ComfyUI/comfy_api_nodes/nodes_api.py

import os
import requests
from inspect import cleandoc
from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, IO
from comfy_api_nodes.apis.client import ApiEndpoint, SynchronousOperation, HttpMethod, PollingOperation, EmptyRequest
from comfy_api_nodes.apis.stubs import IdeogramGenerateRequest, IdeogramGenerateResponse, ImageRequest, MinimaxVideoGenerationRequest, MinimaxVideoGenerationResponse, MinimaxFileRetrieveResponse, MinimaxTaskResultResponse, Model
import folder_paths
import logging
from comfy.comfy_types.node_typing import FileLocator
import json
import av


def check_auth_token(auth_token):
    """Verify that an auth token is present."""
    if auth_token is None:
        raise Exception("Please login first to use this node.")
    return auth_token

class IdeogramTextToImage(ComfyNodeABC):
    """
    Generates images synchronously based on a given prompt and optional parameters.

    Images links are available for a limited period of time; if you would like to keep the image, you must download it.
    """
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls) -> InputTypeDict:
        """
            Return a dictionary which contains config for all input fields.
            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
            The type can be a list for selection.

            Returns: `dict`:
                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
                - Value input_fields (`dict`): Contains input fields config:
                    * Key field_name (`string`): Name of a entry-point method's argument
                    * Value field_config (`tuple`):
                        + First value is a string indicate the type of field or a list for selection.
                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
        """
        return {
            "required": {
                "prompt": (IO.STRING, {
                    "multiline": True,
                    "default": "",
                    "tooltip": "Prompt for the image generation",
                }),
                "model": (IO.COMBO, { "options": ["V_2", "V_2_TURBO", "V_1", "V_1_TURBO"], "default": "V_2", "tooltip": "Model to use for image generation"}),
            },
            "optional": {
                "aspect_ratio": (IO.COMBO, { "options": ["ASPECT_1_1", "ASPECT_4_3", "ASPECT_3_4", "ASPECT_16_9", "ASPECT_9_16", "ASPECT_2_1", "ASPECT_1_2", "ASPECT_3_2", "ASPECT_2_3", "ASPECT_4_5", "ASPECT_5_4"], "default": "ASPECT_1_1", "tooltip": "The aspect ratio for image generation. Cannot be used with resolution"
                }),
                "resolution": (IO.COMBO, { "options": ["1024x1024", "1024x1792", "1792x1024"],
                    "default": "1024x1024",
                    "tooltip": "The resolution for image generation (V2 only). Cannot be used with aspect_ratio"
                }),
                "magic_prompt_option": (IO.COMBO, { "options": ["AUTO", "ON", "OFF"],
                    "default": "AUTO",
                    "tooltip": "Determine if MagicPrompt should be used in generation"
                }),
                "seed": (IO.INT, {
                    "default": 0,
                    "min": 0,
                    "max": 2147483647,
                    "step": 1,
                    "display": "number"
                }),
                "style_type": (IO.COMBO, { "options": ["NONE", "ANIME", "CINEMATIC", "CREATIVE", "DIGITAL_ART", "PHOTOGRAPHIC"],
                    "default": "NONE",
                    "tooltip": "Style type for generation (V2+ only)"
                }),
                "negative_prompt": (IO.STRING, {
                    "multiline": True,
                    "default": "",
                    "tooltip": "Description of what to exclude from the image (V1/V2 only)"
                }),
                "num_images": (IO.INT, {
                    "default": 1,
                    "min": 1,
                    "max": 8,
                    "step": 1,
                    "display": "number"
                }),
                "color_palette": (IO.STRING, {
                    "multiline": False,
                    "default": "",
                    "tooltip": "Color palette preset name or hex colors with weights (V2/V2_TURBO only)"
                }),
            },
            "hidden": {
                "auth_token": "AUTH_TOKEN_COMFY_ORG"
            }
        }

    RETURN_TYPES = (IO.IMAGE,)
    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
    FUNCTION = "api_call"
    API_NODE = True
    CATEGORY = "Example"

    def api_call(self, prompt, model, aspect_ratio=None, resolution=None,
                 magic_prompt_option="AUTO", seed=0, style_type="NONE",
                 negative_prompt="", num_images=1, color_palette="", auth_token=None):
        import torch
        from PIL import Image
        import io
        import numpy as np
        import requests

        operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path="/proxy/ideogram/generate",
                method=HttpMethod.POST,
                request_model=IdeogramGenerateRequest,
                response_model=IdeogramGenerateResponse
            ),
            request=IdeogramGenerateRequest(
                image_request=ImageRequest(
                  prompt=prompt,
                  model=model,
                  num_images=num_images,
                  seed=seed,
                  aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None,
                  resolution=resolution if resolution != "1024x1024" else None,
                  magic_prompt_option=magic_prompt_option if magic_prompt_option != "AUTO" else None,
                  style_type=style_type if style_type != "NONE" else None,
                  negative_prompt=negative_prompt if negative_prompt else None,
                  color_palette=None
                )
            ),
            auth_token=auth_token
        )

        response = operation.execute()

        if not response.data or len(response.data) == 0:
            raise Exception("No images were generated in the response")
        image_url = response.data[0].url

        if not image_url:
            raise Exception("No image URL was generated in the response")
        img_response = requests.get(image_url)
        if img_response.status_code != 200:
            raise Exception("Failed to download the image")

        img = Image.open(io.BytesIO(img_response.content))
        img = img.convert("RGB")  # Ensure RGB format

        # Convert to numpy array, normalize to float32 between 0 and 1
        img_array = np.array(img).astype(np.float32) / 255.0

        # Convert to torch tensor and add batch dimension
        img_tensor = torch.from_numpy(img_array)[None,]

        return (img_tensor,)

    """
        The node will always be re executed if any of the inputs change but
        this method can be used to force the node to execute again even when the inputs don't change.
        You can make this node return a number or a string. This value will be compared to the one returned the last time the node was
        executed, if it is different the node will be executed again.
        This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash
        changes between executions the LoadImage node is executed again.
    """
    #@classmethod
    #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
    #    return ""


class MinimaxVideoNode:
    """
    Generates videos synchronously based on a prompt, and optional parameters using Minimax's API.
    """

    def __init__(self):
        self.output_dir = folder_paths.get_output_directory()
        self.type = "output"

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "prompt_text": (
                    "STRING",
                    {
                        "multiline": True,
                        "default": "",
                        "tooltip": "Text prompt to guide the video generation",
                    },
                ),
                "filename_prefix": ("STRING", {"default": "ComfyUI"}),
                "model": (
                    [
                        "T2V-01",
                        "I2V-01-Director",
                        "S2V-01",
                        "I2V-01",
                        "I2V-01-live",
                        "T2V-01",
                    ],
                    {
                        "default": "T2V-01",
                        "tooltip": "Model to use for video generation",
                    },
                ),
            },
            "optional": {
                "seed": (
                    IO.INT,
                    {
                        "default": 0,
                        "min": 0,
                        "max": 0xFFFFFFFFFFFFFFFF,
                        "control_after_generate": True,
                        "tooltip": "The random seed used for creating the noise.",
                    },
                ),
            },
            "hidden": {
                "prompt": "PROMPT",
                "extra_pnginfo": "EXTRA_PNGINFO",
                "auth_token": "AUTH_TOKEN_COMFY_ORG",
            },
        }

    RETURN_TYPES = ("VIDEO",)
    DESCRIPTION = "Generates videos from prompts using Minimax's API"
    FUNCTION = "generate_video"
    CATEGORY = "video"
    API_NODE = True
    OUTPUT_NODE = True

    def generate_video(
        self,
        prompt_text,
        filename_prefix,
        seed=0,
        model="T2V-01",
        prompt=None,
        extra_pnginfo=None,
        auth_token=None,
    ):
        video_generate_operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path="/proxy/minimax/video_generation",
                method=HttpMethod.POST,
                request_model=MinimaxVideoGenerationRequest,
                response_model=MinimaxVideoGenerationResponse,
            ),
            request=MinimaxVideoGenerationRequest(
                model=Model(model),
                prompt=prompt_text,
                callback_url=None,
                first_frame_image=None,
                subject_reference=None,
                prompt_optimizer=None,
            ),
            auth_token=auth_token,
        )
        response = video_generate_operation.execute()

        task_id = response.task_id

        video_generate_operation = PollingOperation(
            poll_endpoint=ApiEndpoint(
                path="/proxy/minimax/query/video_generation",
                method=HttpMethod.GET,
                request_model=EmptyRequest,
                response_model=MinimaxTaskResultResponse,
                query_params={"task_id": task_id},
            ),
            completed_statuses=["Success"],
            failed_statuses=["Fail"],
            status_extractor=lambda x: x.status.value,
            auth_token=auth_token,
        )
        task_result = video_generate_operation.execute()

        file_id = task_result.file_id

        file_retrieve_operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path="/proxy/minimax/files/retrieve",
                method=HttpMethod.GET,
                request_model=EmptyRequest,
                response_model=MinimaxFileRetrieveResponse,
                query_params={"file_id": file_id},
            ),
            request=EmptyRequest(),
            auth_token=auth_token,
        )
        file_result = file_retrieve_operation.execute()

        file_url = file_result.file.download_url

        logging.info(f"Generated video URL: {file_url}")

        # Construct the save path
        full_output_folder, filename, counter, subfolder, filename_prefix = (
            folder_paths.get_save_image_path(filename_prefix, self.output_dir)
        )
        file_basename = f"{filename}_{counter:05}_.mp4"
        save_path = os.path.join(full_output_folder, file_basename)

        # Download the video data
        video_response = requests.get(file_url)
        video_data = video_response.content

        # Save the video data to a file
        with open(save_path, "wb") as video_file:
            video_file.write(video_data)

        # Add workflow metadata to the video container
        if prompt is not None or extra_pnginfo is not None:
            try:
                container = av.open(save_path, mode="r+")
                if prompt is not None:
                    container.metadata["prompt"] = json.dumps(prompt)
                if extra_pnginfo is not None:
                    for x in extra_pnginfo:
                        container.metadata[x] = json.dumps(extra_pnginfo[x])
                container.close()
            except Exception as e:
                logging.warning(f"Failed to add metadata to video: {e}")

        # Create a FileLocator for the frontend to use for the preview
        results: list[FileLocator] = [
            {
                "filename": file_basename,
                "subfolder": subfolder,
                "type": self.type,
            }
        ]

        return {"ui": {"images": results, "animated": (True,)}}


# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
    "IdeogramTextToImage": IdeogramTextToImage,
    "MinimaxVideoNode": MinimaxVideoNode
}

# A dictionary that contains the friendly/humanly readable titles for the nodes
NODE_DISPLAY_NAME_MAPPINGS = {
    "IdeogramTextToImage": "Ideogram Text to Image",
    "MinimaxVideoNode": "Minimax Video Generator"
}