Squish Anything with LTX-2 I2V and LoRA
Upload any image to apply the Squish It LoRA effect. Generate a squished animation using the LTX-2 I2V model.
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
This ComfyUI workflow, titled 'Squish Anything with LTX-2 I2V and LoRA', enables users to transform static images into dynamic, squished animations. Leveraging the LTX-2 I2V model, this workflow takes an uploaded image and applies the Squish It LoRA effect to create a unique animated sequence. The workflow is designed to be intuitive, utilizing nodes such as LoadImage for importing images and SaveVideo for exporting the final animation. The b7c2d337-c38d-4c04-922b-2d638449d13e node plays a crucial role in managing the transformation process, ensuring that the animation maintains a smooth and consistent flow.
Technically, the workflow operates by first loading an image using the LoadImage node. The image is then processed through a subgraph node, which applies the Squish It LoRA effect. This effect is particularly effective at creating exaggerated, cartoon-like animations that can be both entertaining and visually striking. The LTX-2 I2V model is pivotal in this process, as it interprets the image data and generates a video sequence that reflects the squished effect. The final video is saved using the SaveVideo node, allowing for easy sharing and further editing if needed.
This workflow is particularly useful for artists and content creators looking to add a playful, animated twist to their images.
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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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({
"75": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/LTX-2",
"format": "mp4",
"format.codec": "auto",
"video": [
"92:97",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"98": {
"class_type": "LoadImage",
"inputs": {
"image": "puppy_with_blue_bandana.png"
},
"_meta": {
"title": "Load Image"
}
},
"92:1": {
"class_type": "CheckpointLoaderSimple",
"inputs": {
"ckpt_name": "ltx-2-19b-dev.safetensors"
},
"_meta": {
"title": "Load Checkpoint"
}
},
"92:102": {
"class_type": "PrimitiveFloat",
"inputs": {
"value": 24
},
"_meta": {
"title": "Frame Rate(float)"
}
},
"92:108": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx-2-19b-distilled-lora-384.safetensors",
"model": [
"92:110",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"92:109": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx2-squish.safetensors",
"model": [
"92:68",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"92:11": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 4545545
},
"_meta": {
"title": "RandomNoise"
}
},
"92:110": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx2-squish.safetensors",
"model": [
"92:1",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"92:111": {
"class_type": "ResizeImagesByLongerEdge",
"inputs": {
"images": [
"98",
0
],
"longer_edge": 1536
},
"_meta": {
"title": "Resize Images by Longer Edge (DEPRECATED)"
}
},
"92:112": {
"class_type": "LTXVPreprocess",
"inputs": {
"image": [
"92:111",
0
],
"img_compression": 33
},
"_meta": {
"title": "LTXV Preprocess"
}
},
"92:113": {
"class_type": "LTXVImgToVideoInplace",
"inputs": {
"bypass": False,
"image": [
"92:112",
0
],
"latent": [
"92:43",
0
],
"strength": 1,
"vae": [
"92:1",
2
]
},
"_meta": {
"title": "LTXVImgToVideoInplace"
}
},
"92:22": {
"class_type": "LTXVConditioning",
"inputs": {
"frame_rate": [
"92:102",
0
],
"negative": [
"92:4",
0
],
"positive": [
"92:3",
0
]
},
"_meta": {
"title": "LTXVConditioning"
}
},
"92:3": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"92:60",
0
],
"text": "squish it -- a woman's voice exclaims \"I'm gonna squish this adorable little puppy... squish squish squishy\""
},
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"92:4": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"92:60",
0
],
"text": "watermark, logo, channel logo, TV logo, broadcast overlay,\nUI elements, interface, HUD, subtitles, captions,\nsymbols, glyphs, letters, text, numbers,\non-screen graphics, corner marks, branding,\ntimestamp, camera overlay, app icon watermark, text, markup, logo, 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,, 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)"
}
},
"92:41": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"92:47",
0
],
"latent_image": [
"92:56",
0
],
"noise": [
"92:11",
0
],
"sampler": [
"92:8",
0
],
"sigmas": [
"92:9",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"92:43": {
"class_type": "EmptyLTXVLatentVideo",
"inputs": {
"batch_size": 1,
"height": [
"92:91",
1
],
"length": [
"92:62",
0
],
"width": [
"92:91",
0
]
},
"_meta": {
"title": "Empty LTXV Latent Video"
}
},
"92:47": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"92:108",
0
],
"negative": [
"92:22",
1
],
"positive": [
"92:22",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"92:48": {
"class_type": "LTXVAudioVAELoader",
"inputs": {
"ckpt_name": "ltx-2-19b-dev.safetensors"
},
"_meta": {
"title": "Load LTXV Audio VAE"
}
},
"92:51": {
"class_type": "LTXVEmptyLatentAudio",
"inputs": {
"audio_vae": [
"92:48",
0
],
"batch_size": 1,
"frame_rate": [
"92:99",
0
],
"frames_number": [
"92:62",
0
]
},
"_meta": {
"title": "Empty LTXV Latent Audio"
}
},
"92:56": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"92:51",
0
],
"video_latent": [
"92:113",
0
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"92:60": {
"class_type": "LTXAVTextEncoderLoader",
"inputs": {
"ckpt_name": "ltx-2-19b-dev.safetensors",
"device": "default",
"text_encoder": "gemma_3_12B_it.safetensors"
},
"_meta": {
"title": "Load LTXV Audio Text Encoder"
}
},
"92:62": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 281
},
"_meta": {
"title": "Length"
}
},
"92:66": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler_ancestral"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"92:67": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 0
},
"_meta": {
"title": "RandomNoise"
}
},
"92:68": {
"class_type": "LoraLoaderModelOnly",
"inputs": {
"lora_name": "ltx-2-19b-distilled-lora-384.safetensors",
"model": [
"92:1",
0
],
"strength_model": 1
},
"_meta": {
"title": "Load LoRA"
}
},
"92:70": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"92:82",
0
],
"latent_image": [
"92:83",
0
],
"noise": [
"92:67",
0
],
"sampler": [
"92:66",
0
],
"sigmas": [
"92:73",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"92:73": {
"class_type": "ManualSigmas",
"inputs": {
"sigmas": "0.909375, 0.725, 0.421875, 0.0"
},
"_meta": {
"title": "ManualSigmas"
}
},
"92:76": {
"class_type": "LatentUpscaleModelLoader",
"inputs": {
"model_name": "ltx-2-spatial-upscaler-x2-1.0.safetensors"
},
"_meta": {
"title": "Load Latent Upscale Model"
}
},
"92:8": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler_ancestral"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"92:80": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"92:41",
0
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"92:81": {
"class_type": "LTXVCropGuides",
"inputs": {
"latent": [
"92:80",
0
],
"negative": [
"92:22",
1
],
"positive": [
"92:22",
0
]
},
"_meta": {
"title": "LTXVCropGuides"
}
},
"92:82": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 1,
"model": [
"92:109",
0
],
"negative": [
"92:81",
1
],
"positive": [
"92:81",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"92:83": {
"class_type": "LTXVConcatAVLatent",
"inputs": {
"audio_latent": [
"92:80",
1
],
"video_latent": [
"92:84",
0
]
},
"_meta": {
"title": "Concat AV Latent"
}
},
"92:84": {
"class_type": "LTXVLatentUpsampler",
"inputs": {
"samples": [
"92:81",
2
],
"upscale_model": [
"92:76",
0
],
"vae": [
"92:1",
2
]
},
"_meta": {
"title": "spatial"
}
},
"92:89": {
"class_type": "EmptyImage",
"inputs": {
"batch_size": 1,
"color": 0,
"height": 1280,
"width": 1280
},
"_meta": {
"title": "Empty Image"
}
},
"92:9": {
"class_type": "LTXVScheduler",
"inputs": {
"base_shift": 0.95,
"latent": [
"92:56",
0
],
"max_shift": 2.05,
"steps": 12,
"stretch": True,
"terminal": 0.1
},
"_meta": {
"title": "LTXVScheduler"
}
},
"92:90": {
"class_type": "ImageScaleBy",
"inputs": {
"image": [
"92:89",
0
],
"scale_by": 0.5,
"upscale_method": "lanczos"
},
"_meta": {
"title": "Upscale Image By"
}
},
"92:91": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"92:90",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"92:94": {
"class_type": "LTXVSeparateAVLatent",
"inputs": {
"av_latent": [
"92:70",
1
]
},
"_meta": {
"title": "Separate AV Latent"
}
},
"92:96": {
"class_type": "LTXVAudioVAEDecode",
"inputs": {
"audio_vae": [
"92:48",
0
],
"samples": [
"92:94",
1
]
},
"_meta": {
"title": "LTXV Audio VAE Decode"
}
},
"92:97": {
"class_type": "CreateVideo",
"inputs": {
"audio": [
"92:96",
0
],
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": [
"92:102",
0
],
"images": [
"92:98",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"92:98": {
"class_type": "VAEDecodeTiled",
"inputs": {
"overlap": 64,
"samples": [
"92:94",
0
],
"temporal_overlap": 8,
"temporal_size": 4096,
"tile_size": 512,
"vae": [
"92:1",
2
]
},
"_meta": {
"title": "VAE Decode (Tiled)"
}
},
"92:99": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 24
},
"_meta": {
"title": "Frame Rate(int)"
}
}
})
# 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 LoadImage node supports common image formats such as JPEG, PNG, and BMP.
Yes, the intensity of the squish effect can be adjusted within the subgraph node settings.
The default resolution is 720p, but you can use 1920x1088 if your system supports it.
No, the workflow will automatically adjust to the closest valid parameters without causing errors.