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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",
}
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