LTX-2 Depth to Video
Generate high-quality videos from depth maps with synchronized audio-video generation using LTX-2. Features precise depth control, spatial awareness, and efficient performance.
下载工作流在 Cloud 中试用 API 文档
简介
The LTX-2 Depth to Video workflow in ComfyUI is designed to transform depth maps into high-quality videos, incorporating synchronized audio-video generation. This workflow leverages the LTX-2 model, renowned for its precise depth control and spatial awareness, to create visually compelling video content.
The workflow begins with nodes like 'LoadImage' and 'LoadVideo' to import the necessary media files. The 'GetVideoComponents' node is crucial as it extracts essential components from the video, which are then processed through nodes like 'ImageScaleBy' and 'ResizeImageMaskNode' to ensure the correct dimensions and aspect ratios. The 'SaveVideo' node finalizes the process by compiling the processed frames into a video file.
This workflow is particularly useful for creators looking to integrate depth-based visual effects into their video projects, offering efficient performance and high-quality output.
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- ComfyUI
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尽可能根据工作流图和输入自动生成代码，否则显示已保存的示例。运行代码可能产生费用，请先核对凭据和端点。
1 获取 API 密钥 ↗
What you need
- Runs on
- Comfy Cloud
- 密钥
- COMFY_API_KEY
需要付费的 Cloud 方案和额度。
下载 API JSONAPI 文档 ↗
# 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({
"144": {
"class_type": "GetVideoComponents",
"inputs": {
"video": [
"183",
0
]
},
"_meta": {
"title": "Get Video Components"
}
},
"146": {
"class_type": "ImageScaleBy",
"inputs": {
"image": [
"164",
0
],
"scale_by": 0.5,
"upscale_method": "lanczos"
},
"_meta": {
"title": "Upscale Image By"
}
},
"152": {
"class_type": "ImageFromBatch",
"inputs": {
"batch_index": 0,
"image": [
"144",
0
],
"length": [
"168",
0
]
},
"_meta": {
"title": "Get Image from Batch"
}
},
"160": {
"class_type": "LoadImage",
"inputs": {
"image": "minimalist_sky_view_lounge.png"
},
"_meta": {
"title": "Load Image"
}
},
"164": {
"class_type": "ResizeImageMaskNode",
"inputs": {
"input": [
"152",
0
],
"resize_type": "scale dimensions",
"resize_type.crop": "center",
"resize_type.height": [
"167",
0
],
"resize_type.width": [
"166",
0
],
"scale_method": "lanczos"
},
"_meta": {
"title": "Resize Image/Mask"
}
},
"165": {
"class_type": "ResizeImageMaskNode",
"inputs": {
"input": [
"160",
0
],
"resize_type": "scale dimensions",
"resize_type.crop": "center",
"resize_type.height": [
"167",
0
],
"resize_type.width": [
"166",
0
],
"scale_method": "lanczos"
},
"_meta": {
"title": "Resize Image/Mask"
}
},
"166": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 1280
},
"_meta": {
"title": "Width"
}
},
"167": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 720
},
"_meta": {
"title": "height"
}
},
"168": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 121
},
"_meta": {
"title": "Frame Count"
}
},
"169": {
"class_type": "PrimitiveBoolean",
"inputs": {
"value": False
},
"_meta": {
"title": "Ignore First Frame?"
}
},
"175": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/LTX_2.0",
"format": "auto",
"format.codec": "auto",
"video": [
"143:106",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"183": {
"class_type": "LoadVideo",
"inputs": {
"file": "minimalist_sky_view_lounge.mp4"
},
"_meta": {
"title": "Load Video"
}
},
"143:100": {
"class_type": "LatentUpscaleModelLoader",
"inputs": {
"model_name": "ltx-2-spatial-upscaler-x2-1.0.safetensors"
},
"_meta": {
"title": "Load Latent Upscale Model"
}
},
"143:101": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"143:122",
1
],
"video_latent": [
"143:151",
0
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"143:103": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "ltx-2-19b-dev-fp8.safetensors"
},
"_meta": {
"title": "Load Checkpoint"
}
},
"143:104": {
"class_type": "LTXVCropGuides",
"inputs": {
"latent": [
"143:122",
0
],
"negative": [
"143:132",
1
],
"positive": [
"143:132",
0
]
},
"_meta": {
"title": "LTXVCropGuides"
}
},
"143:105": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx-2-19b-distilled-lora-384.safetensors",
"model": [
"143:134",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"143:106": {
"class_type": "CreateVideo",
"inputs": {
"audio": [
"143:139",
0
],
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": [
"143:148",
0
],
"images": [
"143:144",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"143:107": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"143:108",
0
],
"latent_image": [
"143:101",
0
],
"noise": [
"143:143",
0
],
"sampler": [
"143:98",
0
],
"sigmas": [
"143:99",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"143:108": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"143:105",
0
],
"negative": [
"143:104",
1
],
"positive": [
"143:104",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"143:110": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"181:22",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"143:111": {
"class_type": "LTXVEmptyLatentAudio",
"inputs": {
"audio_vae": [
"143:96",
0
],
"batch_size": 1,
"frame_rate": [
"143:145",
0
],
"frames_number": [
"143:110",
2
]
},
"_meta": {
"title": "Empty LTXV Latent Audio"
}
},
"143:112": {
"class_type": "LTXVLatentUpsampler",
"inputs": {
"samples": [
"143:104",
2
],
"upscale_model": [
"143:100",
0
],
"vae": [
"143:103",
2
]
},
"_meta": {
"title": "spatial"
}
},
"143:113": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"143:138",
0
],
"vae": [
"143:103",
2
]
},
"_meta": {
"title": "VAE Decode"
}
},
"143:114": {
"class_type": "LTXVConditioning",
"inputs": {
"frame_rate": [
"143:148",
0
],
"negative": [
"143:119",
0
],
"positive": [
"143:124",
0
]
},
"_meta": {
"title": "LTXVConditioning"
}
},
"143:115": {
"class_type": "EmptyLTXVLatentVideo",
"inputs": {
"batch_size": 1,
"height": [
"143:110",
1
],
"length": [
"143:110",
2
],
"width": [
"143:110",
0
]
},
"_meta": {
"title": "Empty LTXV Latent Video"
}
},
"143:116": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"143:111",
0
],
"video_latent": [
"143:132",
2
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"143:119": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"143:97",
0
],
"text": "blurry, low quality, still frame, frames, watermark, overlay, titles, has blurbox, has subtitles"
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"143:122": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"143:123",
0
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"143:123": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"143:93",
0
],
"latent_image": [
"143:116",
0
],
"noise": [
"143:126",
0
],
"sampler": [
"143:94",
0
],
"sigmas": [
"143:95",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"143:124": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"143:97",
0
],
"text": "A wide shot reveals a whimsical outdoor scene with a decorative tree-like structure crafted from brown branches. Golden spherical ornaments and delicate white artificial flowers adorn the branches, catching bright daylight. A small grey and white squirrel stands on a round, light-colored wooden platform at the base, its bushy tail slightly raised. The squirrel dips its head down, nibbling at something on the platform. The camera slowly pushes in, focusing on the squirrel's gentle movements. Soft forest sounds fill the air—rustling leaves, distant birdsong, and a gentle breeze. The golden ornaments shimmer in the sunlight as the squirrel continues to eat."
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"143:126": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 0
},
"_meta": {
"title": "RandomNoise"
}
},
"143:132": {
"class_type": "LTXVAddGuide",
"inputs": {
"frame_idx": 0,
"image": [
"181:22",
0
],
"latent": [
"143:149",
0
],
"negative": [
"143:114",
1
],
"positive": [
"143:114",
0
],
"strength": 1,
"vae": [
"143:103",
2
]
},
"_meta": {
"title": "LTXVAddGuide"
}
},
"143:134": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx-2-19b-ic-lora-depth-control.safetensors",
"model": [
"143:103",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"143:138": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"143:107",
1
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"143:139": {
"class_type": "LTXVAudioVAEDecode",
"inputs": {
"audio_vae": [
"143:96",
0
],
"samples": [
"143:138",
1
]
},
"_meta": {
"title": "LTXV Audio VAE Decode"
}
},
"143:143": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 0
},
"_meta": {
"title": "RandomNoise"
}
},
"143:144": {
"class_type": "VAEDecodeTiled",
"inputs": {
"overlap": 64,
"samples": [
"143:138",
0
],
"temporal_overlap": 8,
"temporal_size": 4096,
"tile_size": 512,
"vae": [
"143:103",
2
]
},
"_meta": {
"title": "VAE Decode (Tiled)"
}
},
"143:145": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 24
},
"_meta": {
"title": "Int"
}
},
"143:148": {
"class_type": "PrimitiveFloat",
"inputs": {
"value": 24
},
"_meta": {
"title": "Float"
}
},
"143:149": {
"class_type": "LTXVImgToVideoInplace",
"inputs": {
"bypass": [
"169",
0
],
"image": [
"165",
0
],
"latent": [
"143:115",
0
],
"strength": 1,
"vae": [
"143:103",
2
]
},
"_meta": {
"title": "LTXVImgToVideoInplace"
}
},
"143:151": {
"class_type": "LTXVImgToVideoInplace",
"inputs": {
"bypass": [
"169",
0
],
"image": [
"165",
0
],
"latent": [
"143:112",
0
],
"strength": 1,
"vae": [
"143:103",
2
]
},
"_meta": {
"title": "LTXVImgToVideoInplace"
}
},
"143:93": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 3,
"model": [
"143:134",
0
],
"negative": [
"143:132",
1
],
"positive": [
"143:132",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"143:94": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"143:95": {
"class_type": "LTXVScheduler",
"inputs": {
"base_shift": 0.95,
"latent": [
"143:116",
0
],
"max_shift": 2.05,
"steps": 20,
"stretch": True,
"terminal": 0.1
},
"_meta": {
"title": "LTXVScheduler"
}
},
"143:96": {
"class_type": "LTXVAudioVAELoader",
"inputs": {
"ckpt_name": "ltx-2-19b-dev-fp8.safetensors"
},
"_meta": {
"title": "Load LTXV Audio VAE"
}
},
"143:97": {
"class_type": "LTXAVTextEncoderLoader",
"inputs": {
"ckpt_name": "ltx-2-19b-dev-fp8.safetensors",
"device": "default",
"text_encoder": "gemma_3_12B_it_fp4_mixed.safetensors"
},
"_meta": {
"title": "Load LTXV Audio Text Encoder"
}
},
"143:98": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "gradient_estimation"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"143:99": {
"class_type": "ManualSigmas",
"inputs": {
"sigmas": "0.909375, 0.725, 0.421875, 0.0"
},
"_meta": {
"title": "ManualSigmas"
}
},
"181:10": {
"class_type": "UNETLoader",
"inputs": {
"unet_name": "lotus-depth-d-v1-1.safetensors",
"weight_dtype": "default"
},
"_meta": {
"title": "Load Diffusion Model"
}
},
"181:14": {
"class_type": "VAELoader",
"inputs": {
"vae_name": "vae-ft-mse-840000-ema-pruned.safetensors"
},
"_meta": {
"title": "Load VAE"
}
},
"181:16": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"181:19",
0
],
"latent_image": [
"181:23",
0
],
"noise": [
"181:18",
0
],
"sampler": [
"181:21",
0
],
"sigmas": [
"181:28",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"181:18": {
"class_type": "DisableNoise",
"inputs": {},
"_meta": {
"title": "DisableNoise"
}
},
"181:19": {
"class_type": "BasicGuider",
"inputs": {
"conditioning": [
"181:68",
0
],
"model": [
"181:10",
0
]
},
"_meta": {
"title": "Basic Guider"
}
},
"181:20": {
"class_type": "BasicScheduler",
"inputs": {
"denoise": 1,
"model": [
"181:10",
0
],
"scheduler": "normal",
"steps": 1
},
"_meta": {
"title": "BasicScheduler"
}
},
"181:21": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"181:22": {
"class_type": "ImageInvert",
"inputs": {
"image": [
"181:8",
0
]
},
"_meta": {
"title": "Invert Image Colors"
}
},
"181:23": {
"class_type": "VAEEncode",
"inputs": {
"pixels": [
"146",
0
],
"vae": [
"181:14",
0
]
},
"_meta": {
"title": "VAE Encode"
}
},
"181:28": {
"class_type": "SetFirstSigma",
"inputs": {
"sigma": 999.0000000000002,
"sigmas": [
"181:20",
0
]
},
"_meta": {
"title": "SetFirstSigma"
}
},
"181:68": {
"class_type": "LotusConditioning",
"inputs": {},
"_meta": {
"title": "LotusConditioning"
}
},
"181:8": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"181:16",
0
],
"vae": [
"181:14",
0
]
},
"_meta": {
"title": "VAE Decode"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)常见问答
The workflow will automatically adjust to the closest valid parameters, ensuring smooth processing without errors.
Yes, you can try using a resolution of 1920x1088 if your system supports it, but ensure the settings are divisible by 64.
Yes, the workflow is designed to handle synchronized audio-video generation, enhancing the overall output quality.
The workflow utilizes the LTX-2 model from Lightricks, known for its depth control and spatial awareness capabilities.