Face Swap Workflow
A ComfyUI workflow for de-aging and/or face replacements that combines AI-driven masking, reference-based identity transfer, and inpainting to produce natural, temporally consistent results. Designed for VFX use, it allows artists to quickly swap or rejuvenate faces while preserving performance, lighting, and integration with the original plate.
ワークフローをダウンロードCloudで試す API ドキュメント
概要
The Face Swap Workflow in ComfyUI is a sophisticated tool designed for visual effects artists who need to perform de-aging or face replacements in video footage. This workflow leverages a combination of AI-driven masking, identity transfer, and inpainting techniques to deliver natural and consistent results across frames. Key nodes such as WanVideoVAELoader and WanVideoModelLoader are used to load and manage video data, while nodes like DrawMaskOnImage and GrowMask help in creating precise masks for face swapping.
The workflow also incorporates advanced detection models through OnnxDetectionModelLoader and CLIPVisionLoader to accurately identify and manipulate facial features. By integrating these components, the workflow ensures that the new face blends seamlessly with the original footage, maintaining lighting and performance nuances.
入力するもの
- ワークフローを読み込み中
ComfyUI で実行できます。 このワークフローはこのページでは実行できません。すべてが読み込まれた状態でComfy Cloudで開くか、ご自身のComfyUI用にダウンロードしてください。
Cloudで試す例
例を試す
ワークフローの内部
ノードグラフの読み取り専用プレビューです。ノードを編集するには、Comfy Cloudで開くかダウンロードしてください。
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Open full-size workflow preview- 作成者
- ComfyUI
カスタムノードが必要です
- ワークフローを読み込み中
コードからこのワークフローを使用する
可能な場合はワークフローグラフと入力から生成されます。それ以外の場合は保存された例が表示されます。料金が発生する場合があるため、コードを実行する前に認証情報とエンドポイントを確認してください。
1 API キーを取得 ↗
What you need
- Runs on
- Comfy Cloud
- キー
- COMFY_API_KEY
有料の Cloud プランとクレジットが必要です。
API JSONをダウンロードAPI ドキュメント ↗
# Python 3.10+: pip install comfy-sdk==0.4.0
import os
from comfy_sdk import Comfy
api_key = os.environ.get("COMFY_API_KEY", "").strip()
if not api_key: raise RuntimeError("Set COMFY_API_KEY")
os.environ.setdefault("COMFY_BASE_URL", "https://cloud.comfy.org")
client = Comfy(api_key=api_key)
workflow = client.workflows.from_json({
"38": {
"class_type": "WanVideoVAELoader",
"inputs": {
"model_name": "Wan2_1_VAE_fp32.safetensors",
"precision": "fp32",
"use_cpu_cache": False,
"verbose": False
},
"_meta": {
"title": "WanVideo VAE Loader"
}
},
"195": {
"class_type": "WanVideoTorchCompileSettings",
"inputs": {
"allow_unmerged_lora_compile": False,
"backend": "inductor",
"compile_transformer_blocks_only": True,
"dynamic": False,
"dynamo_cache_size_limit": 64,
"dynamo_recompile_limit": 128,
"force_parameter_static_shapes": False,
"fullgraph": False,
"mode": "default"
},
"_meta": {
"title": "WanVideo Torch Compile Settings"
}
},
"891": {
"class_type": "WanVideoLoraSelectMulti",
"inputs": {
"lora_0": "WanAnimate_relight_lora_fp16.safetensors",
"lora_1": "lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16.safetensors",
"lora_2": "none",
"lora_3": "none",
"lora_4": "none",
"low_mem_load": False,
"merge_loras": False,
"strength_0": 1,
"strength_1": 1,
"strength_2": 1,
"strength_3": 1,
"strength_4": 1
},
"_meta": {
"title": "WanVideo Lora Select Multi"
}
},
"899": {
"class_type": "LoadImage",
"inputs": {
"image": "27e8b5aa2f2631d728cbaca117fe57ae2297a085a2d356d1a3f1438a7e8dadea.png"
},
"_meta": {
"title": "Load Image"
}
},
"900": {
"class_type": "ImageResizeKJv2",
"inputs": {
"crop_position": "top",
"device": "cpu",
"divisible_by": 16,
"height": [
"1099",
4
],
"image": [
"899",
0
],
"keep_proportion": "crop",
"pad_color": "0, 0, 0",
"upscale_method": "lanczos",
"width": [
"1099",
3
]
},
"_meta": {
"title": "Resize Image v2"
}
},
"911": {
"class_type": "VHS_VideoCombine",
"inputs": {
"crf": 19,
"filename_prefix": "WanVideo2_1_T2V",
"format": "video/h264-mp4",
"frame_rate": [
"1099",
0
],
"images": [
"912",
0
],
"loop_count": 0,
"pingpong": False,
"pix_fmt": "yuv420p",
"save_metadata": True,
"save_output": False,
"trim_to_audio": False
},
"_meta": {
"title": "Video Combine 🎥🅥🅗🅢"
}
},
"912": {
"class_type": "DrawMaskOnImage",
"inputs": {
"color": "0,0,0",
"device": "cpu",
"image": [
"1098",
0
],
"mask": [
"914",
0
]
},
"_meta": {
"title": "Draw Mask On Image"
}
},
"913": {
"class_type": "GrowMask",
"inputs": {
"expand": 300,
"mask": [
"1293",
0
],
"tapered_corners": False
},
"_meta": {
"title": "Grow Mask"
}
},
"914": {
"class_type": "BlockifyMask",
"inputs": {
"block_size": 8,
"device": "cpu",
"masks": [
"913",
0
]
},
"_meta": {
"title": "Blockify Mask"
}
},
"926": {
"class_type": "VHS_VideoCombine",
"inputs": {
"crf": 19,
"filename_prefix": "WanVideo2_1_T2V",
"format": "video/h264-mp4",
"frame_rate": [
"1099",
0
],
"images": [
"1036",
1
],
"loop_count": 0,
"pingpong": False,
"pix_fmt": "yuv420p",
"save_metadata": True,
"save_output": False,
"trim_to_audio": False
},
"_meta": {
"title": "Video Combine 🎥🅥🅗🅢"
}
},
"935": {
"class_type": "CLIPVisionLoader",
"inputs": {
"clip_name": "clip_vision_h.safetensors"
},
"_meta": {
"title": "Load CLIP Vision"
}
},
"936": {
"class_type": "WanVideoClipVisionEncode",
"inputs": {
"clip_vision": [
"935",
0
],
"combine_embeds": "average",
"crop": "center",
"force_offload": True,
"image_1": [
"900",
0
],
"ratio": 0.5,
"strength_1": 1,
"strength_2": 1,
"tiles": 0
},
"_meta": {
"title": "WanVideo ClipVision Encode"
}
},
"951": {
"class_type": "WanVideoAnimateEmbeds",
"inputs": {
"bg_images": [
"912",
0
],
"clip_embeds": [
"936",
0
],
"colormatch": "hm-mvgd-hm",
"face_images": [
"1036",
1
],
"face_strength": 1,
"force_offload": False,
"frame_window_size": [
"1099",
1
],
"height": [
"1099",
4
],
"mask": [
"914",
0
],
"num_frames": [
"1099",
1
],
"pose_images": [
"1035",
0
],
"pose_strength": 1,
"ref_images": [
"900",
0
],
"tiled_vae": False,
"vae": [
"38",
0
],
"width": [
"1099",
3
]
},
"_meta": {
"title": "WanVideo Animate Embeds"
}
},
"953": {
"class_type": "WanVideoSampler",
"inputs": {
"add_noise_to_samples": False,
"batched_cfg": False,
"cfg": 1,
"denoise_strength": 1,
"end_step": -1,
"force_offload": True,
"image_embeds": [
"951",
0
],
"model": [
"979",
0
],
"riflex_freq_index": 0,
"rope_function": "comfy",
"scheduler": "dpm++_sde/beta",
"seed": 65346457346,
"shift": 6,
"start_step": 0,
"steps": 8,
"text_embeds": [
"982",
0
]
},
"_meta": {
"title": "WanVideo Sampler"
}
},
"954": {
"class_type": "WanVideoDecode",
"inputs": {
"enable_vae_tiling": False,
"normalization": "default",
"samples": [
"953",
0
],
"tile_stride_x": 144,
"tile_stride_y": 128,
"tile_x": 272,
"tile_y": 272,
"vae": [
"38",
0
]
},
"_meta": {
"title": "WanVideo Decode"
}
},
"979": {
"class_type": "WanVideoModelLoader",
"inputs": {
"attention_mode": "sdpa",
"base_precision": "fp16_fast",
"compile_args": [
"195",
0
],
"load_device": "offload_device",
"lora": [
"891",
0
],
"model": "Wan2_2-Animate-14B_fp8_scaled_e4m3fn_KJ_v2.safetensors",
"quantization": "disabled",
"rms_norm_function": "default"
},
"_meta": {
"title": "WanVideo Model Loader"
}
},
"982": {
"class_type": "WanVideoTextEncodeCached",
"inputs": {
"device": "gpu",
"model_name": "umt5-xxl-enc-bf16.safetensors",
"negative_prompt": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
"positive_prompt": "A young man with short, dark brown hair sits in a dimly lit room, his gaze steady on the camera. He is wearing an olive green button-up shirt that appears slightly worn but neat. His skin has a natural tan tone, and he looks calm yet thoughtful. He has no wrinkles and looks very youthful. Not old.",
"precision": "bf16",
"quantization": "disabled",
"use_disk_cache": False
},
"_meta": {
"title": "WanVideo TextEncode Cached"
}
},
"1034": {
"class_type": "OnnxDetectionModelLoader",
"inputs": {
"onnx_device": "CUDAExecutionProvider",
"vitpose_model": "vitpose-l-wholebody.onnx",
"yolo_model": "yolov10m.onnx"
},
"_meta": {
"title": "ONNX Detection Model Loader"
}
},
"1035": {
"class_type": "DrawViTPose",
"inputs": {
"body_stick_width": -1,
"draw_head": True,
"hand_stick_width": -1,
"height": [
"1099",
4
],
"pose_data": [
"1036",
0
],
"retarget_padding": 16,
"width": [
"1099",
3
]
},
"_meta": {
"title": "Draw ViT Pose"
}
},
"1036": {
"class_type": "PoseAndFaceDetection",
"inputs": {
"face_padding": 0,
"height": [
"1099",
4
],
"images": [
"1098",
0
],
"model": [
"1034",
0
],
"width": [
"1099",
3
]
},
"_meta": {
"title": "Pose and Face Detection"
}
},
"1037": {
"class_type": "VHS_VideoCombine",
"inputs": {
"crf": 19,
"filename_prefix": "WanVideo2_1_T2V",
"format": "video/h264-mp4",
"frame_rate": [
"1099",
0
],
"images": [
"1035",
0
],
"loop_count": 0,
"pingpong": False,
"pix_fmt": "yuv420p",
"save_metadata": True,
"save_output": False,
"trim_to_audio": False
},
"_meta": {
"title": "Video Combine 🎥🅥🅗🅢"
}
},
"1098": {
"class_type": "VHS_LoadVideoFFmpeg",
"inputs": {
"custom_height": [
"1288",
0
],
"custom_width": [
"1287",
0
],
"force_rate": 0,
"format": "Wan",
"frame_load_cap": 0,
"start_time": 0,
"video": "dacaa9337be45cb55ba8e804af52574ff18a96c7e20e40757558d116b54fe1e6.mp4"
},
"_meta": {
"title": "Load Video FFmpeg (Upload) 🎥🅥🅗🅢"
}
},
"1099": {
"class_type": "VHS_VideoInfoLoaded",
"inputs": {
"video_info": [
"1098",
3
]
},
"_meta": {
"title": "Video Info (Loaded) 🎥🅥🅗🅢"
}
},
"1201": {
"class_type": "AILab_QwenVL",
"inputs": {
"attention_mode": "sdpa",
"custom_prompt": "describe this image as if its a 5 second long video. Include a detail description of the person and their features and hair and age. ",
"image": [
"900",
0
],
"keep_model_loaded": False,
"max_tokens": 512,
"model_name": "Qwen3-VL-2B-Instruct",
"preset_prompt": "🖼️ Detailed Description",
"quantization": "None (FP16)",
"seed": 2074003516
},
"_meta": {
"title": "QwenVL"
}
},
"1287": {
"class_type": "INTConstant",
"inputs": {
"value": 1920
},
"_meta": {
"title": "Width"
}
},
"1288": {
"class_type": "INTConstant",
"inputs": {
"value": 1088
},
"_meta": {
"title": "Height"
}
},
"1289": {
"class_type": "PreviewAny",
"inputs": {
"source": [
"1201",
0
]
},
"_meta": {
"title": "Preview as Text"
}
},
"1290": {
"class_type": "DownloadAndLoadFlorence2Model",
"inputs": {
"attention": "sdpa",
"convert_to_safetensors": False,
"model": "microsoft/Florence-2-large",
"precision": "fp16"
},
"_meta": {
"title": "DownloadAndLoadFlorence2Model"
}
},
"1291": {
"class_type": "Florence2Run",
"inputs": {
"do_sample": True,
"fill_mask": True,
"florence2_model": [
"1290",
0
],
"image": [
"1098",
0
],
"keep_model_loaded": False,
"max_new_tokens": 1024,
"num_beams": 3,
"output_mask_select": "",
"seed": 3228786869,
"task": "caption_to_phrase_grounding",
"text_input": "Face, Head"
},
"_meta": {
"title": "Florence2Run"
}
},
"1292": {
"class_type": "Florence2toCoordinates",
"inputs": {
"batch": True,
"data": [
"1291",
3
],
"index": "0"
},
"_meta": {
"title": "Florence2 Coordinates"
}
},
"1293": {
"class_type": "Sam2Segmentation",
"inputs": {
"bboxes": [
"1292",
1
],
"image": [
"1295",
0
],
"individual_objects": True,
"keep_model_loaded": True,
"sam2_model": [
"1294",
0
]
},
"_meta": {
"title": "Sam2Segmentation"
}
},
"1294": {
"class_type": "DownloadAndLoadSAM2Model",
"inputs": {
"device": "cuda",
"model": "sam2_hiera_base_plus.safetensors",
"precision": "bf16",
"segmentor": "single_image"
},
"_meta": {
"title": "(Down)Load SAM2Model"
}
},
"1295": {
"class_type": "GetImageSizeAndCount",
"inputs": {
"image": [
"1098",
0
]
},
"_meta": {
"title": "Get Image Size & Count"
}
},
"1298": {
"class_type": "VHS_VideoCombine",
"inputs": {
"audio": [
"1098",
2
],
"crf": 22,
"filename_prefix": "FaceSwap",
"format": "video/h265-mp4",
"frame_rate": [
"1099",
0
],
"images": [
"954",
0
],
"loop_count": 0,
"pingpong": False,
"pix_fmt": "yuv420p10le",
"save_metadata": True,
"save_output": True
},
"_meta": {
"title": "Video Combine 🎥🅥🅗🅢"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)よくある質問
The workflow supports common video formats such as MP4 and AVI. Ensure your footage is compatible with the WanVideoVAELoader node.
Use high-quality reference images and adjust the mask settings carefully. The GrowMask and BlockifyMask nodes can help refine the mask edges for a seamless blend.
Yes, the workflow can process multiple faces. Use the OnnxDetectionModelLoader to detect and manage each face individually.
The Face Swap Workflow is optimized for post-production rather than real-time applications due to its computational requirements.