Capybara: Image to Video
Upload an image and provide a text instruction. Generate a new video where the input is edited according to your instruction, preserving structure and temporal coherence.
简介
The 'Capybara: Image to Video' workflow is designed to transform a static image into a dynamic video based on user-provided text instructions. This workflow leverages the Capybara model, which excels in maintaining the structure and temporal coherence of the original image while introducing the specified changes.
Technically, the process begins with the 'LoadImage' node, which imports the initial image. The core transformation is handled by the Capybara model node (4f4ddf39-1508-4d34-a35c-ff10e6ce995b), which interprets the text instructions to edit the image progressively across frames, creating a seamless video output.
Finally, the 'SaveVideo' node compiles these frames into a coherent video file.
This workflow is particularly useful for content creators and digital artists who wish to animate static images with specific thematic or stylistic changes. By preserving the original image's structure, the workflow ensures that the resulting video remains true to the source material, while the temporal coherence feature prevents jarring transitions between frames. The Capybara model's ability to handle various resolutions and aspect ratios, as detailed in the resolution configuration, adds versatility to this tool, making it suitable for different platforms and display requirements.
你需要提供
- 正在读取工作流
在 ComfyUI 中运行。 此工作流无法在本页面运行。你可以在 Comfy Cloud 中打开（已预先加载所需内容），或下载后在自己的 ComfyUI 中使用。
在 Cloud 中试用查看工作流内部
节点图的只读预览。在 Comfy Cloud 中打开或下载后即可编辑节点。
Loading the graph
Open full-size workflow preview- 作者
- ComfyUI
需要自定义节点
- 正在读取工作流
通过代码使用此工作流
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。
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({
"114": {
"class_type": "SaveVideo",
"inputs": {
"filename_prefix": "video/ComfyUI",
"format": "auto",
"format.codec": "auto",
"video": [
"103:113",
0
]
},
"_meta": {
"title": "Save Video"
}
},
"126": {
"class_type": "LoadImage",
"inputs": {
"image": "joy_snap.png"
},
"_meta": {
"title": "Load Image"
}
},
"103:10": {
"class_type": "VAELoader",
"inputs": {
"vae_name": "hunyuanvideo15_vae_fp16.safetensors"
},
"_meta": {
"title": "Load VAE"
}
},
"103:11": {
"class_type": "DualCLIPLoader",
"inputs": {
"clip_name1": "qwen_2.5_vl_7b.safetensors",
"clip_name2": "byt5_small_glyphxl_fp16.safetensors",
"device": "default",
"type": "hunyuan_video_15"
},
"_meta": {
"title": "Load CLIP (Dual)"
}
},
"103:113": {
"class_type": "CreateVideo",
"inputs": {
"bit_depth": "auto",
"codec": "none",
"color_space": "sRGB",
"fps": 24,
"images": [
"103:8",
0
]
},
"_meta": {
"title": "Create Video"
}
},
"103:115": {
"class_type": "HunyuanVideo15ImageToVideo",
"inputs": {
"batch_size": 1,
"clip_vision_output": [
"103:119",
0
],
"height": [
"103:122",
0
],
"length": 81,
"negative": [
"103:93",
0
],
"positive": [
"103:44",
0
],
"start_image": [
"103:118",
0
],
"vae": [
"103:10",
0
],
"width": [
"103:121",
0
]
},
"_meta": {
"title": "HunyuanVideo15ImageToVideo"
}
},
"103:117": {
"class_type": "CLIPVisionLoader",
"inputs": {
"clip_name": "sigclip_vision_patch14_384.safetensors"
},
"_meta": {
"title": "Load CLIP Vision"
}
},
"103:118": {
"class_type": "ResizeAndPadImage",
"inputs": {
"image": [
"126",
0
],
"interpolation": "area",
"padding_color": "white",
"target_height": [
"103:122",
0
],
"target_width": [
"103:121",
0
]
},
"_meta": {
"title": "Resize And Pad Image"
}
},
"103:119": {
"class_type": "CLIPVisionEncode",
"inputs": {
"clip_vision": [
"103:117",
0
],
"crop": "center",
"image": [
"126",
0
]
},
"_meta": {
"title": "CLIP Vision Encode"
}
},
"103:12": {
"class_type": "UNETLoader",
"inputs": {
"unet_name": "capybara_v0.1.safetensors",
"weight_dtype": "default"
},
"_meta": {
"title": "Load Diffusion Model"
}
},
"103:121": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 640
},
"_meta": {
"title": "Width"
}
},
"103:122": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 640
},
"_meta": {
"title": "Height"
}
},
"103:13": {
"class_type": "SamplerCustomAdvanced",
"inputs": {
"guider": [
"103:91",
0
],
"latent_image": [
"103:115",
2
],
"noise": [
"103:25",
0
],
"sampler": [
"103:16",
0
],
"sigmas": [
"103:17",
0
]
},
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"103:16": {
"class_type": "KSamplerSelect",
"inputs": {
"sampler_name": "euler"
},
"_meta": {
"title": "KSamplerSelect"
}
},
"103:17": {
"class_type": "BasicScheduler",
"inputs": {
"denoise": 1,
"model": [
"103:12",
0
],
"scheduler": "simple",
"steps": 20
},
"_meta": {
"title": "BasicScheduler"
}
},
"103:25": {
"class_type": "RandomNoise",
"inputs": {
"noise_seed": 663555858763862
},
"_meta": {
"title": "RandomNoise"
}
},
"103:44": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"103:11",
0
],
"text": "A vibrant, sunny video scene: a young man and woman sitting on a green bench on a tennis court, taking a selfie with a white smartphone. The camera starts at a low angle and slowly tilts upward, revealing the clear blue sky. The man wears a light green jacket and shorts, the woman wears a light green blazer and trousers with a red and white striped turtleneck. The woman raises one arm playfully, and they hold hands, radiating youthful energy. The overall style is bright, cinematic, with smooth, slow camera movement and warm, vivid color grading."
},
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"103:67": {
"class_type": "ModelSamplingSD3",
"inputs": {
"model": [
"103:12",
0
],
"shift": 7
},
"_meta": {
"title": "ModelSamplingSD3"
}
},
"103:8": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"103:13",
0
],
"vae": [
"103:10",
0
]
},
"_meta": {
"title": "VAE Decode"
}
},
"103:91": {
"class_type": "CFGGuider",
"inputs": {
"cfg": 6,
"model": [
"103:67",
0
],
"negative": [
"103:115",
1
],
"positive": [
"103:115",
0
]
},
"_meta": {
"title": "CFG Guider"
}
},
"103:93": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"103:11",
0
],
"text": "blurry, low quality, distorted, ugly, watermark, text"
},
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)常见问答
Images with clear structure and elements that can be logically animated based on the text instructions work best. High-resolution images are recommended for better video quality.
The Capybara model uses advanced algorithms to ensure smooth transitions between frames, maintaining the visual consistency of the original image while applying the specified changes.
It's best to set your desired resolution before running the workflow. However, you can use video editing software to adjust the resolution post-processing if needed.
Try refining your text instructions for clarity and specificity, and consider increasing the number of steps to 50 for improved detail and quality.