wan-flf-video

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"]

WAN 2.2 First-Last-Frame (FLF) Video Workflows

Overview

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.

CRITICAL: Dual Hi-Lo Architecture (REQUIRED)

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.

• HighNoise model (pass 1, steps 0→N/2): Establishes structure, motion, and composition
• LowNoise model (pass 2, steps N/2→N): Refines details and ensures fidelity to input frames
• Both passes share the same conditioning from WanFirstLastFrameToVideo
• Pass 1 returns noisy latent → Pass 2 continues from there

NEVER use a single KSampler with only one model for WAN 2.2 I2V.

Two native approaches are available:

1. Native Dual Hi-Lo (Default) — WanFirstLastFrameToVideo + dual KSamplerAdvanced two-pass
2. WanVideoWrapper — WanVideoVACEStartToEndFrame + WanVideoVACEEncode + WanVideoSampler (VACE, caching, context windows)

Models

UNET Pairs (Always load BOTH Hi and Lo)

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

Text Encoder

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

CLIP Vision + VAE

Component	Node	Model
CLIP Vision	CLIPVisionLoader	clip_vision_h.safetensors
VAE	VAELoader	wan_2.1_vae.safetensors

ModelSamplingSD3 (REQUIRED)

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.

Lightning LoRAs

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 UNET
• wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors → LowNoise UNET

LoRA Stacks (rgthree)

Each 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.

Image Resizing (ImageResizeKJv2)

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
}}

KSamplerAdvanced Two-Pass Settings

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.

Negative Prompt (REQUIRED)

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

Node: WanFirstLastFrameToVideo

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)

Pipeline Flow

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)

Complete Workflow: Native FLF (Remix NSFW + Lightning)

{
  "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
  }}
}

Optional: Video Upscaling with SeedVR2

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
  }}
}

Alternative: GGUF Models with Lightning LoRAs

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.safetensors
• Unknown\no tags\wan2.2_i2v_lightx2v_4steps_lora_v1_low_noise.safetensors

Approach 2: WanVideoWrapper (Advanced Control)

Uses the WanVideoWrapper custom node pack for more control over conditioning, caching, context windows, and advanced features.

Key Differences from Native

• Uses WANVIDEOMODEL type instead of generic MODEL
• Uses WANVIDIMAGE_EMBEDS for conditioning instead of CONDITIONING
• Has own sampler (WanVideoSampler) with shift parameter and scheduler options
• Supports TeaCache, MagCache, EasyCache for speed optimization
• Supports context windows for longer videos
• VACE module provides more flexible frame conditioning

VACE-Based FLF Pipeline

WanVideoModelLoader → 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

WanVideoSampler Settings

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

When to Use WanVideoWrapper vs Native

Feature	Native	WanVideoWrapper
Simplicity	Simpler	More complex
Dual Hi-Lo	Manual two-pass	May handle internally
LoRA loading	Lora Loader Stack (rgthree)	WanVideoLoraSelect / WanVideoSetLoRAs
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.

Resolution & Frame Count

Standard Resolutions

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.

Frame Count

• 81 frames at 16fps = ~5 seconds (default, recommended)
• 49 frames at 16fps = ~3 seconds (faster, less motion)
• 121 frames at 16fps = ~7.5 seconds (longer, more VRAM)
• Frame count should be 4n + 1 (1, 5, 9, ..., 49, 81, 121)

Frame Rate

Standard: 16 fps for WAN 2.2 output.

Video Output

VHS_VideoCombine

{
  "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
  }
}

VRAM Considerations

Dual Hi-Lo with Remix fp16

• Two UNETs loaded sequentially (ComfyUI offloads between passes): ~14GB each
• NSFW UMT5-XXL bf16: ~8GB (offloaded after text encoding)
• CLIP Vision H: ~1.5GB (offloaded after encoding)
• VAE: ~200MB
• Latent (81 frames at 480x720): ~1-2GB

ComfyUI manages VRAM by offloading models between passes. The Hi UNET is offloaded before the Lo UNET loads.

Tips

1. Always clear_vram before switching to WAN from another model family
2. Use VRAM_Debug node between generation and SeedVR2 upscaling to free all VRAM
3. For 24GB GPUs, 81 frames at 480x720 is the practical maximum
4. Remix NSFW models have lightning baked in — no separate LoRA needed, 4 steps total

Morph LoRAs (Smooth Metamorphosis)

By 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.

Magical Morph (Recommended)

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

• Source: NikolaSigmoid/wan2.2-i2v-loras-magical-morph
• No trigger word needed — the LoRA modifies the denoising behavior
• Strength 1.0 can add visual sparkle/particle effects. Reduce to 0.7-0.8 for cleaner morphs
• Works with Remix NSFW models (no conflict with built-in lightning)

SkinMorph Redmond (Alternative — Face/Body Focus)

For person-to-person morphs (identity, gender transforms):

• Trigger word: Skin morph
• Strength: 0.8-1.0
• Source: CivitAI

Prompt Tips

Describe 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:

• AVOID words like "magical", "enchanted", "mystical" — they cause literal sparkle/particle effects
• USE clean motion language: "smoothly transforms", "gradually reshapes", "seamlessly morphs", "transitions into"
• The morph LoRA handles the morphing effect — the prompt should describe motion and form change, not style
• Include scale/position cues when subjects differ in size: "grows into", "expands upward", "shrinks down"

Settings Quick Reference

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)

Multi-Step Pipeline Pattern

Anchor Frame Strategy (Proportions)

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:

• Consistent background/scene between frames
• Correct relative proportions (the edit inherits the scene scale)
• Better FLF results since both frames share the same visual context

Example — Cat-to-Girl Morph:

1. Generate girl standing in front of barn with Z-Image (she fills the frame)
2. Qwen Edit: "Replace the woman with a small cat sitting at the bottom of the image"
3. FLF: cat (start) → girl (end) — proportions are correct because the barn establishes scale

Anti-pattern: Generating cat and girl independently produces mismatched scale.

Full Pipeline

1. Generate anchor frame with Z-Image/SDXL/Flux (portrait orientation for standing subjects)
2. Qwen Edit to create second frame — the edit preserves scene context
3. Clear VRAM between model families
4. Upload both frames with upload_image
5. Run dual hi-lo FLF with morph LoRA if morphing is desired
6. Optionally upscale with SeedVR2 to 1080p

Proven timing on RTX 4090: Z-Image (35s) → Qwen Edit (78s) → WAN FLF 81 frames (139s) = ~4 minutes total.

Working with Saved Workflows

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.