> ## Documentation Index
> Fetch the complete documentation index at: https://docs.comfy.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Luma Image to Video - ComfyUI 原生合作伙伴节点文档
> 使用Luma AI将静态图像转换为动态视频的节点
Luma Image to Video 节点允许你使用Luma AI的先进技术，将静态图像转换为流畅、动态的视频内容，为图像赋予生命力和动态特性。
## 节点功能
此节点连接到Luma AI的图像到视频API，让用户能够基于输入图像创建动态视频。它可以理解图像中的内容并生成自然、连贯的动作，同时保持原始图像的视觉风格和特性。结合文本提示词，用户可以精确控制生成视频的动态效果。
## 参数说明
### 基本参数
| 参数 | 类型 | 默认值 | 说明 |
| ---------- | --- | ------ | ---------------------------- |
| prompt | 字符串 | "" | 描述视频动作和内容的文本提示词 |
| model | 选择项 | - | 使用的视频生成模型 |
| resolution | 选择项 | "540p" | 输出视频分辨率 |
| duration | 选择项 | - | 视频时长选项 |
| loop | 布尔值 | False | 是否循环播放视频 |
| seed | 整数 | 0 | 种子值，用于确定节点是否应重新运行，但实际结果与种子无关 |
### 可选参数
| 参数 | 类型 | 说明 |
| -------------- | -------------- | -------------------------------- |
| first\_image | 图像 | 视频的第一帧图像（与last\_image至少需要提供一个） |
| last\_image | 图像 | 视频的最后一帧图像（与first\_image至少需要提供一个） |
| luma\_concepts | LUMA\_CONCEPTS | 用于控制相机运动和镜头效果的概念引导 |
### 参数要求
* **first\_image** 和 **last\_image** 至少需要提供其中一个
* 每个图像输入（first\_image和last\_image）最多只接受1张图片
### 输出
| 输出 | 类型 | 说明 |
| ----- | -- | ------- |
| VIDEO | 视频 | 生成的视频结果 |
## 使用示例
Luma Image to Video 工作流教程
## 工作原理
Luma Image to Video 节点分析输入图像的内容和结构，然后结合文本提示词来确定如何为图像添加动态效果。它使用Luma AI的生成模型理解图像中的对象、人物或场景，并创建合理、连贯的动作序列。
用户可以通过提示词描述期望的动作类型、方向和强度，节点将据此生成相应的视频效果。通过设置不同的参数，如分辨率和时长，用户可以进一步定制输出视频的特性。
此外，通过提供起始帧和最后帧参考图像，用户可以指定视频的起始和结束状态，使动作朝特定方向发展。概念引导功能则允许用户进一步控制视频的整体风格、相机运动和美学效果。
## 源码参考
\[节点源码 (更新于2025-05-03)]
```python theme={null}
class LumaImageToVideoGenerationNode(ComfyNodeABC):
"""
Generates videos synchronously based on prompt, input images, and output_size.
"""
RETURN_TYPES = (IO.VIDEO,)
DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
FUNCTION = "api_call"
API_NODE = True
CATEGORY = "api node/video/Luma"
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"prompt": (
IO.STRING,
{
"multiline": True,
"default": "",
"tooltip": "Prompt for the video generation",
},
),
"model": ([model.value for model in LumaVideoModel],),
# "aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], {
# "default": LumaAspectRatio.ratio_16_9,
# }),
"resolution": (
[resolution.value for resolution in LumaVideoOutputResolution],
{
"default": LumaVideoOutputResolution.res_540p,
},
),
"duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
"loop": (
IO.BOOLEAN,
{
"default": False,
},
),
"seed": (
IO.INT,
{
"default": 0,
"min": 0,
"max": 0xFFFFFFFFFFFFFFFF,
"control_after_generate": True,
"tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
},
),
},
"optional": {
"first_image": (
IO.IMAGE,
{"tooltip": "First frame of generated video."},
),
"last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}),
"luma_concepts": (
LumaIO.LUMA_CONCEPTS,
{
"tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
},
),
},
"hidden": {
"auth_token": "AUTH_TOKEN_COMFY_ORG",
},
}
def api_call(
self,
prompt: str,
model: str,
resolution: str,
duration: str,
loop: bool,
seed,
first_image: torch.Tensor = None,
last_image: torch.Tensor = None,
luma_concepts: LumaConceptChain = None,
auth_token=None,
**kwargs,
):
if first_image is None and last_image is None:
raise Exception(
"At least one of first_image and last_image requires an input."
)
keyframes = self._convert_to_keyframes(first_image, last_image, auth_token)
duration = duration if model != LumaVideoModel.ray_1_6 else None
resolution = resolution if model != LumaVideoModel.ray_1_6 else None
operation = SynchronousOperation(
endpoint=ApiEndpoint(
path="/proxy/luma/generations",
method=HttpMethod.POST,
request_model=LumaGenerationRequest,
response_model=LumaGeneration,
),
request=LumaGenerationRequest(
prompt=prompt,
model=model,
aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason
resolution=resolution,
duration=duration,
loop=loop,
keyframes=keyframes,
concepts=luma_concepts.create_api_model() if luma_concepts else None,
),
auth_token=auth_token,
)
response_api: LumaGeneration = operation.execute()
operation = PollingOperation(
poll_endpoint=ApiEndpoint(
path=f"/proxy/luma/generations/{response_api.id}",
method=HttpMethod.GET,
request_model=EmptyRequest,
response_model=LumaGeneration,
),
completed_statuses=[LumaState.completed],
failed_statuses=[LumaState.failed],
status_extractor=lambda x: x.state,
auth_token=auth_token,
)
response_poll = operation.execute()
vid_response = requests.get(response_poll.assets.video)
return (VideoFromFile(BytesIO(vid_response.content)),)
def _convert_to_keyframes(
self,
first_image: torch.Tensor = None,
last_image: torch.Tensor = None,
auth_token=None,
):
if first_image is None and last_image is None:
return None
frame0 = None
frame1 = None
if first_image is not None:
download_urls = upload_images_to_comfyapi(
first_image, max_images=1, auth_token=auth_token
)
frame0 = LumaImageReference(type="image", url=download_urls[0])
if last_image is not None:
download_urls = upload_images_to_comfyapi(
last_image, max_images=1, auth_token=auth_token
)
frame1 = LumaImageReference(type="image", url=download_urls[0])
return LumaKeyframes(frame0=frame0, frame1=frame1)
```