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https://git.datalinker.icu/comfyanonymous/ComfyUI
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Add staging api.
This commit is contained in:
Robin Huang
parent
6f5caa0879
commit
7290683905
@ -1,5 +1,10 @@
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# Add API base URL at the top of the file
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API_BASE = "https://stagingapi.comfy.org"
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from inspect import cleandoc
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class IdeogramTextToImage:
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from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, IO
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class IdeogramTextToImage(ComfyNodeABC):
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"""
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Generates images synchronously based on a given prompt and optional parameters.
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@ -9,7 +14,7 @@ class IdeogramTextToImage:
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pass
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@classmethod
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def INPUT_TYPES(s):
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def INPUT_TYPES(cls) -> InputTypeDict:
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"""
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Return a dictionary which contains config for all input fields.
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Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
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@ -26,58 +31,61 @@ class IdeogramTextToImage:
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"""
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return {
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"required": {
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"prompt": ("STRING", {"multiline": True,
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"default": "", "tooltip": "Prompt for the image generation"}),
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"model": (["V_2", "V_2_TURBO", "V_1", "V_1_TURBO"], {"default": "V_2"}),
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"prompt": (IO.STRING, {
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"multiline": True,
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"default": "",
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"tooltip": "Prompt for the image generation",
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}),
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"model": (IO.COMBO, { "options": ["V_2", "V_2_TURBO", "V_1", "V_1_TURBO"], "default": "V_2", "tooltip": "Model to use for image generation"}),
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},
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"optional": {
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"aspect_ratio": (["ASPECT_1_1", "ASPECT_4_3", "ASPECT_3_4", "ASPECT_16_9", "ASPECT_9_16",
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"ASPECT_2_1", "ASPECT_1_2", "ASPECT_3_2", "ASPECT_2_3", "ASPECT_4_5", "ASPECT_5_4"], {
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"default": "ASPECT_1_1",
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"tooltip": "The aspect ratio for image generation. Cannot be used with resolution"
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"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"
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}),
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"resolution": (["1024x1024", "1024x1792", "1792x1024"], {
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"resolution": (IO.COMBO, { "options": ["1024x1024", "1024x1792", "1792x1024"],
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"default": "1024x1024",
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"tooltip": "The resolution for image generation (V2 only). Cannot be used with aspect_ratio"
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}),
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"magic_prompt_option": (["AUTO", "ON", "OFF"], {
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"magic_prompt_option": (IO.COMBO, { "options": ["AUTO", "ON", "OFF"],
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"default": "AUTO",
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"tooltip": "Determine if MagicPrompt should be used in generation"
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}),
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"seed": ("INT", {
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"seed": (IO.INT, {
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"default": 0,
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"min": 0,
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"max": 2147483647,
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"step": 1,
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"display": "number"
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}),
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"style_type": (["NONE", "ANIME", "CINEMATIC", "CREATIVE", "DIGITAL_ART", "PHOTOGRAPHIC"], {
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"style_type": (IO.COMBO, { "options": ["NONE", "ANIME", "CINEMATIC", "CREATIVE", "DIGITAL_ART", "PHOTOGRAPHIC"],
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"default": "NONE",
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"tooltip": "Style type for generation (V2+ only)"
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}),
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"negative_prompt": ("STRING", {
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"negative_prompt": (IO.STRING, {
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"multiline": True,
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"default": "",
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"tooltip": "Description of what to exclude from the image (V1/V2 only)"
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}),
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"num_images": ("INT", {
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"num_images": (IO.INT, {
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"default": 1,
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"min": 1,
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"max": 8,
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"step": 1,
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"display": "number"
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}),
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"color_palette": ("STRING", {
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"color_palette": (IO.STRING, {
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"multiline": False,
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"default": "",
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"tooltip": "Color palette preset name or hex colors with weights (V2/V2_TURBO only)"
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}),
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG"
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_TYPES = (IO.IMAGE,)
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#RETURN_NAMES = ("image_output_name",)
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DESCRIPTION = cleandoc(__doc__)
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DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
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FUNCTION = "api_call"
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#OUTPUT_NODE = False
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@ -85,15 +93,14 @@ class IdeogramTextToImage:
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CATEGORY = "Example"
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def api_call(self, prompt, model, aspect_ratio=None, resolution=None,
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magic_prompt_option="AUTO", seed=0, style_type="NONE",
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negative_prompt="", num_images=1, color_palette=""):
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def api_call(self, prompt, model, aspect_ratio=None, resolution=None,
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magic_prompt_option="AUTO", seed=0, style_type="NONE",
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negative_prompt="", num_images=1, color_palette="", auth_token=None):
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import requests
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import torch
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from PIL import Image
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import io
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import numpy as np
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import time
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# Build payload with all available parameters
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payload = {
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@ -107,12 +114,12 @@ class IdeogramTextToImage:
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# Make API request
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headers = {
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"Authorization": "Bearer TBD", # TODO(robin): add authorization key
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"Authorization": f"Bearer {auth_token}",
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"Content-Type": "application/json"
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}
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response = requests.post(
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"http://localhost:8080/proxy/ideogram/generate",
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f"{API_BASE}/proxy/ideogram/generate",
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headers=headers,
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json=payload
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)
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@ -122,30 +129,22 @@ class IdeogramTextToImage:
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# Parse response
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response_data = response.json()
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# Get the image URL from the response
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image_url = response_data["data"][0]["url"]
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# Time the image download
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download_start = time.time()
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img_response = requests.get(image_url)
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if img_response.status_code != 200:
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raise Exception("Failed to download the image")
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download_time = (time.time() - download_start) * 1000 # Convert to milliseconds
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print(f"Image download time: {download_time:.2f}ms")
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# Time the conversion process
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conversion_start = time.time()
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img = Image.open(io.BytesIO(img_response.content))
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img = img.convert("RGB") # Ensure RGB format
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# Convert to numpy array, normalize to float32 between 0 and 1
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img_array = np.array(img).astype(np.float32) / 255.0
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# Convert to torch tensor and add batch dimension
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img_tensor = torch.from_numpy(img_array)[None,]
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conversion_time = (time.time() - conversion_start) * 1000 # Convert to milliseconds
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print(f"Image conversion time: {conversion_time:.2f}ms")
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return (img_tensor,)
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@ -208,7 +207,10 @@ class RunwayVideoNode:
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"default": False,
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"tooltip": "Whether to include watermark in the output"
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}),
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}
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},
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"hidden": {
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"auth_token": "AUTH_TOKEN_COMFY_ORG"
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},
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}
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RETURN_TYPES = ("VIDEO",)
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@ -216,15 +218,9 @@ class RunwayVideoNode:
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FUNCTION = "generate_video"
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CATEGORY = "video"
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def generate_video(self, prompt_image, prompt_text, seed=0, model="gen3a_turbo",
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duration=5.0, ratio="1280:768", watermark=False):
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def generate_video(self, prompt_image, prompt_text, seed=0, model="gen3a_turbo",
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duration=5.0, ratio="1280:768", watermark=False, auth_token=None):
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import requests
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import torch
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import time
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import os
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# Hardcoded API key (temporary solution)
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api_key = "key_e861661aa0b307e07e8cc269c1f42cf56fcce876ed6511a507e185ee51f695291da21f4777be1326b4467c34be5a6498b72dc27c9780e483250c692aa410d4c6" # Replace with actual API key
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# Convert torch tensor image to URL (you'll need to implement this part)
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# This is a placeholder - you'll need to either save the image temporarily
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@ -244,27 +240,22 @@ class RunwayVideoNode:
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# Make API request
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Authorization": f"Bearer {auth_token}",
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"Content-Type": "application/json",
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"X-Runway-Version": "2024-11-06"
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}
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# Time the API request
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api_start = time.time()
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response = requests.post(
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"https://api.dev.runwayml.com/v1/image_to_video",
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f"{API_BASE}/proxy/runway/image_to_video",
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headers=headers,
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json=payload
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)
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api_time = (time.time() - api_start) * 1000 # Convert to milliseconds
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print(f"API request time: {api_time:.2f}ms")
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if response.status_code != 200:
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raise Exception(f"API request failed: {response.text}")
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# Parse response
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response_data = response.json()
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# response_data = response.json()
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# Note: You'll need to implement the actual video handling here
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# This is a placeholder return
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return (None,)
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