Merge 606052119c into 7ac999bf30

comfy_api_nodes/apis/openai.py

@@ -1,170 +0,0 @@
-from pydantic import BaseModel, Field
-
-
-class Datum2(BaseModel):
-    b64_json: str | None = Field(None, description="Base64 encoded image data")
-    revised_prompt: str | None = Field(None, description="Revised prompt")
-    url: str | None = Field(None, description="URL of the image")
-
-
-class InputTokensDetails(BaseModel):
-    image_tokens: int | None = Field(None)
-    text_tokens: int | None = Field(None)
-
-
-class Usage(BaseModel):
-    input_tokens: int | None = Field(None)
-    input_tokens_details: InputTokensDetails | None = Field(None)
-    output_tokens: int | None = Field(None)
-    total_tokens: int | None = Field(None)
-
-
-class OpenAIImageGenerationResponse(BaseModel):
-    data: list[Datum2] | None = Field(None)
-    usage: Usage | None = Field(None)
-
-
-class OpenAIImageEditRequest(BaseModel):
-    background: str | None = Field(None, description="Background transparency")
-    model: str = Field(...)
-    moderation: str | None = Field(None)
-    n: int | None = Field(None, description="The number of images to generate")
-    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
-    output_format: str | None = Field(None)
-    prompt: str = Field(...)
-    quality: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
-    size: str | None = Field(None, description="Size of the output image")
-
-
-class OpenAIImageGenerationRequest(BaseModel):
-    background: str | None = Field(None, description="Background transparency")
-    model: str | None = Field(None)
-    moderation: str | None = Field(None)
-    n: int | None = Field(
-        None,
-        description="The number of images to generate.",
-    )
-    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
-    output_format: str | None = Field(None)
-    prompt: str = Field(...)
-    quality: str | None = Field(None, description="The quality of the generated image")
-    size: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
-    style: str | None = Field(None, description="Style of the image (only for dall-e-3)")
-
-
-class ModelResponseProperties(BaseModel):
-    instructions: str | None = Field(None)
-    max_output_tokens: int | None = Field(None)
-    model: str | None = Field(None)
-    temperature: float | None = Field(1, description="Controls randomness in the response", ge=0.0, le=2.0)
-    top_p: float | None = Field(
-        1,
-        description="Controls diversity of the response via nucleus sampling",
-        ge=0.0,
-        le=1.0,
-    )
-    truncation: str | None = Field("disabled", description="Allowed values: 'auto' or 'disabled'")
-
-
-class ResponseProperties(BaseModel):
-    instructions: str | None = Field(None)
-    max_output_tokens: int | None = Field(None)
-    model: str | None = Field(None)
-    previous_response_id: str | None = Field(None)
-    truncation: str | None = Field("disabled", description="Allowed values: 'auto' or 'disabled'")
-
-
-class ResponseError(BaseModel):
-    code: str = Field(...)
-    message: str = Field(...)
-
-
-class OutputTokensDetails(BaseModel):
-    reasoning_tokens: int = Field(..., description="The number of reasoning tokens.")
-
-
-class CachedTokensDetails(BaseModel):
-    cached_tokens: int = Field(
-        ...,
-        description="The number of tokens that were retrieved from the cache.",
-    )
-
-
-class ResponseUsage(BaseModel):
-    input_tokens: int = Field(..., description="The number of input tokens.")
-    input_tokens_details: CachedTokensDetails = Field(...)
-    output_tokens: int = Field(..., description="The number of output tokens.")
-    output_tokens_details: OutputTokensDetails = Field(...)
-    total_tokens: int = Field(..., description="The total number of tokens used.")
-
-
-class InputTextContent(BaseModel):
-    text: str = Field(..., description="The text input to the model.")
-    type: str = Field("input_text")
-
-
-class OutputContent(BaseModel):
-    type: str = Field(..., description="The type of output content")
-    text: str | None = Field(None, description="The text content")
-    data: str | None = Field(None, description="Base64-encoded audio data")
-    transcript: str | None = Field(None, description="Transcript of the audio")
-
-
-class OutputMessage(BaseModel):
-    type: str = Field(..., description="The type of output item")
-    content: list[OutputContent] | None = Field(None, description="The content of the message")
-    role: str | None = Field(None, description="The role of the message")
-
-
-class OpenAIResponse(ModelResponseProperties, ResponseProperties):
-    created_at: float | None = Field(
-        None,
-        description="Unix timestamp (in seconds) of when this Response was created.",
-    )
-    error: ResponseError | None = Field(None)
-    id: str | None = Field(None, description="Unique identifier for this Response.")
-    object: str | None = Field(None, description="The object type of this resource - always set to `response`.")
-    output: list[OutputMessage] | None = Field(None)
-    parallel_tool_calls: bool | None = Field(True)
-    status: str | None = Field(
-        None,
-        description="One of `completed`, `failed`, `in_progress`, or `incomplete`.",
-    )
-    usage: ResponseUsage | None = Field(None)
-
-
-class InputImageContent(BaseModel):
-    detail: str = Field(..., description="One of `high`, `low`, or `auto`. Defaults to `auto`.")
-    file_id: str | None = Field(None)
-    image_url: str | None = Field(None)
-    type: str = Field(..., description="The type of the input item. Always `input_image`.")
-
-
-class InputFileContent(BaseModel):
-    file_data: str | None = Field(None)
-    file_id: str | None = Field(None)
-    filename: str | None = Field(None, description="The name of the file to be sent to the model.")
-    type: str = Field(..., description="The type of the input item. Always `input_file`.")
-
-
-class InputMessage(BaseModel):
-    content: list[InputTextContent | InputImageContent | InputFileContent] = Field(
-        ...,
-        description="A list of one or many input items to the model, containing different content types.",
-    )
-    role: str | None = Field(None)
-    type: str | None = Field(None)
-
-
-class OpenAICreateResponse(ModelResponseProperties, ResponseProperties):
-    include: str | None = Field(None)
-    input: list[InputMessage] = Field(...)
-    parallel_tool_calls: bool | None = Field(
-        True, description="Whether to allow the model to run tool calls in parallel."
-    )
-    store: bool | None = Field(
-        True,
-        description="Whether to store the generated model response for later retrieval via API.",
-    )
-    stream: bool | None = Field(False)
-    usage: ResponseUsage | None = Field(None)

comfy_api_nodes/apis/openai_api.py

@@ -0,0 +1,52 @@
+from pydantic import BaseModel, Field
+
+
+class Datum2(BaseModel):
+    b64_json: str | None = Field(None, description="Base64 encoded image data")
+    revised_prompt: str | None = Field(None, description="Revised prompt")
+    url: str | None = Field(None, description="URL of the image")
+
+
+class InputTokensDetails(BaseModel):
+    image_tokens: int | None = None
+    text_tokens: int | None = None
+
+
+class Usage(BaseModel):
+    input_tokens: int | None = None
+    input_tokens_details: InputTokensDetails | None = None
+    output_tokens: int | None = None
+    total_tokens: int | None = None
+
+
+class OpenAIImageGenerationResponse(BaseModel):
+    data: list[Datum2] | None = None
+    usage: Usage | None = None
+
+
+class OpenAIImageEditRequest(BaseModel):
+    background: str | None = Field(None, description="Background transparency")
+    model: str = Field(...)
+    moderation: str | None = Field(None)
+    n: int | None = Field(None, description="The number of images to generate")
+    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
+    output_format: str | None = Field(None)
+    prompt: str = Field(...)
+    quality: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
+    size: str | None = Field(None, description="Size of the output image")
+
+
+class OpenAIImageGenerationRequest(BaseModel):
+    background: str | None = Field(None, description="Background transparency")
+    model: str | None = Field(None)
+    moderation: str | None = Field(None)
+    n: int | None = Field(
+        None,
+        description="The number of images to generate.",
+    )
+    output_compression: int | None = Field(None, description="Compression level for JPEG or WebP (0-100)")
+    output_format: str | None = Field(None)
+    prompt: str = Field(...)
+    quality: str | None = Field(None, description="The quality of the generated image")
+    size: str | None = Field(None, description="Size of the image (e.g., 1024x1024, 1536x1024, auto)")
+    style: str | None = Field(None, description="Style of the image (only for dall-e-3)")

comfy_api_nodes/apis/topaz_api.py

@@ -41,7 +41,7 @@ class Resolution(BaseModel):
     height: int = Field(...)
 
 
-class CreateVideoRequestSource(BaseModel):
+class CreateCreateVideoRequestSource(BaseModel):
     container: str = Field(...)
     size: int = Field(..., description="Size of the video file in bytes")
     duration: int = Field(..., description="Duration of the video file in seconds")
@@ -89,7 +89,7 @@ class Overrides(BaseModel):
 
 
 class CreateVideoRequest(BaseModel):
-    source: CreateVideoRequestSource = Field(...)
+    source: CreateCreateVideoRequestSource = Field(...)
     filters: list[Union[VideoFrameInterpolationFilter, VideoEnhancementFilter]] = Field(...)
     output: OutputInformationVideo = Field(...)
     overrides: Overrides = Field(Overrides(isPaidDiffusion=True))

comfy_api_nodes/nodes_bfl.py

@@ -3,7 +3,7 @@ from pydantic import BaseModel
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.bfl import (
+from comfy_api_nodes.apis.bfl_api import (
     BFLFluxExpandImageRequest,
     BFLFluxFillImageRequest,
     BFLFluxKontextProGenerateRequest,

comfy_api_nodes/nodes_bytedance.py

@@ -5,7 +5,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.bytedance import (
+from comfy_api_nodes.apis.bytedance_api import (
     RECOMMENDED_PRESETS,
     RECOMMENDED_PRESETS_SEEDREAM_4,
     VIDEO_TASKS_EXECUTION_TIME,

comfy_api_nodes/nodes_gemini.py

@@ -14,7 +14,7 @@ from typing_extensions import override
 
 import folder_paths
 from comfy_api.latest import IO, ComfyExtension, Input, Types
-from comfy_api_nodes.apis.gemini import (
+from comfy_api_nodes.apis.gemini_api import (
     GeminiContent,
     GeminiFileData,
     GeminiGenerateContentRequest,

comfy_api_nodes/nodes_kling.py

@@ -49,7 +49,7 @@ from comfy_api_nodes.apis import (
     KlingCharacterEffectModelName,
     KlingSingleImageEffectModelName,
 )
-from comfy_api_nodes.apis.kling import (
+from comfy_api_nodes.apis.kling_api import (
     ImageToVideoWithAudioRequest,
     MotionControlRequest,
     OmniImageParamImage,

comfy_api_nodes/nodes_luma.py

@@ -4,7 +4,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.luma import (
+from comfy_api_nodes.apis.luma_api import (
     LumaAspectRatio,
     LumaCharacterRef,
     LumaConceptChain,

comfy_api_nodes/nodes_minimax.py

@@ -4,7 +4,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.minimax import (
+from comfy_api_nodes.apis.minimax_api import (
     MinimaxFileRetrieveResponse,
     MiniMaxModel,
     MinimaxTaskResultResponse,

comfy_api_nodes/nodes_openai.py

@@ -10,18 +10,24 @@ from typing_extensions import override
 
 import folder_paths
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.openai import (
+from comfy_api_nodes.apis import (
+    CreateModelResponseProperties,
+    Detail,
+    InputContent,
     InputFileContent,
     InputImageContent,
     InputMessage,
+    InputMessageContentList,
     InputTextContent,
-    ModelResponseProperties,
+    Item,
     OpenAICreateResponse,
+    OpenAIResponse,
+    OutputContent,
+)
+from comfy_api_nodes.apis.openai_api import (
     OpenAIImageEditRequest,
     OpenAIImageGenerationRequest,
     OpenAIImageGenerationResponse,
-    OpenAIResponse,
-    OutputContent,
 )
 from comfy_api_nodes.util import (
     ApiEndpoint,
@@ -260,7 +266,7 @@ class OpenAIDalle3(IO.ComfyNode):
                     "seed",
                     default=0,
                     min=0,
-                    max=2**31 - 1,
+                    max=2 ** 31 - 1,
                     step=1,
                     display_mode=IO.NumberDisplay.number,
                     control_after_generate=True,
@@ -378,7 +384,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
                     "seed",
                     default=0,
                     min=0,
-                    max=2**31 - 1,
+                    max=2 ** 31 - 1,
                     step=1,
                     display_mode=IO.NumberDisplay.number,
                     control_after_generate=True,
@@ -494,8 +500,8 @@ class OpenAIGPTImage1(IO.ComfyNode):
             files = []
             batch_size = image.shape[0]
             for i in range(batch_size):
-                single_image = image[i : i + 1]
-                scaled_image = downscale_image_tensor(single_image, total_pixels=2048 * 2048).squeeze()
+                single_image = image[i: i + 1]
+                scaled_image = downscale_image_tensor(single_image, total_pixels=2048*2048).squeeze()
 
                 image_np = (scaled_image.numpy() * 255).astype(np.uint8)
                 img = Image.fromarray(image_np)
@@ -517,7 +523,7 @@ class OpenAIGPTImage1(IO.ComfyNode):
                 rgba_mask = torch.zeros(height, width, 4, device="cpu")
                 rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
 
-                scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048 * 2048).squeeze()
+                scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0), total_pixels=2048*2048).squeeze()
 
                 mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
                 mask_img = Image.fromarray(mask_np)
@@ -690,23 +696,29 @@ class OpenAIChatNode(IO.ComfyNode):
         )
 
     @classmethod
-    def get_message_content_from_response(cls, response: OpenAIResponse) -> list[OutputContent]:
+    def get_message_content_from_response(
+        cls, response: OpenAIResponse
+    ) -> list[OutputContent]:
         """Extract message content from the API response."""
         for output in response.output:
-            if output.type == "message":
-                return output.content
+            if output.root.type == "message":
+                return output.root.content
         raise TypeError("No output message found in response")
 
     @classmethod
-    def get_text_from_message_content(cls, message_content: list[OutputContent]) -> str:
+    def get_text_from_message_content(
+        cls, message_content: list[OutputContent]
+    ) -> str:
         """Extract text content from message content."""
         for content_item in message_content:
-            if content_item.type == "output_text":
-                return str(content_item.text)
+            if content_item.root.type == "output_text":
+                return str(content_item.root.text)
         return "No text output found in response"
 
     @classmethod
-    def tensor_to_input_image_content(cls, image: torch.Tensor, detail_level: str = "auto") -> InputImageContent:
+    def tensor_to_input_image_content(
+        cls, image: torch.Tensor, detail_level: Detail = "auto"
+    ) -> InputImageContent:
         """Convert a tensor to an input image content object."""
         return InputImageContent(
             detail=detail_level,
@@ -720,9 +732,9 @@ class OpenAIChatNode(IO.ComfyNode):
         prompt: str,
         image: torch.Tensor | None = None,
         files: list[InputFileContent] | None = None,
-    ) -> list[InputTextContent | InputImageContent | InputFileContent]:
+    ) -> InputMessageContentList:
         """Create a list of input message contents from prompt and optional image."""
-        content_list: list[InputTextContent | InputImageContent | InputFileContent] = [
+        content_list: list[InputContent | InputTextContent | InputImageContent | InputFileContent] = [
             InputTextContent(text=prompt, type="input_text"),
         ]
         if image is not None:
@@ -734,9 +746,13 @@ class OpenAIChatNode(IO.ComfyNode):
                         type="input_image",
                     )
                 )
+
         if files is not None:
             content_list.extend(files)
-        return content_list
+
+        return InputMessageContentList(
+            root=content_list,
+        )
 
     @classmethod
     async def execute(
@@ -746,7 +762,7 @@ class OpenAIChatNode(IO.ComfyNode):
         model: SupportedOpenAIModel = SupportedOpenAIModel.gpt_5.value,
         images: torch.Tensor | None = None,
         files: list[InputFileContent] | None = None,
-        advanced_options: ModelResponseProperties | None = None,
+        advanced_options: CreateModelResponseProperties | None = None,
     ) -> IO.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
 
@@ -757,28 +773,36 @@ class OpenAIChatNode(IO.ComfyNode):
             response_model=OpenAIResponse,
             data=OpenAICreateResponse(
                 input=[
-                    InputMessage(
-                        content=cls.create_input_message_contents(prompt, images, files),
-                        role="user",
+                    Item(
+                        root=InputMessage(
+                            content=cls.create_input_message_contents(
+                                prompt, images, files
+                            ),
+                            role="user",
+                        )
                     ),
                 ],
                 store=True,
                 stream=False,
                 model=model,
                 previous_response_id=None,
-                **(advanced_options.model_dump(exclude_none=True) if advanced_options else {}),
+                **(
+                    advanced_options.model_dump(exclude_none=True)
+                    if advanced_options
+                    else {}
+                ),
             ),
         )
         response_id = create_response.id
 
         # Get result output
         result_response = await poll_op(
-            cls,
-            ApiEndpoint(path=f"{RESPONSES_ENDPOINT}/{response_id}"),
-            response_model=OpenAIResponse,
-            status_extractor=lambda response: response.status,
-            completed_statuses=["incomplete", "completed"],
-        )
+                cls,
+                ApiEndpoint(path=f"{RESPONSES_ENDPOINT}/{response_id}"),
+                response_model=OpenAIResponse,
+                status_extractor=lambda response: response.status,
+                completed_statuses=["incomplete", "completed"]
+            )
         return IO.NodeOutput(cls.get_text_from_message_content(cls.get_message_content_from_response(result_response)))
 
 
@@ -899,7 +923,7 @@ class OpenAIChatConfig(IO.ComfyNode):
             remove depending on model choice.
         """
         return IO.NodeOutput(
-            ModelResponseProperties(
+            CreateModelResponseProperties(
                 instructions=instructions,
                 truncation=truncation,
                 max_output_tokens=max_output_tokens,

comfy_api_nodes/nodes_pixverse.py

@@ -1,7 +1,7 @@
 import torch
 from typing_extensions import override
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.pixverse import (
+from comfy_api_nodes.apis.pixverse_api import (
     PixverseTextVideoRequest,
     PixverseImageVideoRequest,
     PixverseTransitionVideoRequest,

comfy_api_nodes/nodes_recraft.py

@@ -8,7 +8,7 @@ from typing_extensions import override
 
 from comfy.utils import ProgressBar
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.recraft import (
+from comfy_api_nodes.apis.recraft_api import (
     RecraftColor,
     RecraftColorChain,
     RecraftControls,

comfy_api_nodes/nodes_rodin.py

@@ -14,7 +14,7 @@ from typing import Optional
 from io import BytesIO
 from typing_extensions import override
 from PIL import Image
-from comfy_api_nodes.apis.rodin import (
+from comfy_api_nodes.apis.rodin_api import (
     Rodin3DGenerateRequest,
     Rodin3DGenerateResponse,
     Rodin3DCheckStatusRequest,

comfy_api_nodes/nodes_stability.py

@@ -3,7 +3,7 @@ from typing import Optional
 from typing_extensions import override
 
 from comfy_api.latest import ComfyExtension, Input, IO
-from comfy_api_nodes.apis.stability import (
+from comfy_api_nodes.apis.stability_api import (
     StabilityUpscaleConservativeRequest,
     StabilityUpscaleCreativeRequest,
     StabilityAsyncResponse,

comfy_api_nodes/nodes_topaz.py

@@ -5,24 +5,7 @@ import aiohttp
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input
-from comfy_api_nodes.apis.topaz import (
-    CreateVideoRequest,
-    CreateVideoRequestSource,
-    CreateVideoResponse,
-    ImageAsyncTaskResponse,
-    ImageDownloadResponse,
-    ImageEnhanceRequest,
-    ImageStatusResponse,
-    OutputInformationVideo,
-    Resolution,
-    VideoAcceptResponse,
-    VideoCompleteUploadRequest,
-    VideoCompleteUploadRequestPart,
-    VideoCompleteUploadResponse,
-    VideoEnhancementFilter,
-    VideoFrameInterpolationFilter,
-    VideoStatusResponse,
-)
+from comfy_api_nodes.apis import topaz_api
 from comfy_api_nodes.util import (
     ApiEndpoint,
     download_url_to_image_tensor,
@@ -170,13 +153,13 @@ class TopazImageEnhance(IO.ComfyNode):
         if get_number_of_images(image) != 1:
             raise ValueError("Only one input image is supported.")
         download_url = await upload_images_to_comfyapi(
-            cls, image, max_images=1, mime_type="image/png", total_pixels=4096 * 4096
+            cls, image, max_images=1, mime_type="image/png", total_pixels=4096*4096
         )
         initial_response = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/topaz/image/v1/enhance-gen/async", method="POST"),
-            response_model=ImageAsyncTaskResponse,
-            data=ImageEnhanceRequest(
+            response_model=topaz_api.ImageAsyncTaskResponse,
+            data=topaz_api.ImageEnhanceRequest(
                 model=model,
                 prompt=prompt,
                 subject_detection=subject_detection,
@@ -198,7 +181,7 @@ class TopazImageEnhance(IO.ComfyNode):
         await poll_op(
             cls,
             poll_endpoint=ApiEndpoint(path=f"/proxy/topaz/image/v1/status/{initial_response.process_id}"),
-            response_model=ImageStatusResponse,
+            response_model=topaz_api.ImageStatusResponse,
             status_extractor=lambda x: x.status,
             progress_extractor=lambda x: getattr(x, "progress", 0),
             price_extractor=lambda x: x.credits * 0.08,
@@ -210,7 +193,7 @@ class TopazImageEnhance(IO.ComfyNode):
         results = await sync_op(
             cls,
             ApiEndpoint(path=f"/proxy/topaz/image/v1/download/{initial_response.process_id}"),
-            response_model=ImageDownloadResponse,
+            response_model=topaz_api.ImageDownloadResponse,
             monitor_progress=False,
         )
         return IO.NodeOutput(await download_url_to_image_tensor(results.download_url))
@@ -348,7 +331,7 @@ class TopazVideoEnhance(IO.ComfyNode):
             if target_height % 2 != 0:
                 target_height += 1
             filters.append(
-                VideoEnhancementFilter(
+                topaz_api.VideoEnhancementFilter(
                     model=UPSCALER_MODELS_MAP[upscaler_model],
                     creativity=(upscaler_creativity if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
                     isOptimizedMode=(True if UPSCALER_MODELS_MAP[upscaler_model] == "slc-1" else None),
@@ -357,7 +340,7 @@ class TopazVideoEnhance(IO.ComfyNode):
         if interpolation_enabled:
             target_frame_rate = interpolation_frame_rate
             filters.append(
-                VideoFrameInterpolationFilter(
+                topaz_api.VideoFrameInterpolationFilter(
                     model=interpolation_model,
                     slowmo=interpolation_slowmo,
                     fps=interpolation_frame_rate,
@@ -368,19 +351,19 @@ class TopazVideoEnhance(IO.ComfyNode):
         initial_res = await sync_op(
             cls,
             ApiEndpoint(path="/proxy/topaz/video/", method="POST"),
-            response_model=CreateVideoResponse,
-            data=CreateVideoRequest(
-                source=CreateVideoRequestSource(
+            response_model=topaz_api.CreateVideoResponse,
+            data=topaz_api.CreateVideoRequest(
+                source=topaz_api.CreateCreateVideoRequestSource(
                     container="mp4",
                     size=get_fs_object_size(src_video_stream),
                     duration=int(duration_sec),
                     frameCount=video.get_frame_count(),
                     frameRate=src_frame_rate,
-                    resolution=Resolution(width=src_width, height=src_height),
+                    resolution=topaz_api.Resolution(width=src_width, height=src_height),
                 ),
                 filters=filters,
-                output=OutputInformationVideo(
-                    resolution=Resolution(width=target_width, height=target_height),
+                output=topaz_api.OutputInformationVideo(
+                    resolution=topaz_api.Resolution(width=target_width, height=target_height),
                     frameRate=target_frame_rate,
                     audioCodec="AAC",
                     audioTransfer="Copy",
@@ -396,7 +379,7 @@ class TopazVideoEnhance(IO.ComfyNode):
                 path=f"/proxy/topaz/video/{initial_res.requestId}/accept",
                 method="PATCH",
             ),
-            response_model=VideoAcceptResponse,
+            response_model=topaz_api.VideoAcceptResponse,
             wait_label="Preparing upload",
             final_label_on_success="Upload started",
         )
@@ -419,10 +402,10 @@ class TopazVideoEnhance(IO.ComfyNode):
                 path=f"/proxy/topaz/video/{initial_res.requestId}/complete-upload",
                 method="PATCH",
             ),
-            response_model=VideoCompleteUploadResponse,
-            data=VideoCompleteUploadRequest(
+            response_model=topaz_api.VideoCompleteUploadResponse,
+            data=topaz_api.VideoCompleteUploadRequest(
                 uploadResults=[
-                    VideoCompleteUploadRequestPart(
+                    topaz_api.VideoCompleteUploadRequestPart(
                         partNum=1,
                         eTag=upload_etag,
                     ),
@@ -434,7 +417,7 @@ class TopazVideoEnhance(IO.ComfyNode):
         final_response = await poll_op(
             cls,
             ApiEndpoint(path=f"/proxy/topaz/video/{initial_res.requestId}/status"),
-            response_model=VideoStatusResponse,
+            response_model=topaz_api.VideoStatusResponse,
             status_extractor=lambda x: x.status,
             progress_extractor=lambda x: getattr(x, "progress", 0),
             price_extractor=lambda x: (x.estimates.cost[0] * 0.08 if x.estimates and x.estimates.cost[0] else None),

comfy_api_nodes/nodes_tripo.py

@@ -5,7 +5,7 @@ import torch
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension
-from comfy_api_nodes.apis.tripo import (
+from comfy_api_nodes.apis.tripo_api import (
     TripoAnimateRetargetRequest,
     TripoAnimateRigRequest,
     TripoConvertModelRequest,

comfy_api_nodes/nodes_veo2.py

@@ -4,7 +4,7 @@ from io import BytesIO
 from typing_extensions import override
 
 from comfy_api.latest import IO, ComfyExtension, Input, InputImpl
-from comfy_api_nodes.apis.veo import (
+from comfy_api_nodes.apis.veo_api import (
     VeoGenVidPollRequest,
     VeoGenVidPollResponse,
     VeoGenVidRequest,