LTX-2.3 Style Transition
Apply the ltx2.3-transition LoRA to create smooth style and scene transitions. This workflow uses a specialized model to blend visual elements.
简介
This ComfyUI workflow builds a guided style and scene transition on top of the LTX-2.3 text-to-video model by applying the ltx2.3-transition LoRA. The core generation happens inside a custom LTX-2.3 pipeline node (beb19732-5803-4af6-b2d2-289692ce780b), which takes your prompt, applies the LoRA at a user-defined weight, and renders a sequence of frames that smoothly evolve from one style or scene description to another. If you provide a reference image with LoadImage, the pipeline can also pull color palettes or subject cues into the transition; if you skip it, the transition is guided purely by text.
Practical controls are surfaced where you need them: prompt fields (with support for the trigger word "zhuanchang"), frame count and FPS for timing, and LoRA strength to dial how assertively the transition behaves. MarkdownNote is embedded to keep prompting tips visible as you iterate, and SaveVideo encodes the final frames into a video file at your chosen frame rate and quality. The result is a reliable, loop-friendly way to blend visual elements—style, lighting, subject emphasis, or even scene composition—without hand-animating or post-processing crossfades.
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What you need
- Runs on
- Comfy Cloud
- 密钥
- COMFY_API_KEY
需要付费的 Cloud 方案和额度。
# 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({
"68": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/ltx2.3_flf2v",
"format": "auto",
"format.codec": "auto",
"video": [
"139:122",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"137": {
"class_type": "LoadImage",
"inputs": {
"image": "ltx23_flf2v_last_frame.png"
},
"_meta": {
"title": "Load Image"
}
},
"138": {
"class_type": "LoadImage",
"inputs": {
"image": "ltx23_flf2v_first_frame.png"
},
"_meta": {
"title": "Load Image"
}
},
"139:100": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 767871932424233
},
"_meta": {
"title": "RandomNoise"
}
},
"139:101": {
"class_type": "LTXVEmptyLatentAudio",
"inputs": {
"audio_vae": [
"139:126",
0
],
"batch_size": 1,
"frame_rate": [
"139:114",
0
],
"frames_number": [
"139:144",
1
]
},
"_meta": {
"title": "Empty LTXV Latent Audio"
}
},
"139:103": {
"class_type": "LTXAVTextEncoderLoader",
"inputs": {
"ckpt_name": "ltx-2.3-22b-distilled-fp8.safetensors",
"device": "default",
"text_encoder": "gemma_3_12B_it_fp4_mixed.safetensors"
},
"_meta": {
"title": "Load LTXV Audio Text Encoder"
}
},
"139:104": {
"class_type": "LTXVPreprocess",
"inputs": {
"image": [
"139:124",
0
],
"img_compression": 25
},
"_meta": {
"title": "LTXV Preprocess"
}
},
"139:105": {
"class_type": "VAEDecodeTiled",
"inputs": {
"overlap": 64,
"samples": [
"139:106",
2
],
"temporal_overlap": 64,
"temporal_size": 4096,
"tile_size": 768,
"vae": [
"139:127",
2
]
},
"_meta": {
"title": "VAE Decode (Tiled)"
}
},
"139:106": {
"class_type": "LTXVCropGuides",
"inputs": {
"latent": [
"139:121",
0
],
"negative": [
"139:111",
1
],
"positive": [
"139:111",
0
]
},
"_meta": {
"title": "LTXVCropGuides"
}
},
"139:107": {
"class_type": "LTXVAudioVAEDecode",
"inputs": {
"audio_vae": [
"139:126",
0
],
"samples": [
"139:121",
1
]
},
"_meta": {
"title": "LTXV Audio VAE Decode"
}
},
"139:108": {
"class_type": "EmptyLTXVLatentVideo",
"inputs": {
"batch_size": 1,
"height": [
"139:110",
1
],
"length": [
"139:144",
1
],
"width": [
"139:110",
0
]
},
"_meta": {
"title": "Empty LTXV Latent Video"
}
},
"139:109": {
"class_type": "LTXVConditioning",
"inputs": {
"frame_rate": [
"139:123",
0
],
"negative": [
"139:112",
0
],
"positive": [
"139:128",
0
]
},
"_meta": {
"title": "LTXVConditioning"
}
},
"139:110": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"139:124",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"139:111": {
"class_type": "LTXVAddGuide",
"inputs": {
"frame_idx": -1,
"image": [
"139:99",
0
],
"latent": [
"139:115",
2
],
"negative": [
"139:115",
1
],
"positive": [
"139:115",
0
],
"strength": 0.7,
"vae": [
"139:127",
2
]
},
"_meta": {
"title": "LTXVAddGuide"
}
},
"139:112": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"139:103",
0
],
"text": "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, wrong hand count, artifacts around text, unreadable text on shirt or hat, incorrect lettering on cap (“PNTR”), incorrect t-shirt slogan (“JUST DO IT”), missing microphone, misplaced microphone, inconsistent perspective, camera shake, incorrect depth of field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, smiling, laughing, exaggerated sadness, wrong gaze direction, eyes looking at camera, mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, off-sync audio, missing sniff sounds, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, missing door or shelves, missing shallow depth of field, flat lighting, inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts."
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"139:113": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 720
},
"_meta": {
"title": "Width"
}
},
"139:114": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 25
},
"_meta": {
"title": "Frame Rate(int)"
}
},
"139:115": {
"class_type": "LTXVAddGuide",
"inputs": {
"frame_idx": 0,
"image": [
"139:104",
0
],
"latent": [
"139:108",
0
],
"negative": [
"139:109",
1
],
"positive": [
"139:109",
0
],
"strength": 0.7,
"vae": [
"139:127",
2
]
},
"_meta": {
"title": "LTXVAddGuide"
}
},
"139:116": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"139:129",
0
],
"negative": [
"139:111",
1
],
"positive": [
"139:111",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"139:117": {
"class_type": "SamplerEulerAncestral",
"inputs": {
"eta": 0,
"s_noise": 1
},
"_meta": {
"title": "SamplerEulerAncestral"
}
},
"139:118": {
"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"
}
},
"139:119": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"139:101",
0
],
"video_latent": [
"139:111",
2
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"139:120": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"139:116",
0
],
"latent_image": [
"139:119",
0
],
"noise": [
"139:100",
0
],
"sampler": [
"139:117",
0
],
"sigmas": [
"139:118",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"139:121": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"139:120",
1
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"139:122": {
"class_type": "CreateVideo",
"inputs": {
"audio": [
"139:107",
0
],
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": [
"139:123",
0
],
"images": [
"139:105",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"139:123": {
"class_type": "ComfyMathExpression",
"inputs": {
"expression": "a",
"values.a": [
"139:114",
0
]
},
"_meta": {
"title": "Math Expression"
}
},
"139:124": {
"class_type": "ResizeImageMaskNode",
"inputs": {
"input": [
"138",
0
],
"resize_type": "scale dimensions",
"resize_type.crop": "center",
"resize_type.height": [
"139:98",
0
],
"resize_type.width": [
"139:113",
0
],
"scale_method": "nearest-exact"
},
"_meta": {
"title": "Resize Image/Mask"
}
},
"139:125": {
"class_type": "ResizeImageMaskNode",
"inputs": {
"input": [
"137",
0
],
"resize_type": "scale dimensions",
"resize_type.crop": "center",
"resize_type.height": [
"139:98",
0
],
"resize_type.width": [
"139:113",
0
],
"scale_method": "nearest-exact"
},
"_meta": {
"title": "Resize Image/Mask"
}
},
"139:126": {
"class_type": "LTXVAudioVAELoader",
"inputs": {
"ckpt_name": "ltx-2.3-22b-distilled-fp8.safetensors"
},
"_meta": {
"title": "Load LTXV Audio VAE"
}
},
"139:127": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "ltx-2.3-22b-distilled-fp8.safetensors"
},
"_meta": {
"title": "Load Checkpoint"
}
},
"139:128": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"139:103",
0
],
"text": "The red hair wizard girl looks up as the magical flame burns in her palm. Camera dollys out. The scene turns from a professional photography to a wet pastel watercolor painting. zhuanchang"
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"139:129": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx2.3-transition.safetensors",
"model": [
"139:127",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"139:143": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 6
},
"_meta": {
"title": "Duration"
}
},
"139:144": {
"class_type": "ComfyMathExpression",
"inputs": {
"expression": "a * b + 1",
"values.a": [
"139:143",
0
],
"values.b": [
"139:114",
0
]
},
"_meta": {
"title": "Math Expression"
}
},
"139:98": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 1280
},
"_meta": {
"title": "Height"
}
},
"139:99": {
"class_type": "LTXVPreprocess",
"inputs": {
"image": [
"139:125",
0
],
"img_compression": 25
},
"_meta": {
"title": "LTXV Preprocess"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)常见问答
It activates the ltx2.3-transition behavior in the model+LoRA combo. Place it near the end of your prompt so your core subject, scene, camera, and transformation details remain dominant. This helps the base description stay clear while the transition logic is applied.
Use frame count and FPS. More frames at the same FPS produce a longer, slower transition; fewer frames produce a faster one. You can also phrase timing cues in your prompt (e.g., "first half… then…"), but duration is primarily governed by total frames and playback speed.
Use a reference if you want to anchor color palette, texture, or a specific subject look. Connect it to the pipeline’s reference input. If you aim for a purely text-driven transition, skip the reference and rely on descriptive prompts.
Lower the LoRA strength to 0.4–0.6 for subtler shifts, fix the seed for repeatability, and simplify the prompt to fewer, clearer visual goals. Avoid conflicting descriptors; keep subject and camera consistent while describing how the scene or style evolves.