from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict from inspect import cleandoc from PIL import Image import numpy as np import io import torch from comfy_api_nodes.apis import ( IdeogramGenerateRequest, IdeogramGenerateResponse, ImageRequest, IdeogramV3Request, IdeogramV3EditRequest, ) from comfy_api_nodes.apis.client import ( ApiEndpoint, HttpMethod, SynchronousOperation, ) from comfy_api_nodes.apinode_utils import ( download_url_to_bytesio, bytesio_to_image_tensor, resize_mask_to_image, ) from server import PromptServer V1_V1_RES_MAP = { "Auto":"AUTO", "512 x 1536":"RESOLUTION_512_1536", "576 x 1408":"RESOLUTION_576_1408", "576 x 1472":"RESOLUTION_576_1472", "576 x 1536":"RESOLUTION_576_1536", "640 x 1024":"RESOLUTION_640_1024", "640 x 1344":"RESOLUTION_640_1344", "640 x 1408":"RESOLUTION_640_1408", "640 x 1472":"RESOLUTION_640_1472", "640 x 1536":"RESOLUTION_640_1536", "704 x 1152":"RESOLUTION_704_1152", "704 x 1216":"RESOLUTION_704_1216", "704 x 1280":"RESOLUTION_704_1280", "704 x 1344":"RESOLUTION_704_1344", "704 x 1408":"RESOLUTION_704_1408", "704 x 1472":"RESOLUTION_704_1472", "720 x 1280":"RESOLUTION_720_1280", "736 x 1312":"RESOLUTION_736_1312", "768 x 1024":"RESOLUTION_768_1024", "768 x 1088":"RESOLUTION_768_1088", "768 x 1152":"RESOLUTION_768_1152", "768 x 1216":"RESOLUTION_768_1216", "768 x 1232":"RESOLUTION_768_1232", "768 x 1280":"RESOLUTION_768_1280", "768 x 1344":"RESOLUTION_768_1344", "832 x 960":"RESOLUTION_832_960", "832 x 1024":"RESOLUTION_832_1024", "832 x 1088":"RESOLUTION_832_1088", "832 x 1152":"RESOLUTION_832_1152", "832 x 1216":"RESOLUTION_832_1216", "832 x 1248":"RESOLUTION_832_1248", "864 x 1152":"RESOLUTION_864_1152", "896 x 960":"RESOLUTION_896_960", "896 x 1024":"RESOLUTION_896_1024", "896 x 1088":"RESOLUTION_896_1088", "896 x 1120":"RESOLUTION_896_1120", "896 x 1152":"RESOLUTION_896_1152", "960 x 832":"RESOLUTION_960_832", "960 x 896":"RESOLUTION_960_896", "960 x 1024":"RESOLUTION_960_1024", "960 x 1088":"RESOLUTION_960_1088", "1024 x 640":"RESOLUTION_1024_640", "1024 x 768":"RESOLUTION_1024_768", "1024 x 832":"RESOLUTION_1024_832", "1024 x 896":"RESOLUTION_1024_896", "1024 x 960":"RESOLUTION_1024_960", "1024 x 1024":"RESOLUTION_1024_1024", "1088 x 768":"RESOLUTION_1088_768", "1088 x 832":"RESOLUTION_1088_832", "1088 x 896":"RESOLUTION_1088_896", "1088 x 960":"RESOLUTION_1088_960", "1120 x 896":"RESOLUTION_1120_896", "1152 x 704":"RESOLUTION_1152_704", "1152 x 768":"RESOLUTION_1152_768", "1152 x 832":"RESOLUTION_1152_832", "1152 x 864":"RESOLUTION_1152_864", "1152 x 896":"RESOLUTION_1152_896", "1216 x 704":"RESOLUTION_1216_704", "1216 x 768":"RESOLUTION_1216_768", "1216 x 832":"RESOLUTION_1216_832", "1232 x 768":"RESOLUTION_1232_768", "1248 x 832":"RESOLUTION_1248_832", "1280 x 704":"RESOLUTION_1280_704", "1280 x 720":"RESOLUTION_1280_720", "1280 x 768":"RESOLUTION_1280_768", "1280 x 800":"RESOLUTION_1280_800", "1312 x 736":"RESOLUTION_1312_736", "1344 x 640":"RESOLUTION_1344_640", "1344 x 704":"RESOLUTION_1344_704", "1344 x 768":"RESOLUTION_1344_768", "1408 x 576":"RESOLUTION_1408_576", "1408 x 640":"RESOLUTION_1408_640", "1408 x 704":"RESOLUTION_1408_704", "1472 x 576":"RESOLUTION_1472_576", "1472 x 640":"RESOLUTION_1472_640", "1472 x 704":"RESOLUTION_1472_704", "1536 x 512":"RESOLUTION_1536_512", "1536 x 576":"RESOLUTION_1536_576", "1536 x 640":"RESOLUTION_1536_640", } V1_V2_RATIO_MAP = { "1:1":"ASPECT_1_1", "4:3":"ASPECT_4_3", "3:4":"ASPECT_3_4", "16:9":"ASPECT_16_9", "9:16":"ASPECT_9_16", "2:1":"ASPECT_2_1", "1:2":"ASPECT_1_2", "3:2":"ASPECT_3_2", "2:3":"ASPECT_2_3", "4:5":"ASPECT_4_5", "5:4":"ASPECT_5_4", } V3_RATIO_MAP = { "1:3":"1x3", "3:1":"3x1", "1:2":"1x2", "2:1":"2x1", "9:16":"9x16", "16:9":"16x9", "10:16":"10x16", "16:10":"16x10", "2:3":"2x3", "3:2":"3x2", "3:4":"3x4", "4:3":"4x3", "4:5":"4x5", "5:4":"5x4", "1:1":"1x1", } V3_RESOLUTIONS= [ "Auto", "512x1536", "576x1408", "576x1472", "576x1536", "640x1344", "640x1408", "640x1472", "640x1536", "704x1152", "704x1216", "704x1280", "704x1344", "704x1408", "704x1472", "736x1312", "768x1088", "768x1216", "768x1280", "768x1344", "800x1280", "832x960", "832x1024", "832x1088", "832x1152", "832x1216", "832x1248", "864x1152", "896x960", "896x1024", "896x1088", "896x1120", "896x1152", "960x832", "960x896", "960x1024", "960x1088", "1024x832", "1024x896", "1024x960", "1024x1024", "1088x768", "1088x832", "1088x896", "1088x960", "1120x896", "1152x704", "1152x832", "1152x864", "1152x896", "1216x704", "1216x768", "1216x832", "1248x832", "1280x704", "1280x768", "1280x800", "1312x736", "1344x640", "1344x704", "1344x768", "1408x576", "1408x640", "1408x704", "1472x576", "1472x640", "1472x704", "1536x512", "1536x576", "1536x640" ] def download_and_process_images(image_urls): """Helper function to download and process multiple images from URLs""" # Initialize list to store image tensors image_tensors = [] for image_url in image_urls: # Using functions from apinode_utils.py to handle downloading and processing image_bytesio = download_url_to_bytesio(image_url) # Download image content to BytesIO img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB") # Convert to torch.Tensor with RGB mode image_tensors.append(img_tensor) # Stack tensors to match (N, width, height, channels) if image_tensors: stacked_tensors = torch.cat(image_tensors, dim=0) else: raise Exception("No valid images were processed") return stacked_tensors def display_image_urls_on_node(image_urls, node_id): if node_id and image_urls: if len(image_urls) == 1: PromptServer.instance.send_progress_text( f"Generated Image URL:\n{image_urls[0]}", node_id ) else: urls_text = "Generated Image URLs:\n" + "\n".join( f"{i+1}. {url}" for i, url in enumerate(image_urls) ) PromptServer.instance.send_progress_text(urls_text, node_id) class IdeogramV1(ComfyNodeABC): """ Generates images using the Ideogram V1 model. """ def __init__(self): pass @classmethod def INPUT_TYPES(cls) -> InputTypeDict: return { "required": { "prompt": ( IO.STRING, { "multiline": True, "default": "", "tooltip": "Prompt for the image generation", }, ), "turbo": ( IO.BOOLEAN, { "default": False, "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)", } ), }, "optional": { "aspect_ratio": ( IO.COMBO, { "options": list(V1_V2_RATIO_MAP.keys()), "default": "1:1", "tooltip": "The aspect ratio for image generation.", }, ), "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, "control_after_generate": True, "display": "number", }, ), "negative_prompt": ( IO.STRING, { "multiline": True, "default": "", "tooltip": "Description of what to exclude from the image", }, ), "num_images": ( IO.INT, {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, ), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", "unique_id": "UNIQUE_ID", }, } RETURN_TYPES = (IO.IMAGE,) FUNCTION = "api_call" CATEGORY = "api node/image/Ideogram/v1" DESCRIPTION = cleandoc(__doc__ or "") API_NODE = True def api_call( self, prompt, turbo=False, aspect_ratio="1:1", magic_prompt_option="AUTO", seed=0, negative_prompt="", num_images=1, unique_id=None, **kwargs, ): # Determine the model based on turbo setting aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) model = "V_1_TURBO" if turbo else "V_1" 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, magic_prompt_option=( magic_prompt_option if magic_prompt_option != "AUTO" else None ), negative_prompt=negative_prompt if negative_prompt else None, ) ), auth_kwargs=kwargs, ) response = operation.execute() if not response.data or len(response.data) == 0: raise Exception("No images were generated in the response") image_urls = [image_data.url for image_data in response.data if image_data.url] if not image_urls: raise Exception("No image URLs were generated in the response") display_image_urls_on_node(image_urls, unique_id) return (download_and_process_images(image_urls),) class IdeogramV2(ComfyNodeABC): """ Generates images using the Ideogram V2 model. """ def __init__(self): pass @classmethod def INPUT_TYPES(cls) -> InputTypeDict: return { "required": { "prompt": ( IO.STRING, { "multiline": True, "default": "", "tooltip": "Prompt for the image generation", }, ), "turbo": ( IO.BOOLEAN, { "default": False, "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)", } ), }, "optional": { "aspect_ratio": ( IO.COMBO, { "options": list(V1_V2_RATIO_MAP.keys()), "default": "1:1", "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to AUTO.", }, ), "resolution": ( IO.COMBO, { "options": list(V1_V1_RES_MAP.keys()), "default": "Auto", "tooltip": "The resolution for image generation. If not set to AUTO, this overrides the aspect_ratio setting.", }, ), "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, "control_after_generate": True, "display": "number", }, ), "style_type": ( IO.COMBO, { "options": ["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"], "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", }, ), "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", # }, #), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", "unique_id": "UNIQUE_ID", }, } RETURN_TYPES = (IO.IMAGE,) FUNCTION = "api_call" CATEGORY = "api node/image/Ideogram/v2" DESCRIPTION = cleandoc(__doc__ or "") API_NODE = True def api_call( self, prompt, turbo=False, aspect_ratio="1:1", resolution="Auto", magic_prompt_option="AUTO", seed=0, style_type="NONE", negative_prompt="", num_images=1, color_palette="", unique_id=None, **kwargs, ): aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None) resolution = V1_V1_RES_MAP.get(resolution, None) # Determine the model based on turbo setting model = "V_2_TURBO" if turbo else "V_2" # Handle resolution vs aspect_ratio logic # If resolution is not AUTO, it overrides aspect_ratio final_resolution = None final_aspect_ratio = None if resolution != "AUTO": final_resolution = resolution else: final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None 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=final_aspect_ratio, resolution=final_resolution, 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=color_palette if color_palette else None, ) ), auth_kwargs=kwargs, ) response = operation.execute() if not response.data or len(response.data) == 0: raise Exception("No images were generated in the response") image_urls = [image_data.url for image_data in response.data if image_data.url] if not image_urls: raise Exception("No image URLs were generated in the response") display_image_urls_on_node(image_urls, unique_id) return (download_and_process_images(image_urls),) class IdeogramV3(ComfyNodeABC): """ Generates images using the Ideogram V3 model. Supports both regular image generation from text prompts and image editing with mask. """ def __init__(self): pass @classmethod def INPUT_TYPES(cls) -> InputTypeDict: return { "required": { "prompt": ( IO.STRING, { "multiline": True, "default": "", "tooltip": "Prompt for the image generation or editing", }, ), }, "optional": { "image": ( IO.IMAGE, { "default": None, "tooltip": "Optional reference image for image editing.", }, ), "mask": ( IO.MASK, { "default": None, "tooltip": "Optional mask for inpainting (white areas will be replaced)", }, ), "aspect_ratio": ( IO.COMBO, { "options": list(V3_RATIO_MAP.keys()), "default": "1:1", "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to Auto.", }, ), "resolution": ( IO.COMBO, { "options": V3_RESOLUTIONS, "default": "Auto", "tooltip": "The resolution for image generation. If not set to Auto, this overrides the aspect_ratio setting.", }, ), "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, "control_after_generate": True, "display": "number", }, ), "num_images": ( IO.INT, {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"}, ), "rendering_speed": ( IO.COMBO, { "options": ["BALANCED", "TURBO", "QUALITY"], "default": "BALANCED", "tooltip": "Controls the trade-off between generation speed and quality", }, ), }, "hidden": { "auth_token": "AUTH_TOKEN_COMFY_ORG", "comfy_api_key": "API_KEY_COMFY_ORG", "unique_id": "UNIQUE_ID", }, } RETURN_TYPES = (IO.IMAGE,) FUNCTION = "api_call" CATEGORY = "api node/image/Ideogram/v3" DESCRIPTION = cleandoc(__doc__ or "") API_NODE = True def api_call( self, prompt, image=None, mask=None, resolution="Auto", aspect_ratio="1:1", magic_prompt_option="AUTO", seed=0, num_images=1, rendering_speed="BALANCED", unique_id=None, **kwargs, ): # Check if both image and mask are provided for editing mode if image is not None and mask is not None: # Edit mode path = "/proxy/ideogram/ideogram-v3/edit" # Process image and mask input_tensor = image.squeeze().cpu() # Resize mask to match image dimension mask = resize_mask_to_image(mask, image, allow_gradient=False) # Invert mask, as Ideogram API will edit black areas instead of white areas (opposite of convention). mask = 1.0 - mask # Validate mask dimensions match image if mask.shape[1:] != image.shape[1:-1]: raise Exception("Mask and Image must be the same size") # Process image img_np = (input_tensor.numpy() * 255).astype(np.uint8) img = Image.fromarray(img_np) img_byte_arr = io.BytesIO() img.save(img_byte_arr, format="PNG") img_byte_arr.seek(0) img_binary = img_byte_arr img_binary.name = "image.png" # Process mask - white areas will be replaced mask_np = (mask.squeeze().cpu().numpy() * 255).astype(np.uint8) mask_img = Image.fromarray(mask_np) mask_byte_arr = io.BytesIO() mask_img.save(mask_byte_arr, format="PNG") mask_byte_arr.seek(0) mask_binary = mask_byte_arr mask_binary.name = "mask.png" # Create edit request edit_request = IdeogramV3EditRequest( prompt=prompt, rendering_speed=rendering_speed, ) # Add optional parameters if magic_prompt_option != "AUTO": edit_request.magic_prompt = magic_prompt_option if seed != 0: edit_request.seed = seed if num_images > 1: edit_request.num_images = num_images # Execute the operation for edit mode operation = SynchronousOperation( endpoint=ApiEndpoint( path=path, method=HttpMethod.POST, request_model=IdeogramV3EditRequest, response_model=IdeogramGenerateResponse, ), request=edit_request, files={ "image": img_binary, "mask": mask_binary, }, content_type="multipart/form-data", auth_kwargs=kwargs, ) elif image is not None or mask is not None: # If only one of image or mask is provided, raise an error raise Exception("Ideogram V3 image editing requires both an image AND a mask") else: # Generation mode path = "/proxy/ideogram/ideogram-v3/generate" # Create generation request gen_request = IdeogramV3Request( prompt=prompt, rendering_speed=rendering_speed, ) # Handle resolution vs aspect ratio if resolution != "Auto": gen_request.resolution = resolution elif aspect_ratio != "1:1": v3_aspect = V3_RATIO_MAP.get(aspect_ratio) if v3_aspect: gen_request.aspect_ratio = v3_aspect # Add optional parameters if magic_prompt_option != "AUTO": gen_request.magic_prompt = magic_prompt_option if seed != 0: gen_request.seed = seed if num_images > 1: gen_request.num_images = num_images # Execute the operation for generation mode operation = SynchronousOperation( endpoint=ApiEndpoint( path=path, method=HttpMethod.POST, request_model=IdeogramV3Request, response_model=IdeogramGenerateResponse, ), request=gen_request, auth_kwargs=kwargs, ) # Execute the operation and process response response = operation.execute() if not response.data or len(response.data) == 0: raise Exception("No images were generated in the response") image_urls = [image_data.url for image_data in response.data if image_data.url] if not image_urls: raise Exception("No image URLs were generated in the response") display_image_urls_on_node(image_urls, unique_id) return (download_and_process_images(image_urls),) NODE_CLASS_MAPPINGS = { "IdeogramV1": IdeogramV1, "IdeogramV2": IdeogramV2, "IdeogramV3": IdeogramV3, } NODE_DISPLAY_NAME_MAPPINGS = { "IdeogramV1": "Ideogram V1", "IdeogramV2": "Ideogram V2", "IdeogramV3": "Ideogram V3", }