LTX-2 Text to Video (Distilled)
Generate high-quality videos from text prompts with synchronized audio-video generation using LTX-2 distilled model. Optimized for faster generation while maintaining quality. Features expressive lip sync, dynamic motion generation, and improved speed.
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 LTX-2 Text to Video (Distilled) workflow is designed to transform text prompts into high-quality videos with synchronized audio and video elements. Utilizing the LTX-2 distilled model, this workflow is optimized for faster video generation while maintaining a high level of quality. It features expressive lip sync and dynamic motion generation, making it ideal for creating engaging video content.
The workflow employs nodes like SaveVideo and 9b0e709c-82c7-46c0-a2e4-5e96b6f16090, which are crucial for processing and rendering the final video output. The LTX-2 model, developed by Lightricks, is accessible via Huggingface and GitHub, providing a robust foundation for this workflow.
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
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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- 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({
"104": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/LTX-2",
"format": "auto",
"format.codec": "auto",
"video": [
"177:114",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"177:100": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "ltx-2-19b-distilled.safetensors"
},
"_meta": {
"title": "Load Checkpoint"
}
},
"177:101": {
"class_type": "LTXVAudioVAELoader",
"inputs": {
"ckpt_name": "ltx-2-19b-distilled.safetensors"
},
"_meta": {
"title": "Load LTXV Audio VAE"
}
},
"177:102": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"177:111",
0
],
"text": [
"177:109",
0
]
},
"_meta": {
"title": "enhanced prompt (positive)"
}
},
"177:103": {
"class_type": "LTXVConditioning",
"inputs": {
"frame_rate": [
"177:112",
0
],
"negative": [
"177:102",
0
],
"positive": [
"177:102",
0
]
},
"_meta": {
"title": "LTXVConditioning"
}
},
"177:106": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 24
},
"_meta": {
"title": "Frame Rate (Int)"
}
},
"177:109": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": "A man in a black tuxedo stands motionless in a small, red-tiled bathroom, facing a mirror. The camera sits just behind his right shoulder, framing both his back and his solemn reflection. Suddenly, he opens his mouth and begins to sing opera in Italian: \"La donna è mobile, qual piuma al vento.\" Rich, resonant notes echo through the space. As his voice climbs in pitch, his brows lift, and his expression becomes more passionate, almost vulnerable. The overhead lighting casts a sharp glow on his face and tuxedo, reflecting in the glossy red tiles around him. The camera is static"
},
"_meta": {
"title": "Positive prompt"
}
},
"177:110": {
"class_type": "LatentUpscaleModelLoader",
"inputs": {
"model_name": "ltx-2-spatial-upscaler-x2-1.0.safetensors"
},
"_meta": {
"title": "Load Latent Upscale Model"
}
},
"177:111": {
"class_type": "LTXAVTextEncoderLoader",
"inputs": {
"ckpt_name": "ltx-2-19b-distilled.safetensors",
"device": "default",
"text_encoder": "gemma_3_12B_it_fp4_mixed.safetensors"
},
"_meta": {
"title": "Load LTXV Audio Text Encoder"
}
},
"177:112": {
"class_type": "PrimitiveFloat",
"inputs": {
"value": 24
},
"_meta": {
"title": "Frame Rate (Float)"
}
},
"177:113": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 121
},
"_meta": {
"title": "Frame Count"
}
},
"177:114": {
"class_type": "CreateVideo",
"inputs": {
"audio": [
"177:120",
0
],
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": [
"177:112",
0
],
"images": [
"177:119",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"177:115": {
"class_type": "LTXVEmptyLatentAudio",
"inputs": {
"audio_vae": [
"177:101",
0
],
"batch_size": 1,
"frame_rate": [
"177:106",
0
],
"frames_number": [
"177:113",
0
]
},
"_meta": {
"title": "Empty LTXV Latent Audio"
}
},
"177:116": {
"class_type": "EmptyLTXVLatentVideo",
"inputs": {
"batch_size": 1,
"height": [
"177:122",
1
],
"length": [
"177:113",
0
],
"width": [
"177:122",
0
]
},
"_meta": {
"title": "Empty LTXV Latent Video"
}
},
"177:117": {
"class_type": "ManualSigmas",
"inputs": {
"sigmas": "0.909375, 0.725, 0.421875, 0.0"
},
"_meta": {
"title": "ManualSigmas"
}
},
"177:118": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 420
},
"_meta": {
"title": "RandomNoise"
}
},
"177:119": {
"class_type": "VAEDecodeTiled",
"inputs": {
"overlap": 64,
"samples": [
"177:130",
0
],
"temporal_overlap": 8,
"temporal_size": 4096,
"tile_size": 512,
"vae": [
"177:100",
2
]
},
"_meta": {
"title": "VAE Decode (Tiled)"
}
},
"177:120": {
"class_type": "LTXVAudioVAEDecode",
"inputs": {
"audio_vae": [
"177:101",
0
],
"samples": [
"177:130",
1
]
},
"_meta": {
"title": "LTXV Audio VAE Decode"
}
},
"177:121": {
"class_type": "ImageScaleBy",
"inputs": {
"image": [
"177:131",
0
],
"scale_by": 0.5,
"upscale_method": "nearest-exact"
},
"_meta": {
"title": "Upscale Image By"
}
},
"177:122": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"177:121",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"177:123": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 45
},
"_meta": {
"title": "RandomNoise"
}
},
"177:124": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"177:100",
0
],
"negative": [
"177:103",
1
],
"positive": [
"177:103",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"177:125": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"177:127",
0
],
"latent_image": [
"177:132",
0
],
"noise": [
"177:123",
0
],
"sampler": [
"177:126",
0
],
"sigmas": [
"177:176",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"177:126": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler_ancestral"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"177:127": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"177:100",
0
],
"negative": [
"177:103",
1
],
"positive": [
"177:103",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"177:128": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"177:133",
1
],
"video_latent": [
"177:135",
0
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"177:129": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler_ancestral"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"177:130": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"177:134",
1
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"177:131": {
"class_type": "EmptyImage",
"inputs": {
"batch_size": 1,
"color": 0,
"height": 720,
"width": 1280
},
"_meta": {
"title": "Empty Image"
}
},
"177:132": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"177:115",
0
],
"video_latent": [
"177:116",
0
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"177:133": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"177:125",
1
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"177:134": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"177:124",
0
],
"latent_image": [
"177:128",
0
],
"noise": [
"177:118",
0
],
"sampler": [
"177:129",
0
],
"sigmas": [
"177:117",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"177:135": {
"class_type": "LTXVLatentUpsampler",
"inputs": {
"samples": [
"177:133",
0
],
"upscale_model": [
"177:110",
0
],
"vae": [
"177:100",
2
]
},
"_meta": {
"title": "LTXVLatentUpsampler"
}
},
"177:176": {
"class_type": "ManualSigmas",
"inputs": {
"sigmas": "1., 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0"
},
"_meta": {
"title": "ManualSigmas"
}
}
})
# 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 LTX-2 distilled model is optimized for faster video generation while maintaining high quality, making it efficient for quick content creation.
Ensure your resolution settings have dimensions divisible by 64 and frame count divisible by 8 plus 1 to maintain optimal video quality.
Yes, the workflow supports resolutions up to 1920x1088, provided your hardware can handle the processing requirements.
Focus on describing core actions and visual details to ensure the generated video accurately reflects your intended narrative.