261 lines
10 KiB
Python
261 lines
10 KiB
Python
import os
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import json
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import requests
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from PIL import Image
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import numpy as np
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import torch
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import io
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import base64
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from dotenv import load_dotenv
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import sys
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import pathlib
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from datetime import datetime
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# Import the base class and COMFYUI_AVAILABLE flag
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from ..core.base import WanAPIBase, COMFYUI_AVAILABLE
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# Try to import folder_paths if available
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try:
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import folder_paths
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except ImportError:
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pass
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class WanI2IGenerator(WanAPIBase):
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"""Node for image-to-image generation using Wan model"""
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# Define available Wan i2i models
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MODEL_OPTIONS = [
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"wan2.5-i2i-preview", # Preview Edition
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]
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# Define allowed sizes for Wan i2i models
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SIZE_OPTIONS = [
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"1024*1024", # 1:1 square (default)
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"1152*896", # 9:7 landscape
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"896*1152", # 7:9 portrait
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"1280*720", # 16:9 landscape
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"720*1280", # 9:16 portrait
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"1440*512", # Wide landscape
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"512*1440", # Tall portrait
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"768*768", # 1:1 square
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"1440*1440", # 1:1 square
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]
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# Define region options
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REGION_OPTIONS = [
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"international",
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"mainland_china"
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]
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def __init__(self):
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super().__init__()
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model": (cls.MODEL_OPTIONS, {
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"default": "wan2.5-i2i-preview"
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}),
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"image_url_1": ("STRING", {
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"default": "https://example.com/your_image1.png"
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}),
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"prompt": ("STRING", {
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"multiline": True,
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"default": "Edit the image with the desired changes"
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}),
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"region": (cls.REGION_OPTIONS, {
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"default": "international"
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})
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},
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"optional": {
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"image_url_2": ("STRING", {
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"default": ""
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}),
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"negative_prompt": ("STRING", {
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"multiline": True,
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"default": ""
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}),
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"size": (cls.SIZE_OPTIONS, {
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"default": "1024*1024"
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}),
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"watermark": ("BOOLEAN", {
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"default": False
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}),
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"seed": ("INT", {
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"default": 0,
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"min": 0,
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"max": 2147483647
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}),
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"num_images": ("INT", {
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"default": 1,
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"min": 1,
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"max": 4
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})
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}
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}
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RETURN_TYPES = ("IMAGE", "STRING") # Returns image tensor and image URL
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RETURN_NAMES = ("image", "image_url")
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FUNCTION = "generate"
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CATEGORY = "Ru4ls/Wan"
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def generate(self, model, image_url_1, prompt, region, image_url_2="", negative_prompt="", size="1024*1024",
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watermark=False, seed=0, num_images=1):
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# Check API key based on region
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api_key = self.check_api_key(region)
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# Get the appropriate API endpoints based on region
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endpoints = self.get_api_endpoints(region)
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api_url = endpoints["i2i_post"]
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# Prepare images array - at least one image is required
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images = [image_url_1.strip()]
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if image_url_2 and image_url_2.strip():
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images.append(image_url_2.strip())
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# Prepare API payload for image-to-image generation
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payload = {
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"model": model,
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"input": {
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"prompt": prompt,
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"images": images
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},
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"parameters": {
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"size": size,
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"watermark": watermark,
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"n": num_images
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}
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}
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# Add optional parameters if they have non-default values
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if negative_prompt:
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payload["input"]["negative_prompt"] = negative_prompt
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if seed > 0:
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payload["parameters"]["seed"] = seed
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# Set headers according to DashScope documentation
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json",
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"X-DashScope-Async": "enable" # Wan requires async processing
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}
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try:
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# Make API request
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print(f"Making API request to {api_url}")
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response = requests.post(api_url, headers=headers, json=payload)
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print(f"Response status code: {response.status_code}")
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if hasattr(response, 'text'):
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print(f"Response text: {response.text[:500]}...") # Print first 500 chars
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response.raise_for_status()
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# Parse response to get task_id
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result = response.json()
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print(f"API response received: {json.dumps(result, indent=2)[:200]}...") # Print first 200 chars
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# Check if this is a task creation response
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if "output" in result and "task_id" in result["output"]:
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task_id = result["output"]["task_id"]
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task_status = result["output"]["task_status"]
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print(f"Task created with ID: {task_id}, status: {task_status}")
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# Now we need to poll for the result
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task_result = self.poll_task_result(task_id, region)
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return task_result # Return both image tensor and image URL
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else:
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raise ValueError(f"Unexpected API response format: {result}")
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except requests.exceptions.RequestException as e:
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# More detailed error handling
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if hasattr(e, 'response') and e.response is not None:
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status_code = e.response.status_code
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response_text = e.response.text
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print(f"API request failed with status {status_code}: {response_text}")
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if status_code == 401:
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raise RuntimeError(f"API request failed: 401 Unauthorized. "
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f"This usually means your API key is invalid or not properly configured. "
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f"Error details: {response_text}")
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elif status_code == 403:
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raise RuntimeError(f"API request failed: 403 Forbidden. "
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f"This usually means your API key is valid but you don't have access to this model. "
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f"Error details: {response_text}")
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elif status_code == 400:
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raise RuntimeError(f"API request failed: 400 Bad Request. "
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f"This usually means there's an issue with the request format. "
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f"Error details: {response_text}")
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else:
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raise RuntimeError(f"API request failed: {status_code} {e.response.reason}. Response: {response_text}")
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else:
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raise RuntimeError(f"API request failed: {str(e)}")
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except Exception as e:
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raise RuntimeError(f"Failed to process API response: {str(e)}")
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def poll_task_result(self, task_id, region):
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"""Poll for task result until completion"""
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import time
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# Get the appropriate API endpoints based on region
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endpoints = self.get_api_endpoints(region)
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query_url = endpoints["get"].format(task_id=task_id)
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# Check API key based on region
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api_key = self.check_api_key(region)
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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max_attempts = 30 # Maximum polling attempts
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attempt = 0
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while attempt < max_attempts:
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try:
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print(f"Polling task {task_id}, attempt {attempt + 1}/{max_attempts}")
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response = requests.get(query_url, headers=headers)
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response.raise_for_status()
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result = response.json()
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task_status = result["output"]["task_status"]
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print(f"Task status: {task_status}")
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if task_status == "SUCCEEDED":
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# Task completed successfully
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results = result["output"]["results"]
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if len(results) > 0 and "url" in results[0]:
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image_url = results[0]["url"]
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# Download the generated image
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image_response = requests.get(image_url)
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image_response.raise_for_status()
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# Convert to tensor
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image = Image.open(io.BytesIO(image_response.content))
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image_tensor = torch.from_numpy(np.array(image).astype(np.float32) / 255.0)
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image_tensor = image_tensor.unsqueeze(0) # Add batch dimension
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# Return both the image tensor and the image URL
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return (image_tensor, image_url)
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else:
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raise ValueError(f"Unexpected API response format: {result}")
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elif task_status == "FAILED":
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# Task failed
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error_code = result["output"].get("code", "Unknown")
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error_message = result["output"].get("message", "Unknown error")
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raise RuntimeError(f"Task failed with code: {error_code}, message: {error_message}")
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elif task_status in ["PENDING", "RUNNING"]:
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# Task still in progress, wait and retry
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time.sleep(5) # Wait 5 seconds before retrying
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attempt += 1
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continue
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else:
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raise ValueError(f"Unexpected task status: {task_status}")
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except requests.exceptions.RequestException as e:
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raise RuntimeError(f"Failed to query task status: {str(e)}")
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# If we've reached here, we've exceeded max attempts
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raise RuntimeError(f"Task did not complete within the expected time ({max_attempts} attempts)") |