LTX 2.3_ID_lora_CelebVHQ
Bring voice consistency to your open source video generation pipeline with Qwen 3 TTS and LTX 2.3 with the CelebHVQ ID-LoRA.
下载工作流在 Cloud 中试用 API 文档
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- 视频
- 作者
- ComfyUI
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1 获取 API 密钥 ↗
What you need
- Runs on
- Comfy Cloud
- 密钥
- COMFY_API_KEY
- 你的文件
- 由代码根据链接上传
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({
"440": {
"class_type": "LoadImage",
"inputs": {
"image": "UPLOADED_image_FILENAME"
},
"_meta": {
"title": "Load Image"
}
},
"441": {
"class_type": "GeminiNode",
"inputs": {
"images": [
"440",
0
],
"model": "gemini-3-1-pro",
"prompt": "A greenhouse gardener talking straight into the camera, warm and a little amused, about why he starts every morning by repotting one plant before anyone else arrives. He says the quiet of the glasshouse at sunrise, the smell of wet soil and the small job done with his hands set the pace for his whole day, and he ends by inviting the viewer to try one slow, simple task tomorrow morning before checking their phone. Casual, honest, handheld testimonial feel, soft birdsong and a distant sprinkler in the background, natural pauses and a small laugh near the end.",
"seed": 1101794556747325,
"system_prompt": "Role: You are a professional prompt writer for ID-LoRA prompt generation.\n\nTask: Generate a two-part output from the user’s input.\n\nStrict Output Constraint (IMPORTANT):\n\nReturn the output as RAW PLAIN TEXT only.\nDO NOT use Markdown code blocks.\nDO NOT use any headers, labels, or bold text outside of the generated two-part output.\nUse ONLY the separator --- to divide the two parts.\nPart 1: spoken dialogue/performance text only, optimized to sound natural when spoken aloud.\nPart 2: the same concept rewritten in the exact ID-LoRA tagged format using these three sections only:\n[VISUAL]: ...\n[SPEECH]: ...\n[SOUNDS]: ...\nDo NOT output anything before Part 1 or after Part 2.\n\nGlobal Length Rule:\n\nKeep the spoken content short enough to produce about 10 seconds of speech unless the user explicitly asks otherwise.\nAim for roughly 20 to 35 spoken words in Part 1.\nCondense long inputs aggressively while preserving the core meaning, tone, and key message.\nPrioritize brevity, clarity, speakability, and a strong opening hook over completeness.\nRemove repetition, side details, filler, and nonessential context.\nIf the input is too long, compress it into a concise spoken version rather than preserving everything.\n\nPart 1 Rules:\n\nPart 1 must contain ONLY the words meant to be spoken aloud.\nDo NOT begin Part 1 with visual description, scene-setting narration, character description, or action description.\nStart immediately with the most attention-grabbing, high-impact spoken hook.\nThe hook should appear as early as possible, ideally in the first sentence or phrase.\nDo NOT describe what the character looks like, where they are, what they are wearing, what they are holding, or what is happening visually unless that information is spoken by the character as part of the dialogue itself.\nPart 1 should read like a real spoken performance, monologue, ad read, or direct address.\nUse punctuation, pauses, ellipses, and selective capitalization to improve spoken delivery when useful.\nDo NOT use SSML.\nDo NOT use non-voice tags such as music, ambience, sound effects, camera notes, or technical markup.\nPrefer one clear hook, one core message or benefit, and one short closing beat.\n\nPart 2 Rules:\n\nPart 2 must contain exactly three tagged sections in this exact order:\n[VISUAL]:\n[SPEECH]:\n[SOUNDS]:\n\n[VISUAL] Rules:\nDescribe the shot type, subject appearance, clothing, setting, lighting, framing, and visible actions.\nBe descriptive enough to guide generation clearly, but keep it compact and production-useful.\nExplicitly indicate that the person is speaking or talking to camera so the line is generated as on-screen speech rather than voice-over.\nDefault toward believable UGC-style behavior unless the user asks otherwise:\ndirect-to-camera delivery\nhandheld or phone-like framing\nselfie or testimonial feel\nnatural gestures\nauthentic facial expression\nslight body movement\ncasual presenting or showing when relevant\nAvoid cinematic, polished, theatrical, or overly staged action unless the user explicitly asks for that.\n\n[SPEECH] Rules:\nWrite the exact words the person should say.\nThis must be the literal transcript, not a summary.\nKeep it closely aligned with Part 1, ideally verbatim except for minor punctuation cleanup if needed.\nDo NOT add scene description, camera notes, or sound cues inside [SPEECH].\n\n[SOUNDS] Rules:\nDescribe both the vocal delivery and the ambient/background audio.\nInclude speaker qualities such as tone, volume, pace, energy, and mic proximity or distance.\nInclude relevant environmental sounds, room tone, music, nature sounds, or other ambience when appropriate.\nKeep the audio grounded, coherent with the visual scene, and not overly busy unless requested.\n\nConsistency Rules:\n\nPart 1 and Part 2 must describe the same idea, message, tone, and scene.\n[SPEECH] in Part 2 should match Part 1 as closely as possible.\n[VISUAL], [SPEECH], and [SOUNDS] must feel like one unified prompt, not separate concepts.\nDo not introduce unrelated ideas, props, settings, or actions that were not implied by the user’s request.\nIf the user provides exact wording, preserve it in Part 1 and [SPEECH] unless the user asks for rewriting or shortening.\n\nSafety and Quality Rules:\n\nDo not introduce sensitive, explicit, hateful, political, or unsafe content that was not already in the user input.\nEnsure the final output always contains exactly two parts separated by ---.\nEnsure Part 2 always uses the exact three ID-LoRA tags and includes all three of them once."
},
"_meta": {
"title": "Google Gemini"
}
},
"443": {
"class_type": "PreviewAny",
"inputs": {
"source": [
"441",
0
]
},
"_meta": {
"title": "Preview as Text"
}
},
"473": {
"class_type": "PreviewAny",
"inputs": {
"source": [
"476",
0
]
},
"_meta": {
"title": "Preview as Text"
}
},
"474": {
"class_type": "PreviewAny",
"inputs": {
"source": [
"475",
0
]
},
"_meta": {
"title": "Preview as Text"
}
},
"475": {
"class_type": "RegexExtract",
"inputs": {
"case_insensitive": True,
"dotall": False,
"group_index": 1,
"mode": "First Group",
"multiline": False,
"regex_pattern": "(?sm)^(.*?)^---$(.*)",
"string": [
"441",
0
]
},
"_meta": {
"title": "Extract Text"
}
},
"476": {
"class_type": "RegexExtract",
"inputs": {
"case_insensitive": True,
"dotall": False,
"group_index": 1,
"mode": "First Group",
"multiline": False,
"regex_pattern": "(?s)(?<=\\n---\\n)([\\s\\S]*)$",
"string": [
"441",
0
]
},
"_meta": {
"title": "Extract Text"
}
},
"479": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/ComfyUI",
"format": "auto",
"format.codec": "auto",
"video": [
"662:653",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"621": {
"class_type": "FB_Qwen3TTSCustomVoice",
"inputs": {
"attention": "auto",
"custom_model_path": "",
"custom_speaker_name": "",
"device": "cuda",
"instruct": [
"473",
0
],
"language": "English",
"max_new_tokens": 2048,
"model_choice": "1.7B",
"precision": "bf16",
"repetition_penalty": 1.05,
"seed": 193948051206350,
"speaker": "Aiden",
"temperature": 1,
"text": [
"474",
0
],
"top_k": 20,
"top_p": 0.8,
"unload_model_after_generate": False
},
"_meta": {
"title": "🎵 Qwen3-TTS CustomVoice"
}
},
"622": {
"class_type": "SaveAudioMP3",
"inputs": {
"audio": [
"621",
0
],
"filename_prefix": "audio/ComfyUI",
"quality": "V0"
},
"_meta": {
"title": "Save Audio (MP3) (DEPRECATED)"
}
},
"662:293": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx-2.3-22b-distilled-lora-384.safetensors",
"model": [
"662:317",
0
],
"strength_model": 0.5
},
"_meta": {
"title": "Load LoRA"
}
},
"662:313": {
"class_type": "LatentUpscaleModelLoader",
"inputs": {
"model_name": "ltx-2.3-spatial-upscaler-x2-1.1.safetensors"
},
"_meta": {
"title": "Load Latent Upscale Model"
}
},
"662:317": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "ltx-2.3-22b-dev-fp8.safetensors"
},
"_meta": {
"title": "Load Checkpoint"
}
},
"662:318": {
"class_type": "LTXAVTextEncoderLoader",
"inputs": {
"ckpt_name": "ltx-2.3-22b-dev-fp8.safetensors",
"device": "default",
"text_encoder": "gemma_3_12B_it_fp4_mixed.safetensors"
},
"_meta": {
"title": "Load LTXV Audio Text Encoder"
}
},
"662:335": {
"class_type": "LTXVAudioVAELoader",
"inputs": {
"ckpt_name": "ltx-2.3-22b-dev-fp8.safetensors"
},
"_meta": {
"title": "Load LTXV Audio VAE"
}
},
"662:346": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx-2.3-id-lora-celebvhq-3k.safetensors",
"model": [
"662:293",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"662:348": {
"class_type": "LTXVEmptyLatentAudio",
"inputs": {
"audio_vae": [
"662:335",
0
],
"batch_size": 1,
"frame_rate": [
"662:652",
1
],
"frames_number": [
"662:657",
1
]
},
"_meta": {
"title": "Empty LTXV Latent Audio"
}
},
"662:349": {
"class_type": "LTXVReferenceAudio",
"inputs": {
"audio_vae": [
"662:335",
0
],
"end_percent": 1,
"identity_guidance_scale": 3,
"model": [
"662:346",
0
],
"negative": [
"662:642",
0
],
"positive": [
"662:636",
0
],
"reference_audio": [
"621",
0
],
"start_percent": 0
},
"_meta": {
"title": "LTXV Reference Audio (ID-LoRA)"
}
},
"662:624": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 42
},
"_meta": {
"title": "RandomNoise"
}
},
"662:625": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 953776337212690
},
"_meta": {
"title": "RandomNoise"
}
},
"662:626": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"662:639",
1
],
"video_latent": [
"662:633",
0
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"662:627": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler_cfg_pp"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"662:628": {
"class_type": "ManualSigmas",
"inputs": {
"sigmas": "0.85, 0.7250, 0.4219, 0.0"
},
"_meta": {
"title": "ManualSigmas"
}
},
"662:629": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"662:293",
0
],
"negative": [
"662:631",
1
],
"positive": [
"662:631",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"662:630": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"662:643",
0
],
"latent_image": [
"662:659",
0
],
"noise": [
"662:625",
0
],
"sampler": [
"662:634",
0
],
"sigmas": [
"662:638",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"662:631": {
"class_type": "LTXVCropGuides",
"inputs": {
"latent": [
"662:639",
0
],
"negative": [
"662:637",
1
],
"positive": [
"662:637",
0
]
},
"_meta": {
"title": "LTXVCropGuides"
}
},
"662:632": {
"class_type": "LTXVLatentUpsampler",
"inputs": {
"samples": [
"662:639",
0
],
"upscale_model": [
"662:313",
0
],
"vae": [
"662:317",
2
]
},
"_meta": {
"title": "LTXVLatentUpsampler"
}
},
"662:633": {
"class_type": "LTXVImgToVideoInplace",
"inputs": {
"bypass": False,
"image": [
"662:649",
0
],
"latent": [
"662:632",
0
],
"strength": 1,
"vae": [
"662:317",
2
]
},
"_meta": {
"title": "LTXVImgToVideoInplace"
}
},
"662:634": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler_ancestral_cfg_pp"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"662:635": {
"class_type": "LTXVAudioVAEDecode",
"inputs": {
"audio_vae": [
"662:335",
0
],
"samples": [
"662:641",
1
]
},
"_meta": {
"title": "LTXV Audio VAE Decode"
}
},
"662:636": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"662:318",
0
],
"text": [
"662:660",
0
]
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"662:637": {
"class_type": "LTXVConditioning",
"inputs": {
"frame_rate": [
"662:652",
0
],
"negative": [
"662:349",
2
],
"positive": [
"662:349",
1
]
},
"_meta": {
"title": "LTXVConditioning"
}
},
"662:638": {
"class_type": "ManualSigmas",
"inputs": {
"sigmas": "1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0"
},
"_meta": {
"title": "ManualSigmas"
}
},
"662:639": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"662:630",
0
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"662:640": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"662:629",
0
],
"latent_image": [
"662:626",
0
],
"noise": [
"662:624",
0
],
"sampler": [
"662:627",
0
],
"sigmas": [
"662:628",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"662:641": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"662:640",
0
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"662:642": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"662:318",
0
],
"text": "pc game, console game, video game, cartoon, childish, ugly"
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"662:643": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"662:349",
0
],
"negative": [
"662:637",
1
],
"positive": [
"662:637",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"662:644": {
"class_type": "VAEDecodeTiled",
"inputs": {
"overlap": 64,
"samples": [
"662:641",
0
],
"temporal_overlap": 4,
"temporal_size": 4096,
"tile_size": 768,
"vae": [
"662:317",
2
]
},
"_meta": {
"title": "VAE Decode (Tiled)"
}
},
"662:645": {
"class_type": "LTXVImgToVideoInplace",
"inputs": {
"bypass": False,
"image": [
"662:649",
0
],
"latent": [
"662:656",
0
],
"strength": 0.7,
"vae": [
"662:317",
2
]
},
"_meta": {
"title": "LTXVImgToVideoInplace"
}
},
"662:646": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 1280
},
"_meta": {
"title": "Width"
}
},
"662:647": {
"class_type": "SolidMask",
"inputs": {
"height": [
"662:658",
0
],
"value": 0,
"width": [
"662:646",
0
]
},
"_meta": {
"title": "Create Solid Mask"
}
},
"662:648": {
"class_type": "ResizeImagesByLongerEdge",
"inputs": {
"images": [
"662:661",
0
],
"longer_edge": 1536
},
"_meta": {
"title": "Resize Images by Longer Edge (DEPRECATED)"
}
},
"662:649": {
"class_type": "LTXVPreprocess",
"inputs": {
"image": [
"662:648",
0
],
"img_compression": 18
},
"_meta": {
"title": "LTXV Preprocess"
}
},
"662:650": {
"class_type": "ComfyMathExpression",
"inputs": {
"expression": "a/2",
"values.a": [
"662:658",
0
]
},
"_meta": {
"title": "Math Expression"
}
},
"662:651": {
"class_type": "ComfyMathExpression",
"inputs": {
"expression": "a/2",
"values.a": [
"662:646",
0
]
},
"_meta": {
"title": "Math Expression"
}
},
"662:652": {
"class_type": "ComfyMathExpression",
"inputs": {
"expression": "a",
"values.a": [
"662:655",
0
]
},
"_meta": {
"title": "Math Expression"
}
},
"662:653": {
"class_type": "CreateVideo",
"inputs": {
"audio": [
"662:635",
0
],
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": [
"662:652",
0
],
"images": [
"662:644",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"662:654": {
"class_type": "PrimitiveFloat",
"inputs": {
"value": 9
},
"_meta": {
"title": "Duration"
}
},
"662:655": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 25
},
"_meta": {
"title": "Frame Rate"
}
},
"662:656": {
"class_type": "EmptyLTXVLatentVideo",
"inputs": {
"batch_size": 1,
"height": [
"662:650",
1
],
"length": [
"662:657",
1
],
"width": [
"662:651",
1
]
},
"_meta": {
"title": "Empty LTXV Latent Video"
}
},
"662:657": {
"class_type": "ComfyMathExpression",
"inputs": {
"expression": "a * b + 1",
"values.a": [
"662:654",
0
],
"values.b": [
"662:655",
0
]
},
"_meta": {
"title": "Math Expression"
}
},
"662:658": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 720
},
"_meta": {
"title": "Height"
}
},
"662:659": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"662:348",
0
],
"video_latent": [
"662:645",
0
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"662:660": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": [
"473",
0
]
},
"_meta": {
"title": "Prompt"
}
},
"662:661": {
"class_type": "ResizeImageMaskNode",
"inputs": {
"input": [
"440",
0
],
"resize_type": "scale dimensions",
"resize_type.crop": "center",
"resize_type.height": [
"662:658",
0
],
"resize_type.width": [
"662:646",
0
],
"scale_method": "lanczos"
},
"_meta": {
"title": "Resize Image/Mask"
}
}
})
workflow.set_input("440", "image", client.assets.from_url("https://comfy.org/catalog-media/5d0fa42a-34e1-4555-877a-6cd998ac071c/c529f8e8-bfdf-488f-8f98-436a881972a9"))
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)
for output in job.get_outputs("479"):
output.to_file(output.name)试试一个示例
关于此工作流
This ComfyUI tutorial workflow turns a single image into a short, voice-driven video with strong visual identity and perceived lip alignment. It combines Lightricks LTX 2.3 for video generation with an ID-LoRA (CelebVHQ) to keep the subject’s face and styling consistent across frames, while Qwen 3 TTS produces a matching voice track. The pipeline is organized into clear groups: Load Image, Create Prompts, Prompt Extraction, Create Voice, and Create Video.
Under the hood, GeminiNode analyzes your uploaded image (via LoadImage) and drafts two complementary texts: a speech line with expressive cues and a scene/appearance description. RegexExtract then parses these into an ID-LoRA-ready format with three tagged sections: [VISUAL], [SPEECH], and [SOUNDS]. FB_Qwen3TTSCustomVoice uses the [SPEECH] and style hints from [SOUNDS] to synthesize a voice track, saved with SaveAudioMP3. The LTX 2.3 video node (the custom node in this graph) consumes the structured prompt plus your reference image to render a talking video, with the CelebVHQ ID-LoRA improving identity retention in dynamic, in-the-wild scenarios.
PreviewAny lets you iterate quickly, and SaveVideo writes the final clip. Local users can optionally load the LTX spatial upscaler (ltx-2.3-spatial-upscaler-x2-1.1.safetensors) for cleaner details at higher resolution.
常见问答
It helps LTX 2.3 preserve the subject’s identity and facial features across frames, especially in dynamic, in-the-wild shots. It works best with a clear, frontal image and a well-structured prompt that separates [VISUAL], [SPEECH], and [SOUNDS].
The workflow aligns perceived mouth movements by making the [SPEECH] text explicit and pairing it with matching TTS audio. While not frame-perfect lip-sync, using concise lines, clear punctuation, and expression cues improves alignment. You can retime in post if you need exact phoneme matching.
Yes. Swap the LoRA in the LTX 2.3 node. CelebVHQ is tuned for in-the-wild motion; TalkVid is typically stronger for close-up, studio-like talking head shots. For custom LoRAs, keep the same structured prompt format and test different strengths.
It’s optional. If you place ltx-2.3-spatial-upscaler-x2-1.1.safetensors in latent_upscale_models, the workflow can render crisper outputs at higher resolution. If VRAM is limited, disable upscaling or render at a lower base resolution.