diff --git a/modelscope_image_node.py b/modelscope_image_node.py index 0e01f8a..45d359e 100644 --- a/modelscope_image_node.py +++ b/modelscope_image_node.py @@ -33,41 +33,55 @@ def save_config(config: dict) -> bool: print(f"保存配置失败: {e}") return False -def save_api_token(token): - token_path = os.path.join(os.path.dirname(__file__), '.qwen_token') +def save_api_tokens(tokens): + """保存多个API Token""" + tokens_path = os.path.join(os.path.dirname(__file__), '.qwen_tokens') try: - with open(token_path, 'w', encoding='utf-8') as f: - f.write(token) + with open(tokens_path, 'w', encoding='utf-8') as f: + f.write('\n'.join(tokens)) # 每个token一行 except Exception as e: - print(f"保存token失败(.qwen_token): {e}") + print(f"保存tokens失败(.qwen_tokens): {e}") + try: cfg = load_config() - cfg["api_token"] = token + cfg["api_tokens"] = tokens if save_config(cfg): return True return False except Exception as e: - print(f"保存token失败(config.json): {e}") + print(f"保存tokens失败(config.json): {e}") return False -def load_api_token(): - token_path = os.path.join(os.path.dirname(__file__), '.qwen_token') +def load_api_tokens(): + """加载多个API Token""" + tokens_path = os.path.join(os.path.dirname(__file__), '.qwen_tokens') try: cfg = load_config() - token_from_cfg = cfg.get("api_token", "").strip() - if token_from_cfg: - return token_from_cfg + tokens_from_cfg = cfg.get("api_tokens", []) + if tokens_from_cfg and isinstance(tokens_from_cfg, list): + return [token.strip() for token in tokens_from_cfg if token.strip()] except Exception as e: - print(f"读取config.json中的token失败: {e}") + print(f"读取config.json中的tokens失败: {e}") + try: - if os.path.exists(token_path): - with open(token_path, 'r', encoding='utf-8') as f: - token = f.read().strip() - return token if token else "" - return "" + if os.path.exists(tokens_path): + with open(tokens_path, 'r', encoding='utf-8') as f: + tokens = [line.strip() for line in f.read().split('\n') if line.strip()] + return tokens if tokens else [] + return [] except Exception as e: - print(f"加载token失败: {e}") - return "" + print(f"加载tokens失败: {e}") + return [] + +def parse_api_tokens(token_input): + """解析输入的API Tokens（支持逗号、分号、换行分隔）""" + if not token_input or token_input.strip() in ["", "***已保存***"]: + return load_api_tokens() + + # 支持多种分隔符 + import re + tokens = re.split(r'[,;\n]+', token_input) + return [token.strip() for token in tokens if token.strip()] def tensor_to_base64_url(image_tensor): try: @@ -99,17 +113,17 @@ class ModelScopeImageNode: @classmethod def INPUT_TYPES(cls): config = load_config() - saved_token = load_api_token() + saved_tokens = load_api_tokens() return { "required": { "prompt": ("STRING", { "multiline": True, "default": config.get("default_prompt", "A beautiful landscape") }), - "api_token": ("STRING", { - "default": "***已保存***" if saved_token else "", - "placeholder": "请输入您的魔搭API Token" if not saved_token else "留空使用已保存的Token", - "multiline": False + "api_tokens": ("STRING", { + "default": "***已保存{}个Token***".format(len(saved_tokens)) if saved_tokens else "", + "placeholder": "请输入API Token（支持多个，用逗号/换行分隔）" if not saved_tokens else "留空使用已保存的Token", + "multiline": True }), }, "optional": { @@ -156,121 +170,132 @@ class ModelScopeImageNode: FUNCTION = "generate_image" CATEGORY = "ModelScopeAPI" - def generate_image(self, prompt, api_token, model="Qwen/Qwen-Image", negative_prompt="", width=512, height=512, seed=-1, steps=30, guidance=7.5): + def generate_image(self, prompt, api_tokens, model="Qwen/Qwen-Image", negative_prompt="", width=512, height=512, seed=-1, steps=30, guidance=7.5): config = load_config() - if not api_token or api_token.strip() == "" or api_token.strip() == "***已保存***": - api_token = load_api_token() - if not api_token or api_token.strip() == "": - raise Exception("请输入有效的API Token或确保已保存token") - saved_token = load_api_token() - if api_token != saved_token and api_token.strip() != "***已保存***": - if save_api_token(api_token): - print("✅ API Token已自动保存") + tokens = parse_api_tokens(api_tokens) + + if not tokens: + raise Exception("请提供至少一个有效的API Token") + + # 保存Token（如果提供了新的） + if api_tokens and api_tokens.strip() not in ["", "***已保存{}个Token***".format(len(load_api_tokens()))]: + if save_api_tokens(tokens): + print(f"✅ 已保存 {len(tokens)} 个API Token") else: print("⚠️ API Token保存失败，但不影响当前使用") - elif api_token.strip() == "***已保存***": - api_token = saved_token - try: - url = 'https://api-inference.modelscope.cn/v1/images/generations' - payload = { - 'model': model, - 'prompt': prompt, - 'size': f"{width}x{height}", - 'steps': steps, - 'guidance': guidance - } - if negative_prompt.strip(): - payload['negative_prompt'] = negative_prompt - print(f"🚫 负向提示词: {negative_prompt}") - if seed != -1: - payload['seed'] = seed - print(f"🎯 使用指定种子: {seed}") - else: - import random - random_seed = random.randint(0, 2147483647) - payload['seed'] = random_seed - print(f"🎲 使用随机种子: {random_seed}") - print(f"📐 图像尺寸: {width}x{height}") - print(f"🔧 采样步数: {steps}") - print(f"🎨 引导系数: {guidance}") - headers = { - 'Authorization': f'Bearer {api_token}', - 'Content-Type': 'application/json', - 'X-ModelScope-Async-Mode': 'true' - } - submission_response = requests.post( - url, - data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), - headers=headers, - timeout=config.get("timeout", 60) - ) - if submission_response.status_code == 400: - print("⚠️ 提交失败，尝试使用最小参数重试...") - minimal_payload = { + + # 轮询尝试每个Token + last_exception = None + for i, token in enumerate(tokens): + try: + print(f"🔄 尝试使用第 {i+1} 个API Token...") + url = 'https://api-inference.modelscope.cn/v1/images/generations' + payload = { 'model': model, - 'prompt': prompt + 'prompt': prompt, + 'size': f"{width}x{height}", + 'steps': steps, + 'guidance': guidance } + if negative_prompt.strip(): + payload['negative_prompt'] = negative_prompt + if seed != -1: + payload['seed'] = seed + else: + import random + random_seed = random.randint(0, 2147483647) + payload['seed'] = random_seed + + headers = { + 'Authorization': f'Bearer {token}', + 'Content-Type': 'application/json', + 'X-ModelScope-Async-Mode': 'true' + } + submission_response = requests.post( - url, - data=json.dumps(minimal_payload, ensure_ascii=False).encode('utf-8'), + url, + data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), headers=headers, timeout=config.get("timeout", 60) ) - if submission_response.status_code != 200: - raise Exception(f"API请求失败: {submission_response.status_code}, {submission_response.text}") - submission_json = submission_response.json() - image_url = None - if 'task_id' in submission_json: - task_id = submission_json['task_id'] - print(f"🕒 已提交任务，任务ID: {task_id}，开始轮询...") - poll_start = time.time() - max_wait_seconds = max(60, config.get('timeout', 720)) - while True: - task_resp = requests.get( - f"https://api-inference.modelscope.cn/v1/tasks/{task_id}", - headers={ - 'Authorization': f'Bearer {api_token}', - 'X-ModelScope-Task-Type': 'image_generation' - }, - timeout=config.get("image_download_timeout", 120) + + if submission_response.status_code == 400: + # 尝试使用最小参数重试 + minimal_payload = { + 'model': model, + 'prompt': prompt + } + submission_response = requests.post( + url, + data=json.dumps(minimal_payload, ensure_ascii=False).encode('utf-8'), + headers=headers, + timeout=config.get("timeout", 60) ) - if task_resp.status_code != 200: - raise Exception(f"任务查询失败: {task_resp.status_code}, {task_resp.text}") - task_data = task_resp.json() - status = task_data.get('task_status') - if status == 'SUCCEED': - output_images = task_data.get('output_images') or [] - if not output_images: - raise Exception("任务成功但未返回图片URL") - image_url = output_images[0] - print("✅ 任务完成，开始下载图片...") - break - if status == 'FAILED': - raise Exception(f"任务失败: {task_data}") - if time.time() - poll_start > max_wait_seconds: - raise Exception("任务轮询超时，请稍后重试或降低并发") - time.sleep(5) - elif 'images' in submission_json and len(submission_json['images']) > 0: - image_url = submission_json['images'][0]['url'] - print(f"⬇️ 下载生成的图片...") - else: - raise Exception(f"未识别的API返回格式: {submission_json}") - img_response = requests.get(image_url, timeout=config.get("image_download_timeout", 30)) - if img_response.status_code != 200: - raise Exception(f"图片下载失败: {img_response.status_code}") - pil_image = Image.open(BytesIO(img_response.content)) - if pil_image.mode != 'RGB': - pil_image = pil_image.convert('RGB') - image_np = np.array(pil_image).astype(np.float32) / 255.0 - image_tensor = torch.from_numpy(image_np)[None,] - print(f"🎉 图片处理完成！") - return (image_tensor,) - except Exception as e: - print(f"Qwen-Image API调用失败: {str(e)}") - error_image = Image.new('RGB', (width, height), color='red') - error_np = np.array(error_image).astype(np.float32) / 255.0 - error_tensor = torch.from_numpy(error_np)[None,] - return (error_tensor,) + + if submission_response.status_code != 200: + raise Exception(f"API请求失败: {submission_response.status_code}, {submission_response.text}") + + submission_json = submission_response.json() + image_url = None + if 'task_id' in submission_json: + task_id = submission_json['task_id'] + print(f"🕒 已提交任务，任务ID: {task_id}，开始轮询...") + poll_start = time.time() + max_wait_seconds = max(60, config.get('timeout', 720)) + while True: + task_resp = requests.get( + f"https://api-inference.modelscope.cn/v1/tasks/{task_id}", + headers={ + 'Authorization': f'Bearer {token}', + 'X-ModelScope-Task-Type': 'image_generation' + }, + timeout=config.get("image_download_timeout", 120) + ) + if task_resp.status_code != 200: + raise Exception(f"任务查询失败: {task_resp.status_code}, {task_resp.text}") + task_data = task_resp.json() + status = task_data.get('task_status') + if status == 'SUCCEED': + output_images = task_data.get('output_images') or [] + if not output_images: + raise Exception("任务成功但未返回图片URL") + image_url = output_images[0] + print("✅ 任务完成，开始下载图片...") + break + if status == 'FAILED': + raise Exception(f"任务失败: {task_data}") + if time.time() - poll_start > max_wait_seconds: + raise Exception("任务轮询超时，请稍后重试或降低并发") + time.sleep(5) + elif 'images' in submission_json and len(submission_json['images']) > 0: + image_url = submission_json['images'][0]['url'] + print(f"⬇️ 下载生成的图片...") + else: + raise Exception(f"未识别的API返回格式: {submission_json}") + + img_response = requests.get(image_url, timeout=config.get("image_download_timeout", 30)) + if img_response.status_code != 200: + raise Exception(f"图片下载失败: {img_response.status_code}") + + pil_image = Image.open(BytesIO(img_response.content)) + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + image_np = np.array(pil_image).astype(np.float32) / 255.0 + image_tensor = torch.from_numpy(image_np)[None,] + print(f"🎉 图片处理完成！使用的第 {i+1} 个API Token") + return (image_tensor,) + + except Exception as e: + last_exception = e + print(f"⚠️ 第 {i+1} 个API Token失败: {str(e)}") + if i < len(tokens) - 1: # 不是最后一个Token + print(f"➡️ 尝试下一个API Token...") + continue + else: + break # 所有Token都失败了 + + # 所有Token都失败 + raise Exception(f"所有 {len(tokens)} 个API Token都失败了。最后的错误: {str(last_exception)}") class ModelScopeImageEditNode: @@ -280,7 +305,7 @@ class ModelScopeImageEditNode: @classmethod def INPUT_TYPES(cls): config = load_config() - saved_token = load_api_token() + saved_tokens = load_api_tokens() # 获取模型列表 edit_models = config.get("image_edit_models", ["Qwen/Qwen-Image-Edit"]) @@ -293,10 +318,10 @@ class ModelScopeImageEditNode: "multiline": True, "default": "修改图片中的内容" }), - "api_token": ("STRING", { - "default": "***已保存***" if saved_token else "", - "placeholder": "请输入您的魔搭API Token" if not saved_token else "留空使用已保存的Token", - "multiline": False + "api_tokens": ("STRING", { + "default": "***已保存{}个Token***".format(len(saved_tokens)) if saved_tokens else "", + "placeholder": "请输入API Token（支持多个，用逗号/换行分隔）" if not saved_tokens else "留空使用已保存的Token", + "multiline": True }), "image_gen_mode": ("BOOLEAN", { "default": False, @@ -352,200 +377,210 @@ class ModelScopeImageEditNode: FUNCTION = "edit_image" CATEGORY = "ModelScopeAPI" - def edit_image(self, image, prompt, api_token, image_gen_mode=False, gen_model="Qwen/Qwen-Image", + def edit_image(self, image, prompt, api_tokens, image_gen_mode=False, gen_model="Qwen/Qwen-Image", edit_model="Qwen/Qwen-Image-Edit", negative_prompt="", width=512, height=512, steps=30, guidance=3.5, seed=-1): config = load_config() - if not api_token or api_token.strip() == "" or api_token.strip() == "***已保存***": - api_token = load_api_token() - if not api_token or api_token.strip() == "": - raise Exception("请输入有效的API Token或确保已保存token") - saved_token = load_api_token() - if api_token != saved_token and api_token.strip() != "***已保存***": - if save_api_token(api_token): - print("✅ API Token已自动保存") + tokens = parse_api_tokens(api_tokens) + + if not tokens: + raise Exception("请提供至少一个有效的API Token") + + # 保存Token（如果提供了新的） + if api_tokens and api_tokens.strip() not in ["", "***已保存{}个Token***".format(len(load_api_tokens()))]: + if save_api_tokens(tokens): + print(f"✅ 已保存 {len(tokens)} 个API Token") else: print("⚠️ API Token保存失败，但不影响当前使用") - elif api_token.strip() == "***已保存***": - api_token = saved_token - try: - # 根据开关选择使用的模型 - if image_gen_mode: - model = gen_model - mode_name = "图生图" - mode_type = "image_generation" - else: - model = edit_model - mode_name = "图像编辑" - mode_type = "image_edit" + + # 根据开关选择使用的模型 + if image_gen_mode: + model = gen_model + mode_name = "图生图" + else: + model = edit_model + mode_name = "图像编辑" - # 将图像转换为临时文件并上传获取URL - temp_img_path = None - image_url = None + # 轮询尝试每个Token + last_exception = None + for i, token in enumerate(tokens): try: - # 保存图像到临时文件 - temp_img_path = os.path.join(tempfile.gettempdir(), f"qwen_edit_temp_{int(time.time())}.jpg") - if len(image.shape) == 4: - img = image[0] - else: - img = image + print(f"🔄 尝试使用第 {i+1} 个API Token...") - i = 255. * img.cpu().numpy() - img_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - img_pil.save(temp_img_path) - print(f"✅ 图像已保存到临时文件: {temp_img_path}") - - # 上传图像到kefan.cn获取URL - upload_url = 'https://ai.kefan.cn/api/upload/local' - with open(temp_img_path, 'rb') as img_file: - files = {'file': img_file} - upload_response = requests.post( - upload_url, - files=files, - timeout=30 - ) - if upload_response.status_code == 200: - upload_data = upload_response.json() - # 修复这里的判断逻辑，kefan.cn返回code=200表示成功 - if upload_data.get('success') == True and 'data' in upload_data: - image_url = upload_data['data'] - print(f"✅ 图像已上传成功，获取URL: {image_url}") - else: - print(f"⚠️ 图像上传返回错误: {upload_response.text}") - else: - print(f"⚠️ 图像上传失败: {upload_response.status_code}, {upload_response.text}") - except Exception as e: - print(f"⚠️ 图像上传异常: {str(e)}") - - # 如果上传失败，回退到base64 - if not image_url: - print("⚠️ 图像URL获取失败，回退到使用base64") - image_data = tensor_to_base64_url(image) - payload = { - 'model': model, - 'prompt': prompt, - 'image': image_data - } - else: - payload = { - 'model': model, - 'prompt': prompt, - 'image_url': image_url - } - - if negative_prompt.strip(): - payload['negative_prompt'] = negative_prompt - print(f"🚫 负向提示词: {negative_prompt}") - - # 添加新参数 - if width != 512 or height != 512: - size = f"{width}x{height}" - payload['size'] = size - print(f"📏 图像尺寸: {size}") - - if steps != 30: - payload['steps'] = steps - print(f"🔄 采样步数: {steps}") - - if guidance != 3.5: - payload['guidance'] = guidance - print(f"🧭 引导系数: {guidance}") - - if seed != -1: - payload['seed'] = seed - print(f"🎲 随机种子: {seed}") - - headers = { - 'Authorization': f'Bearer {api_token}', - 'Content-Type': 'application/json', - 'X-ModelScope-Async-Mode': 'true' - } - - print(f"🖼️ 开始{mode_name}...") - print(f"✏️ 编辑提示: {prompt}") - print(f"🧠 使用模型: {model}") - - url = 'https://api-inference.modelscope.cn/v1/images/generations' - submission_response = requests.post( - url, - data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), - headers=headers, - timeout=config.get("timeout", 60) - ) - - if submission_response.status_code != 200: - raise Exception(f"API请求失败: {submission_response.status_code}, {submission_response.text}") - - submission_json = submission_response.json() - result_image_url = None - - if 'task_id' in submission_json: - task_id = submission_json['task_id'] - print(f"🕒 已提交任务，任务ID: {task_id}，开始轮询...") - poll_start = time.time() - max_wait_seconds = max(60, config.get('timeout', 720)) - - while True: - task_resp = requests.get( - f"https://api-inference.modelscope.cn/v1/tasks/{task_id}", - headers={ - 'Authorization': f'Bearer {api_token}', - 'X-ModelScope-Task-Type': 'image_generation' - }, - timeout=config.get("image_download_timeout", 120) - ) - - if task_resp.status_code != 200: - raise Exception(f"任务查询失败: {task_resp.status_code}, {task_resp.text}") - - task_data = task_resp.json() - status = task_data.get('task_status') - - if status == 'SUCCEED': - output_images = task_data.get('output_images') or [] - if not output_images: - raise Exception("任务成功但未返回图片URL") - result_image_url = output_images[0] - print("✅ 任务完成，开始下载编辑后的图片...") - break - - if status == 'FAILED': - error_message = task_data.get('errors', {}).get('message', '未知错误') - error_code = task_data.get('errors', {}).get('code', '未知错误码') - raise Exception(f"任务失败: 错误码 {error_code}, 错误信息: {error_message}") - - if time.time() - poll_start > max_wait_seconds: - raise Exception("任务轮询超时，请稍后重试或降低并发") - - time.sleep(5) - else: - raise Exception(f"未识别的API返回格式: {submission_json}") - - img_response = requests.get(result_image_url, timeout=config.get("image_download_timeout", 30)) - if img_response.status_code != 200: - raise Exception(f"图片下载失败: {img_response.status_code}") - - pil_image = Image.open(BytesIO(img_response.content)) - if pil_image.mode != 'RGB': - pil_image = pil_image.convert('RGB') - - image_np = np.array(pil_image).astype(np.float32) / 255.0 - image_tensor = torch.from_numpy(image_np)[None,] - - # 清理临时文件 - if temp_img_path and os.path.exists(temp_img_path): + # 将图像转换为临时文件并上传获取URL + temp_img_path = None + image_url = None try: - os.remove(temp_img_path) - except: - pass - - print(f"🎉 {mode_name}完成！") - return (image_tensor,) - - except Exception as e: - print(f"Qwen-Image-Edit API调用失败: {str(e)}") - # 返回原图像作为错误回退 - return (image.unsqueeze(0),) + # 保存图像到临时文件 + temp_img_path = os.path.join(tempfile.gettempdir(), f"qwen_edit_temp_{int(time.time())}.jpg") + if len(image.shape) == 4: + img = image[0] + else: + img = image + + i = 255. * img.cpu().numpy() + img_pil = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + img_pil.save(temp_img_path) + print(f"✅ 图像已保存到临时文件: {temp_img_path}") + + # 上传图像到kefan.cn获取URL + upload_url = 'https://ai.kefan.cn/api/upload/local' + with open(temp_img_path, 'rb') as img_file: + files = {'file': img_file} + upload_response = requests.post( + upload_url, + files=files, + timeout=30 + ) + if upload_response.status_code == 200: + upload_data = upload_response.json() + if upload_data.get('success') == True and 'data' in upload_data: + image_url = upload_data['data'] + print(f"✅ 图像已上传成功，获取URL: {image_url}") + else: + print(f"⚠️ 图像上传返回错误: {upload_response.text}") + else: + print(f"⚠️ 图像上传失败: {upload_response.status_code}, {upload_response.text}") + except Exception as e: + print(f"⚠️ 图像上传异常: {str(e)}") + + # 如果上传失败，回退到base64 + if not image_url: + print("⚠️ 图像URL获取失败，回退到使用base64") + image_data = tensor_to_base64_url(image) + payload = { + 'model': model, + 'prompt': prompt, + 'image': image_data + } + else: + payload = { + 'model': model, + 'prompt': prompt, + 'image_url': image_url + } + + if negative_prompt.strip(): + payload['negative_prompt'] = negative_prompt + + # 添加新参数 + if width != 512 or height != 512: + size = f"{width}x{height}" + payload['size'] = size + + if steps != 30: + payload['steps'] = steps + + if guidance != 3.5: + payload['guidance'] = guidance + + if seed != -1: + payload['seed'] = seed + + headers = { + 'Authorization': f'Bearer {token}', + 'Content-Type': 'application/json', + 'X-ModelScope-Async-Mode': 'true' + } + + print(f"🖼️ 开始{mode_name}...") + print(f"✏️ 编辑提示: {prompt}") + print(f"🧠 使用模型: {model}") + + url = 'https://api-inference.modelscope.cn/v1/images/generations' + submission_response = requests.post( + url, + data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), + headers=headers, + timeout=config.get("timeout", 60) + ) + + if submission_response.status_code != 200: + raise Exception(f"API请求失败: {submission_response.status_code}, {submission_response.text}") + + submission_json = submission_response.json() + result_image_url = None + + if 'task_id' in submission_json: + task_id = submission_json['task_id'] + print(f"🕒 已提交任务，任务ID: {task_id}，开始轮询...") + poll_start = time.time() + max_wait_seconds = max(60, config.get('timeout', 720)) + + while True: + task_resp = requests.get( + f"https://api-inference.modelscope.cn/v1/tasks/{task_id}", + headers={ + 'Authorization': f'Bearer {token}', + 'X-ModelScope-Task-Type': 'image_generation' + }, + timeout=config.get("image_download_timeout", 120) + ) + + if task_resp.status_code != 200: + raise Exception(f"任务查询失败: {task_resp.status_code}, {task_resp.text}") + + task_data = task_resp.json() + status = task_data.get('task_status') + + if status == 'SUCCEED': + output_images = task_data.get('output_images') or [] + if not output_images: + raise Exception("任务成功但未返回图片URL") + result_image_url = output_images[0] + print("✅ 任务完成，开始下载编辑后的图片...") + break + + if status == 'FAILED': + error_message = task_data.get('errors', {}).get('message', '未知错误') + error_code = task_data.get('errors', {}).get('code', '未知错误码') + raise Exception(f"任务失败: 错误码 {error_code}, 错误信息: {error_message}") + + if time.time() - poll_start > max_wait_seconds: + raise Exception("任务轮询超时，请稍后重试或降低并发") + + time.sleep(5) + else: + raise Exception(f"未识别的API返回格式: {submission_json}") + + img_response = requests.get(result_image_url, timeout=config.get("image_download_timeout", 30)) + if img_response.status_code != 200: + raise Exception(f"图片下载失败: {img_response.status_code}") + + pil_image = Image.open(BytesIO(img_response.content)) + if pil_image.mode != 'RGB': + pil_image = pil_image.convert('RGB') + + image_np = np.array(pil_image).astype(np.float32) / 255.0 + image_tensor = torch.from_numpy(image_np)[None,] + + # 清理临时文件 + if temp_img_path and os.path.exists(temp_img_path): + try: + os.remove(temp_img_path) + except: + pass + + print(f"🎉 {mode_name}完成！使用的第 {i+1} 个API Token") + return (image_tensor,) + + except Exception as e: + last_exception = e + print(f"⚠️ 第 {i+1} 个API Token失败: {str(e)}") + # 清理临时文件 + if temp_img_path and os.path.exists(temp_img_path): + try: + os.remove(temp_img_path) + except: + pass + if i < len(tokens) - 1: # 不是最后一个Token + print(f"➡️ 尝试下一个API Token...") + continue + else: + break # 所有Token都失败了 + + # 所有Token都失败 + raise Exception(f"所有 {len(tokens)} 个API Token都失败了。最后的错误: {str(last_exception)}") # 节点映射