
"images": ["23", 0], "frame_rate": 16, "loop_count": 0,
"filename_prefix": "wan_flf", "format": "video/h264-mp4",
"pingpong": false, "save_output": true,
"pix_fmt": "yuv420p", "crf": 19, "save_metadata": true, "trim_to_audio": false
}}
}
Add after VAEDecode for AI-powered video upscaling to 1080p. Use VRAM_Debug to free VRAM between generation and upscaling:
{
"25": { "class_type": "VRAM_Debug", "inputs": {
"image_pass": ["23", 0], "empty_cache": true, "gc_collect": true, "unload_all_models": true
}},
"26": { "class_type": "SeedVR2LoadDiTModel", "inputs": {
"model": "seedvr2_ema_3b_fp8_e4m3fn.safetensors", "device": "cuda:0",
"blocks_to_swap": 0, "swap_io_components": false, "cache_model": false, "attention_mode": "sdpa"
}},
"27": { "class_type": "SeedVR2LoadVAEModel", "inputs": {
"model": "ema_vae_fp16.safetensors", "device": "cuda:0",
"encode_tiled": false, "decode_tiled": false, "cache_model": false
}},
"28": { "class_type": "SeedVR2VideoUpscaler", "inputs": {
"image": ["25", 1], "dit": ["26", 0], "vae": ["27", 0],
"seed": 0, "resolution": 1080, "max_resolution": 0,
"batch_size": 5, "uniform_batch_size": false, "color_correction": "lab"
}},
"29": { "class_type": "VHS_VideoCombine", "inputs": {
"images": ["28", 0], "frame_rate": 16, "loop_count": 0,
"filename_prefix": "wan_flf_upscaled", "format": "video/h264-mp4",
"pingpong": false, "save_output": true,
"pix_fmt": "yuv420p", "crf": 19, "save_metadata": true, "trim_to_audio": false
}}
}
When using GGUF Q8 models instead of Remix, add paired lightning LoRAs:
Hi path: UnetLoaderGGUF(HN Q8) → ModelSamplingSD3(shift=5) → LoraLoaderModelOnly(hi_noise_lightning) → Hi Common Stack → Hi Lora Stack
Lo path: UnetLoaderGGUF(LN Q8) → ModelSamplingSD3(shift=5) → LoraLoaderModelOnly(lo_noise_lightning) → Lo Common Stack → Lo Lora Stack
LoRA files:
Unknown\no tags\wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise.safetensorsUnknown\no tags\wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensorsUses the WanVideoWrapper custom node pack for more control over conditioning, caching, context windows, and advanced features.
WANVIDEOMODEL type instead of generic MODELWANVIDIMAGE_EMBEDS for conditioning instead of CONDITIONINGWanVideoSampler) with shift parameter and scheduler optionsWanVideoModelLoader → WANVIDEOMODEL
WanVideoVAELoader → WANVAE
WanVideoTextEncode → WANVIDEOTEXTEMBEDS
WanVideoClipVisionEncode (start + end images) → WANVIDIMAGE_CLIPEMBEDS
WanVideoVACEStartToEndFrame (start_image, end_image, num_frames=81)
→ images batch, masks
WanVideoVACEEncode (vae, input_frames, input_masks, width, height, num_frames)
→ WANVIDIMAGE_EMBEDS (vace_embeds)
WanVideoSampler (model, image_embeds, text_embeds, steps, cfg, shift, scheduler)
→ LATENT
WanVideoDecode (vae, samples) → IMAGE → VHS_VideoCombine → MP4
| Parameter | Standard | Lightning | Notes |
|---|---|---|---|
| steps | 30 | 4 | |
| cfg | 6.0 | 1.0 | |
| shift | 5.0 | 5.0 | Flow matching shift |
| scheduler | unipc | euler | WanVideoWrapper has own schedulers |
| force_offload | true | true | Move model to CPU after sampling |
| Feature | Native | WanVideoWrapper |
|---|---|---|
| Simplicity | Simpler | More complex |
| Dual Hi-Lo | Manual two-pass | May handle internally |
| LoRA loading | Lora Loader Stack (rgthree) | WanVideoLoraSelect → WanVideoModelLoader lora (see merge_loras caveat) |
| Caching (TeaCache) | Not available | Built-in |
| Context windows | Not available | WanVideoContextOptions |
| Block swap (VRAM) | Not available | WanVideoBlockSwap |
| VACE conditioning | Not available | Full VACE support |
| Long video (>81 frames) | Limited | InfiniteTalk / context windows |
Recommendation: Use Native dual hi-lo for standard FLF transitions. Use WanVideoWrapper when you need caching, context windows, VRAM management, or advanced conditioning.
merge_loras=false with fp8-scaled modelsWhen loading a LoRA through WanVideoLoraSelect → WanVideoModelLoader's lora
input on an fp8-quantized model (quantization=fp8_e4m3fn_scaled, e.g. the
official wan2.2_i2v_high/low_noise_14B_fp8_scaled weights), you MUST set the
WanVideoLoraSelect widget merge_loras=false.
merge_loras=true (the node default) tries to bake the LoRA deltas into the
already-quantized fp8 weights. That merge path hard-crashes ComfyUI during
LoRA loading — the process dies with no Python traceback (so
panel_get_errors / the frontend show nothing; only a process restart/OOM-style
symptom). This is the #1 cause of a "crashed on lora loading" report with the
wrapper.merge_loras=false applies the LoRA as a runtime patch during the forward
pass instead of merging — fully fp8-safe, negligible speed cost. This is the
correct setting for the lightx2v 4-step lightning LoRAs (hi + lo) on the fp8
hi/lo I2V models.WanVideoBlockSwap (e.g. 20–30 of 40
blocks → RAM) + merge_loras=false is the verified combo for fp8 14B I2V at
720p/81f on a 24GB card. (If you instead use a non-quantized bf16/fp16 model,
merge_loras=true is fine.)Separately, at 720p/81f enable enable_vae_tiling=true on WanVideoDecode —
the full-frame decode is the other common uncaught-OOM crash point.
| Aspect | Resolution | Megapixels |
|---|---|---|
| Portrait 2:3 | 480x720 | 0.35MP (recommended default) |
| Landscape 16:9 | 832x480 | 0.4MP |
| Portrait 9:16 | 480x832 | 0.4MP |
| Square | 640x640 | 0.4MP |
Width and height must be divisible by 16. Use ImageResizeKJv2 with divisible_by: 2 and keep_proportion: crop.
4n + 1 (1, 5, 9, ..., 49, 81, 121)Standard: 16 fps for WAN 2.2 output.
{
"class_type": "VHS_VideoCombine",
"inputs": {
"images": ["<vae_decode>", 0],
"frame_rate": 16,
"loop_count": 0,
"filename_prefix": "wan_flf",
"format": "video/h264-mp4",
"pingpong": false,
"save_output": true,
"pix_fmt": "yuv420p",
"crf": 19,
"save_metadata": true,
"trim_to_audio": false
}
}
ComfyUI manages VRAM by offloading models between passes. The Hi UNET is offloaded before the Lo UNET loads.
clear_vram before switching to WAN from another model familyVRAM_Debug node between generation and SeedVR2 upscaling to free all VRAMBy default, FLF produces a transition/dissolve between frames. For true morphing (one shape seamlessly reshaping into another), use a morph LoRA on both Hi and Lo paths.
| Variant | File | Strength | Notes |
|---|---|---|---|
| HighNoise | wan2.2_i2v_magical_morph_highnoise.safetensors | 0.7-1.0 | Apply to Hi Common stack |
| LowNoise | wan2.2_i2v_magical_morph_lownoise.safetensors | 0.7-1.0 | Apply to Lo Common stack |
For person-to-person morphs (identity, gender transforms):
Skin morphDescribe the transition motion, not just the start/end states:
Good: "A small cat sitting on the ground smoothly transforms and grows into a woman standing tall, seamless transformation, cinematic"
Bad: "A cat and a girl"
IMPORTANT — Prompt language affects visuals:
| Config | Lightning (Remix) | Standard |
|---|---|---|
| Models | Remix NSFW Hi+Lo fp16 | Official Hi+Lo fp8 |
| CLIP | nsfw_wan_umt5-xxl_bf16_fixed | umt5_xxl_fp8_e4m3fn_scaled |
| ModelSamplingSD3 shift | 5 | 8 |
| Total steps | 4 | 20 |
| Hi pass end_at_step | 2 | 10 |
| CFG | 1 | 4 |
| Sampler | uni_pc | euler |
| Scheduler | beta | simple |
| External LoRA needed | No (built-in) | Yes (paired hi/lo) |
When the start and end frames have different subject sizes (e.g., small cat → tall person), generate the "anchor" frame first — the one with the most complex composition — then use Qwen Edit to create the other frame from it. This ensures:
Example — Cat-to-Girl Morph:
Anti-pattern: Generating cat and girl independently produces mismatched scale.
stage_output_as_input with each output's { filename, subfolder?, type? } and feed the returned input filename into each LoadImage. (For a frame already on local disk, use upload_image.) NEVER copy the output file into, or guess, a filesystem input/ path — ComfyUI's input/output dirs may be CUSTOM (--input-directory / --output-directory), so a guessed path makes LoadImage reject the file (Invalid image file) and wastes the render. stage_output_as_input routes through the server API (/view → /upload/image), which resolves the real dirs correctly.Proven timing on RTX 4090: Z-Image (35s) → Qwen Edit (78s) → WAN FLF 81 frames (139s) = ~4 minutes total.
Use analyze_workflow to understand any saved WAN FLF workflow before modifying or executing it. It returns a structured summary with sections, node IDs, key settings, and virtual wire connections — no raw JSON needed.
analyze_workflow("Wan FirstLastFrame Advanced.json") # summary view (default)
analyze_workflow("Wan FirstLastFrame Advanced.json", view="flat") # mermaid diagram
Only use get_workflow when you need the raw JSON for enqueue_workflow or modify_workflow.