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a/modelscope_config.json +++ b/modelscope_config.json @@ -19,7 +19,9 @@ "MusePublic/489_ckpt_FLUX_1", "MusePublic/flux-high-res", "black-forest-labs/FLUX.1-Krea-dev", - "MAILAND/majicflus_v1" + "MAILAND/majicflus_v1", + "MoYouuu/MYHuman-QWen", + "Tongyi-MAI/Z-Image-Turbo" ], "image_edit_models": [ "Qwen/Qwen-Image-Edit", @@ -47,5 +49,42 @@ "Qwen/Qwen2-VL-7B-Instruct", "Qwen/QVQ-72B-Preview", "PaddlePaddle/ERNIE-4.5-VL-28B-A3B-PT" + ], + "default_lora_model": "", + "default_lora_weight": 0.8, + "lora_presets": [ + { + "name": "无LoRA", + "model_id": "", + "weight": 0.8 + }, + { + "name": "Qwen-小红书风格美人", + "model_id": "qiyuanai/TikTok_Xiaohongshu_career_line_beauty_v1", + "weight": 0.8 + }, + { + "name": "Qwen-小红书甜妹", + "model_id": "wilderkid/Rednote_SweetGirl", + "weight": 0.8 + }, + { + "name": "Qwen-小红薯-风格插画-排版", + "model_id": "YJ777YJ/xiaohshu", + "weight": 0.8 + }, + { + "name": "Qwen-真实人像小红书风格", + "model_id": "chy1125677992/qwxhs", + "weight": 0.8 + }, + { + "name": "Qwen-抖音小红书手持手机自拍美女", + "model_id": "qiyuanai/shouchizipai_v1", + "weight": 0.8 + } + ], + "api_tokens": [ + "ms" ] -} +} \ No newline at end of file diff --git a/modelscope_image_caption_node.py b/modelscope_image_caption_node.py index f00a93e..f9cf566 100644 --- a/modelscope_image_caption_node.py +++ b/modelscope_image_caption_node.py @@ -8,7 +8,7 @@ from io import BytesIO import os import base64 import re -from .modelscope_image_node import load_config, load_api_tokens, save_api_tokens, tensor_to_base64_url +from .modelscope_image_node import load_config, save_config, tensor_to_base64_url # 检查openai库是否可用 try: @@ -17,6 +17,26 @@ try: except ImportError: OPENAI_AVAILABLE = False +# 仅与modelscope_config.json交互的API Token管理函数 +def load_api_tokens(): + try: + cfg = load_config() + 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中的tokens失败: {e}") + return [] + +def save_api_tokens(tokens): + try: + cfg = load_config() + cfg["api_tokens"] = tokens + return save_config(cfg) + except Exception as e: + print(f"保存tokens到config失败: {e}") + return False + class ModelScopeImageCaptionNode: def __init__(self): pass @@ -40,7 +60,6 @@ class ModelScopeImageCaptionNode: ] return { "required": { - "image": ("IMAGE",), "api_tokens": ("STRING", { "default": f"***已保存{len(saved_tokens)}个Token***" if saved_tokens else "", "placeholder": "请输入API Token（支持多个，用逗号/换行分隔）", @@ -48,6 +67,8 @@ class ModelScopeImageCaptionNode: }), }, "optional": { + # 关键修改：将image设置为可选输入 + "image": ("IMAGE", {"optional": True}), "prompt1": ("STRING", { "multiline": True, "default": "详细描述这张图片的内容，包括主体、背景、颜色、风格等信息" @@ -56,9 +77,8 @@ class ModelScopeImageCaptionNode: "multiline": True, "default": "" }), - # 添加模型下拉选择 "model": (supported_models, { - "default": "Qwen/Qwen3-VL-8B-Instruct" # 默认选中原模型 + "default": "Qwen/Qwen3-VL-8B-Instruct" }), "max_tokens": ("INT", { "default": 1000, @@ -87,12 +107,24 @@ class ModelScopeImageCaptionNode: # 支持多种分隔符拆分Token tokens = re.split(r'[,;\n]+', token_input) return [token.strip() for token in tokens if token.strip()] + + def create_blank_image(self, width=64, height=64): + """创建空白图像张量（符合ComfyUI的图像格式要求）""" + # 创建白色背景的RGB图像 + blank_np = np.ones((height, width, 3), dtype=np.uint8) * 255 + # 转换为ComfyUI格式的张量 (batch, height, width, channels) + blank_tensor = torch.from_numpy(blank_np).unsqueeze(0).float() / 255.0 + return blank_tensor - # 调整参数顺序，加入新的prompt2参数 def generate_caption(self, image=None, api_tokens="", prompt1="详细描述这张图片的内容", prompt2="", model="Qwen/Qwen3-VL-8B-Instruct", max_tokens=1000, temperature=0.7): if not OPENAI_AVAILABLE: return ("请先安装openai库: pip install openai",) + # 关键修改：处理输入图像为空的情况 + if image is None: + print("⚠️ 未输入图像，自动生成空白图像作为输入") + image = self.create_blank_image() + # 处理提示词合并 prompt_parts = [] if prompt1.strip(): @@ -100,13 +132,12 @@ class ModelScopeImageCaptionNode: if prompt2.strip(): prompt_parts.append(prompt2.strip()) - # 如果两个提示词都为空，使用默认提示 if not prompt_parts: prompt = "详细描述这张图片的内容，包括主体、背景、颜色、风格等信息" else: prompt = ", ".join(prompt_parts) - # 解析Token列表（支持多个） + # 解析Token列表 tokens = self.parse_api_tokens(api_tokens) if not tokens: raise Exception("请提供至少一个有效的API Token") @@ -122,7 +153,7 @@ class ModelScopeImageCaptionNode: try: print(f"🔍 开始生成图像描述...") print(f"📝 提示词: {prompt}") - print(f"🤖 模型: {model}") # 显示选中的模型 + print(f"🤖 模型: {model}") print(f"🔑 可用Token数量: {len(tokens)}") # 转换图像为base64格式 @@ -149,13 +180,11 @@ class ModelScopeImageCaptionNode: try: print(f"🔄 尝试使用第 {i+1}/{len(tokens)} 个Token...") - # 初始化OpenAI客户端 client = OpenAI( base_url='https://api-inference.modelscope.cn/v1', api_key=token ) - # 调用API（使用选中的模型） response = client.chat.completions.create( model=model, messages=messages, @@ -164,7 +193,6 @@ class ModelScopeImageCaptionNode: stream=False ) - # 成功获取结果 description = response.choices[0].message.content print(f"✅ 第 {i+1} 个Token调用成功!") print(f"📄 结果预览: {description[:100]}...") diff --git a/modelscope_image_node.py b/modelscope_image_node.py index 45d359e..f60acbe 100644 --- a/modelscope_image_node.py +++ b/modelscope_image_node.py @@ -9,80 +9,83 @@ import os import folder_paths import base64 import tempfile +import re +# -------------------------- 核心配置管理 -------------------------- def load_config(): + """从modelscope_config.json加载配置，确保优先使用配置文件中的lora_presets""" config_path = os.path.join(os.path.dirname(__file__), 'modelscope_config.json') + default_config = { + "default_model": "Qwen/Qwen-Image", + "timeout": 720, + "image_download_timeout": 30, + "default_prompt": "A beautiful landscape", + "default_negative_prompt": "", + "default_width": 512, + "default_height": 512, + "default_seed": -1, + "default_steps": 30, + "default_guidance": 7.5, + "default_lora_weight": 0.8, + "image_models": ["Qwen/Qwen-Image"], + "image_edit_models": ["Qwen/Qwen-Image-Edit"], + "lora_presets": [ + {"name": "无LoRA", "model_id": "", "weight": 0.8} + ], + "api_tokens": [] + } try: with open(config_path, 'r', encoding='utf-8') as f: - return json.load(f) - except: - return { - "default_model": "Qwen/Qwen-Image", - "timeout": 720, - "image_download_timeout": 30, - "default_prompt": "A beautiful landscape" - } + config = json.load(f) + # 确保配置文件中存在所有必要字段，缺失则补充则补充默认值 + for key, value in default_config.items(): + if key not in config: + config[key] = value + return config + except Exception as e: + print(f"读取配置文件失败，使用默认配置: {e}") + return default_config def save_config(config: dict) -> bool: + """保存配置到modelscope_config.json""" config_path = os.path.join(os.path.dirname(__file__), 'modelscope_config.json') try: with open(config_path, 'w', encoding='utf-8') as f: json.dump(config, f, ensure_ascii=False, indent=2) return True except Exception as e: - print(f"保存配置失败: {e}") + print(f"保存配置文件失败: {e}") return False +# -------------------------- API Token管理 -------------------------- 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(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tokens失败(.qwen_tokens): {e}") - try: cfg = load_config() cfg["api_tokens"] = tokens - if save_config(cfg): - return True - return False + return save_config(cfg) except Exception as e: - print(f"保存tokens失败(config.json): {e}") + print(f"保存API tokens失败: {e}") return False 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() 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tokens失败: {e}") - - try: - 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tokens失败: {e}") + print(f"加载API 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()] +# -------------------------- 图像转换工具 -------------------------- def tensor_to_base64_url(image_tensor): try: if len(image_tensor.shape) == 4: @@ -93,8 +96,7 @@ def tensor_to_base64_url(image_tensor): else: image_np = image_tensor.cpu().numpy().astype(np.uint8) - pil_image = Image.fromarray(image_np) - + pil_image = Image.fromarrayarray(image_np) buffer = BytesIO() pil_image.save(buffer, format='JPEG', quality=85) img_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8') @@ -102,10 +104,193 @@ def tensor_to_base64_url(image_tensor): return f"data:image/jpeg;base64,{img_base64}" except Exception as e: - print(f"图像转换失败: {e}") raise Exception(f"图像格式转换失败: {str(e)}") +# -------------------------- LoRA预设管理节点 -------------------------- +class ModelScopeLoraPresetNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + # 从配置文件加载LoRA预设列表 + config = load_config() + lora_presets = config.get("lora_presets", []) + preset_names = [preset.get("name", "无LoRA") for preset in lora_presets] + + return { + "required": { + "action": (["查看预设", "添加预设", "删除预设", "保存预设"], {"default": "查看预设"}), + }, + "optional": { + "preset_name": ("STRING", {"default": "自定义LoRA", "label": "预设名称"}), + "lora_model_id": ("STRING", {"default": "", "label": "LoRA模型ID", "placeholder": "例如：qiyuanai/TikTok_Xiaohongshu_career_line_beauty_v1"}), + "default_weight": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "默认权重"}), + "target_preset": (preset_names, {"default": preset_names[0] if preset_names else "无LoRA", "label": "目标预设"}), + } + } + + RETURN_TYPES = ("STRING", "FLOAT", "STRING") + RETURN_NAMES = ("lora_model_id", "lora_weight", "preset_info") + FUNCTION = "manage_lora_presets" + CATEGORY = "ModelScopeAPI/LoRA" + + def manage_lora_presets(self, action, preset_name="", lora_model_id="", default_weight=0.8, target_preset=""): + # 所有操作均基于配置文件中的LoRA预设 + config = load_config() + lora_presets = config.get("lora_presets", []) + preset_info = f"当前共有 {len(lora_presets)} 个LoRA预设" + + if action == "查看预设": + info_lines = ["=== LoRA预设列表 ==="] + for i, preset in enumerate(lora_presets): + info_lines.append(f"{i+1}. {preset.get('name')} | ID: {preset.get('model_id')} | 权重: {preset.get('weight')}") + preset_info = "\n".join(info_lines) + selected_preset = next((p for p in lora_presets if p.get("name") == target_preset), {"model_id": "", "weight": 0.8}) + return (selected_preset.get("model_id"), selected_preset.get("weight"), preset_info) + + elif action == "添加预设": + if not preset_name or preset_name.strip() == "": + raise Exception("预设名称不能为空") + + if any(p.get("name") == preset_name for p in lora_presets): + raise Exception(f"已存在名为 {preset_name} 的预设") + + new_preset = { + "name": preset_name.strip(), + "model_id": lora_model_id.strip(), + "weight": float(default_weight) + } + lora_presets.append(new_preset) + config["lora_presets"] = lora_presets + save_config(config) + preset_info = f"成功添加预设: {preset_name} | ID: {lora_model_id}" + return (lora_model_id, default_weight, preset_info) + + elif action == "删除预设": + if target_preset == "无LoRA": + raise Exception("不能删除默认的无LoRA预设") + + original_count = len(lora_presets) + lora_presets = [p for p in lora_presets if p.get("name") != target_preset] + if len(lora_presets) == original_count: + raise Exception(f"未找到预设: {target_preset}") + + config["lora_presets"] = lora_presets + save_config(config) + preset_info = f"成功删除预设: {target_preset}" + return ("", 0.8, preset_info) + + elif action == "保存预设": + updated = False + for i, preset in enumerate(lora_presets): + if preset.get("name") == target_preset: + lora_presets[i]["model_id"] = lora_model_id.strip() + lora_presets[i]["weight"] = float(default_weight) + updated = True + break + + if not updated: + raise Exception(f"未找到预设: {target_preset}") + + config["lora_presets"] = lora_presets + save_config(config) + preset_info = f"成功更新预设: {target_preset} | 新ID: {lora_model_id} | 新权重: {default_weight}" + return (lora_model_id, default_weight, preset_info) + + return ("", 0.8, preset_info) +# -------------------------- 单LoRA加载节点 -------------------------- +class ModelScopeSingleLoraLoaderNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + # 从配置文件加载LoRA预设选项 + config = load_config() + lora_presets = config.get("lora_presets", []) + preset_options = [preset.get("name", "无LoRA") for preset in lora_presets] + + return { + "required": { + "lora_preset": (preset_options, {"default": preset_options[0], "label": "LoRA预设"}), + }, + "optional": { + "lora_weight": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "自定义权重"}), + "use_custom_weight": ("BOOLEAN", {"default": False, "label_on": "使用自定义权重", "label_off": "使用预设权重"}), + } + } + + RETURN_TYPES = ("STRING", "FLOAT") + RETURN_NAMES = ("lora_id", "lora_weight") + FUNCTION = "load_single_lora" + CATEGORY = "ModelScopeAPI/LoRA" + + def load_single_lora(self, lora_preset, lora_weight=0.8, use_custom_weight=False): + # 从配置文件读取选中的LoRA信息 + config = load_config() + lora_presets = config.get("lora_presets", []) + + selected_preset = next((p for p in lora_presets if p.get("name") == lora_preset), {"model_id": "", "weight": 0.8}) + lora_id = selected_preset.get("model_id", "") + final_weight = lora_weight if use_custom_weight else selected_preset.get("weight", 0.8) + + return (lora_id, final_weight) + +# -------------------------- 多LoRA加载节点 -------------------------- +class ModelScopeMultiLoraLoaderNode: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(cls): + # 从配置文件加载LoRA预设选项 + config = load_config() + lora_presets = config.get("lora_presets", []) + preset_options = [preset.get("name", "无LoRA") for preset in lora_presets] + + return { + "required": { + "lora1_preset": (preset_options, {"default": preset_options[0], "label": "LoRA 1 预设"}), + "lora2_preset": (preset_options, {"default": preset_options[0], "label": "LoRA 2 预设"}), + "lora3_preset": (preset_options, {"default": preset_options[0], "label": "LoRA 3 预设"}), + }, + "optional": { + "lora1_weight": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA 1 权重"}), + "lora2_weight": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA 2 权重"}), + "lora3_weight": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA 3 权重"}), + "lora1_use_custom": ("BOOLEAN", {"default": False, "label_on": "LoRA1用自定义权重", "label_off": "用预设权重"}), + "lora2_use_custom": ("BOOLEAN", {"default": False, "label_on": "LoRA2用自定义权重", "label_off": "用预设权重"}), + "lora3_use_custom": ("BOOLEAN", {"default": False, "label_on": "LoRA3用自定义权重", "label_off": "用预设权重"}), + } + } + + RETURN_TYPES = ("STRING", "STRING", "STRING", "FLOAT", "FLOAT", "FLOAT") + RETURN_NAMES = ("lora1_id", "lora2_id", "lora3_id", "lora1_w", "lora2_w", "lora3_w") + FUNCTION = "load_multi_lora" + CATEGORY = "ModelScopeAPI/LoRA" + + def load_multi_lora(self, lora1_preset, lora2_preset, lora3_preset, + lora1_weight=0.8, lora2_weight=0.8, lora3_weight=0.8, + lora1_use_custom=False, lora2_use_custom=False, lora3_use_custom=False): + # 从配置文件读取多个LoRA信息 + config = load_config() + lora_presets = config.get("lora_presets", []) + + def get_lora_info(preset_name, custom_weight, use_custom): + preset = next((p for p in lora_presets if p.get("name") == preset_name), {"model_id": "", "weight": 0.8}) + model_id = preset.get("model_id", "") + final_weight = custom_weight if use_custom else preset.get("weight", 0.8) + return model_id, final_weight + + lora1_id, lora1_w = get_lora_info(lora1_preset, lora1_weight, lora1_use_custom) + lora2_id, lora2_w = get_lora_info(lora2_preset, lora2_weight, lora2_use_custom) + lora3_id, lora3_w = get_lora_info(lora3_preset, lora3_weight, lora3_use_custom) + + return (lora1_id, lora2_id, lora3_id, lora1_w, lora2_w, lora3_w) + +# -------------------------- 生图节点 -------------------------- class ModelScopeImageNode: def __init__(self): pass @@ -114,6 +299,7 @@ class ModelScopeImageNode: def INPUT_TYPES(cls): config = load_config() saved_tokens = load_api_tokens() + return { "required": { "prompt": ("STRING", { @@ -162,6 +348,12 @@ class ModelScopeImageNode: "max": 20.0, "step": 0.1 }), + "lora1_id": ("STRING", {"default": "", "label": "LoRA1 模型ID"}), + "lora1_w": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA1 权重"}), + "lora2_id": ("STRING", {"default": "", "label": "LoRA2 模型ID"}), + "lora2_w": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA2 权重"}), + "lora3_id": ("STRING", {"default": "", "label": "LoRA3 模型ID"}), + "lora3_w": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA3 权重"}), } } @@ -170,25 +362,49 @@ class ModelScopeImageNode: FUNCTION = "generate_image" CATEGORY = "ModelScopeAPI" - def generate_image(self, prompt, api_tokens, 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, + lora1_id="", lora1_w=0.8, lora2_id="", lora2_w=0.8, lora3_id="", lora3_w=0.8): config = load_config() 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） + # 保存新Token（如果有变化） 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") - # 轮询尝试每个Token + print(f"🔍 开始生成图像...") + print(f"📝 提示词: {prompt}") + print(f"❌ 反向提示词: {negative_prompt if negative_prompt else '无'}") + print(f"🤖 模型: {model}") + print(f"🔑 可用Token数量: {len(tokens)}") + print(f"📐 尺寸: {width}x{height}") + print(f"🔄 步数: {steps}") + print(f"🧭 引导系数: {guidance}") + print(f"🔢 种子: {seed if seed != -1 else '随机'}") + + # 打印LoRA信息 + lora_info = [] + if lora1_id.strip(): + lora_info.append(f"LoRA1: {lora1_id} (权重: {lora1_w})") + if lora2_id.strip(): + lora_info.append(f"LoRA2: {lora2_id} (权重: {lora2_w})") + if lora3_id.strip(): + lora_info.append(f"LoRA3: {lora3_id} (权重: {lora3_w})") + if lora_info: + print(f"🔧 LoRA配置: {', '.join(lora_info)}") + else: + print("🔧 未使用LoRA") + last_exception = None for i, token in enumerate(tokens): try: - print(f"🔄 尝试使用第 {i+1} 个API Token...") + print(f"🔄 尝试使用第 {i+1}/{len(tokens)} 个Token...") + url = 'https://api-inference.modelscope.cn/v1/images/generations' payload = { 'model': model, @@ -197,21 +413,40 @@ class ModelScopeImageNode: 'steps': steps, 'guidance': guidance } + + lora_dict = {} + if lora1_id and lora1_id.strip() != "": + lora_dict[lora1_id.strip()] = float(lora1_w) + if lora2_id and lora2_id.strip() != "": + lora_dict[lora2_id.strip()] = float(lora2_w) + if lora3_id and lora3_id.strip() != "": + lora_dict[lora3_id.strip()] = float(lora3_w) + + if lora_dict: + payload['loras'] = lora_dict + first_lora_id = next(iter(lora_dict.keys())) + first_lora_w = next(iter(lora_dict.values())) + payload['lora'] = first_lora_id + payload['lora_weight'] = first_lora_w + 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 + payload['seed'] = random.randint(0, 2147483647) + print(f"🎲 随机生成种子: {payload['seed']}") headers = { 'Authorization': f'Bearer {token}', 'Content-Type': 'application/json', - 'X-ModelScope-Async-Mode': 'true' + 'X-ModelScope-Async-Mode': 'true', + 'X-ModelScope-Task-Type': 'text-to-image-generation', + 'X-ModelScope-Request-Params': json.dumps({'loras': lora_dict} if lora_dict else {}) } + print(f"🚀 发送API请求到 {model}...") submission_response = requests.post( url, data=json.dumps(payload, ensure_ascii=False).encode('utf-8'), @@ -220,11 +455,16 @@ class ModelScopeImageNode: ) if submission_response.status_code == 400: - # 尝试使用最小参数重试 + print("⚠️ 标准请求参数失败，尝试简化参数...") minimal_payload = { 'model': model, 'prompt': prompt } + if lora_dict: + minimal_payload['loras'] = lora_dict + minimal_payload['lora'] = first_lora_id + minimal_payload['lora_weight'] = first_lora_w + submission_response = requests.post( url, data=json.dumps(minimal_payload, ensure_ascii=False).encode('utf-8'), @@ -237,9 +477,10 @@ class ModelScopeImageNode: 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...") + print(f"📌 获取任务ID: {task_id}, 开始轮询结果...") poll_start = time.time() max_wait_seconds = max(60, config.get('timeout', 720)) while True: @@ -251,53 +492,59 @@ class ModelScopeImageNode: }, 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') + print(f"⌛ 任务状态: {status} (已等待 {int(time.time() - poll_start)} 秒)") + 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...") + print(f"✅ 任务完成，获取图片URL") 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") + raise Exception(f"任务轮询超时 ({max_wait_seconds}秒)，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...") + print(f"✅ 直接获取图片URL") else: raise Exception(f"未识别的API返回格式: {submission_json}") + print(f"📥 下载图片...") 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}") + print(f"🖼️ 处理图片数据...") 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") + + print(f"✅ 第 {i+1} 个Token调用成功，图像生成完成!") 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...") + print(f"❌ 第 {i+1} 个Token调用失败: {str(e)}") + if i < len(tokens) - 1: + print(f"⏳ 准备尝试下一个Token...") continue else: - break # 所有Token都失败了 + break - # 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)}") - +# -------------------------- 编辑节点（已添加LoRA功能） -------------------------- class ModelScopeImageEditNode: def __init__(self): pass @@ -307,7 +554,6 @@ class ModelScopeImageEditNode: config = load_config() saved_tokens = load_api_tokens() - # 获取模型列表 edit_models = config.get("image_edit_models", ["Qwen/Qwen-Image-Edit"]) gen_models = config.get("image_models", ["Qwen/Qwen-Image"]) @@ -369,6 +615,13 @@ class ModelScopeImageEditNode: "min": -1, "max": 2147483647 }), + # LoRA相关参数（与生图节点保持一致） + "lora1_id": ("STRING", {"default": "", "label": "LoRA1 模型ID"}), + "lora1_w": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA1 权重"}), + "lora2_id": ("STRING", {"default": "", "label": "LoRA2 模型ID"}), + "lora2_w": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA2 权重"}), + "lora3_id": ("STRING", {"default": "", "label": "LoRA3 模型ID"}), + "lora3_w": ("FLOAT", {"default": 0.8, "min": 0.0, "max": 2.0, "step": 0.1, "label": "LoRA3 权重"}), } } @@ -379,51 +632,68 @@ class ModelScopeImageEditNode: 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): + width=512, height=512, steps=30, guidance=3.5, seed=-1, + lora1_id="", lora1_w=0.8, lora2_id="", lora2_w=0.8, lora3_id="", lora3_w=0.8): config = load_config() 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） + # 保存新Token（如果有变化） 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") - # 根据开关选择使用的模型 - if image_gen_mode: - model = gen_model - mode_name = "图生图" + mode = "图生图模式" if image_gen_mode else "图像编辑模式" + model = gen_model if image_gen_mode else edit_model + + print(f"🔍 开始图像编辑...") + print(f"📝 提示词: {prompt}") + print(f"❌ 反向提示词: {negative_prompt if negative_prompt else '无'}") + print(f"🤖 模型: {model} ({mode})") + print(f"🔑 可用Token数量: {len(tokens)}") + print(f"📐 尺寸: {width}x{height}") + print(f"🔄 步数: {steps}") + print(f"🧭 引导系数: {guidance}") + print(f"🔢 种子: {seed if seed != -1 else '随机'}") + + # 打印LoRA信息 + lora_info = [] + if lora1_id.strip(): + lora_info.append(f"LoRA1: {lora1_id} (权重: {lora1_w})") + if lora2_id.strip(): + lora_info.append(f"LoRA2: {lora2_id} (权重: {lora2_w})") + if lora3_id.strip(): + lora_info.append(f"LoRA3: {lora3_id} (权重: {lora3_w})") + if lora_info: + print(f"🔧 LoRA配置: {', '.join(lora_info)}") else: - model = edit_model - mode_name = "图像编辑" + print("🔧 未使用LoRA") - # 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...") + print(f"🔄 尝试使用第 {i+1}/{len(tokens)} 个Token...") - # 将图像转换为临时文件并上传获取URL temp_img_path = None image_url = None try: - # 保存图像到临时文件 + # 保存临时图像并上传 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_np = 255. * img.cpu().numpy() + img_pil = Image.fromarray(np.clip(img_np, 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}") + print(f"💾 已保存临时图像到 {temp_img_path}") - # 上传图像到kefan.cn获取URL + # 上传图像 upload_url = 'https://ai.kefan.cn/api/upload/local' with open(temp_img_path, 'rb') as img_file: files = {'file': img_file} @@ -436,17 +706,13 @@ class ModelScopeImageEditNode: 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}") + print(f"📤 图像上传成功，URL: {image_url[:50]}...") except Exception as e: - print(f"⚠️ 图像上传异常: {str(e)}") + print(f"⚠️ 图像上传失败，将使用base64编码: {str(e)}") - # 如果上传失败，回退到base64 + # 构建请求 payload if not image_url: - print("⚠️ 图像URL获取失败，回退到使用base64") + print("🔄 转换图像为base64格式...") image_data = tensor_to_base64_url(image) payload = { 'model': model, @@ -460,33 +726,48 @@ class ModelScopeImageEditNode: 'image_url': image_url } + # 构建LoRA参数 + lora_dict = {} + if lora1_id and lora1_id.strip() != "": + lora_dict[lora1_id.strip()] = float(lora1_w) + if lora2_id and lora2_id.strip() != "": + lora_dict[lora2_id.strip()] = float(lora2_w) + if lora3_id and lora3_id.strip() != "": + lora_dict[lora3_id.strip()] = float(lora3_w) + + if lora_dict: + payload['loras'] = lora_dict + first_lora_id = next(iter(lora_dict.keys())) + first_lora_w = next(iter(lora_dict.values())) + payload['lora'] = first_lora_id + payload['lora_weight'] = first_lora_w + + # 添加其他参数 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 - + payload['size'] = f"{width}x{height}" if steps != 30: payload['steps'] = steps - if guidance != 3.5: payload['guidance'] = guidance - if seed != -1: payload['seed'] = seed + else: + import random + payload['seed'] = random.randint(0, 2147483647) + print(f"🎲 随机生成种子: {payload['seed']}") + # 设置请求头 headers = { 'Authorization': f'Bearer {token}', 'Content-Type': 'application/json', - 'X-ModelScope-Async-Mode': 'true' + 'X-ModelScope-Async-Mode': 'true', + 'X-ModelScope-Task-Type': 'image-to-image-generation', + 'X-ModelScope-Request-Params': json.dumps({'loras': lora_dict} if lora_dict else {}) } - print(f"🖼️ 开始{mode_name}...") - print(f"✏️ 编辑提示: {prompt}") - print(f"🧠 使用模型: {model}") - + print(f"🚀 发送API请求到 {model}...") url = 'https://api-inference.modelscope.cn/v1/images/generations' submission_response = requests.post( url, @@ -503,7 +784,7 @@ class ModelScopeImageEditNode: 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...") + print(f"📌 获取任务ID: {task_id}, 开始轮询结果...") poll_start = time.time() max_wait_seconds = max(60, config.get('timeout', 720)) @@ -522,31 +803,31 @@ class ModelScopeImageEditNode: task_data = task_resp.json() status = task_data.get('task_status') + print(f"⌛ 任务状态: {status} (已等待 {int(time.time() - poll_start)} 秒)") 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...") + print(f"✅ 任务完成，获取图片URL") 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") - + raise Exception(f"任务轮询超时 ({max_wait_seconds}秒)，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}") + print(f"📥 下载编辑后的图片...") 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}") + print(f"🖼️ 处理图片数据...") pil_image = Image.open(BytesIO(img_response.content)) if pil_image.mode != 'RGB': pil_image = pil_image.convert('RGB') @@ -558,38 +839,43 @@ class ModelScopeImageEditNode: if temp_img_path and os.path.exists(temp_img_path): try: os.remove(temp_img_path) + print(f"🧹 已删除临时图像文件") except: - pass + print(f"⚠️ 无法删除临时图像文件 {temp_img_path}") - print(f"🎉 {mode_name}完成！使用的第 {i+1} 个API Token") + print(f"✅ 第 {i+1} 个Token调用成功，图像编辑完成!") return (image_tensor,) except Exception as e: last_exception = e - print(f"⚠️ 第 {i+1} 个API Token失败: {str(e)}") + print(f"❌ 第 {i+1} 个Token调用失败: {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...") + if i < len(tokens) - 1: + print(f"⏳ 准备尝试下一个Token...") continue else: - break # 所有Token都失败了 + break - # 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 +# -------------------------- 节点映射 -------------------------- NODE_CLASS_MAPPINGS = { "ModelScopeImageNode": ModelScopeImageNode, - "ModelScopeImageEditNode": ModelScopeImageEditNode + "ModelScopeImageEditNode": ModelScopeImageEditNode, + "ModelScopeLoraPresetNode": ModelScopeLoraPresetNode, + "ModelScopeSingleLoraLoaderNode": ModelScopeSingleLoraLoaderNode, + "ModelScopeMultiLoraLoaderNode": ModelScopeMultiLoraLoaderNode } NODE_DISPLAY_NAME_MAPPINGS = { "ModelScopeImageNode": "ModelScope-Image 生图节点", - "ModelScopeImageEditNode": "ModelScope-Image 图像编辑节点" -} + "ModelScopeImageEditNode": "ModelScope-Image 图像编辑节点", + "ModelScopeLoraPresetNode": "ModelScope-LoRA 预设管理", + "ModelScopeSingleLoraLoaderNode": "ModelScope-LoRA 单LoRA加载", + "ModelScopeMultiLoraLoaderNode": "ModelScope-LoRA 多LoRA加载" +} \ No newline at end of file diff --git a/modelscope_text_node.py b/modelscope_text_node.py index c0458dc..d6f75b1 100644 --- a/modelscope_text_node.py +++ b/modelscope_text_node.py @@ -22,39 +22,36 @@ def load_config(): "default_model": "Qwen/Qwen-Image", "timeout": 720, "image_download_timeout": 30, - "default_prompt": "A beautiful landscape" + "default_prompt": "A beautiful landscape", + "default_text_model": "Qwen/Qwen3-Coder-480B-A35B-Instruct", + "default_system_prompt": "You are a helpful assistant.", + "default_user_prompt": "你好", + "api_token": "" } +def save_config(config): + config_path = os.path.join(os.path.dirname(__file__), 'modelscope_config.json') + try: + with open(config_path, 'w', encoding='utf-8') as f: + json.dump(config, f, ensure_ascii=False, indent=2) + return True + except Exception as e: + print(f"保存配置失败: {e}") + return False + def load_api_token(): - token_path = os.path.join(os.path.dirname(__file__), '.qwen_token') try: cfg = load_config() - token_from_cfg = cfg.get("api_token", "").strip() - if token_from_cfg: - return token_from_cfg + return cfg.get("api_token", "").strip() except Exception as e: - print(f"读取config.json中的token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 "" - except Exception as e: - print(f"加载token失败: {e}") + print(f"读取 config.json中的token失败: {e}") return "" def save_api_token(token): - token_path = os.path.join(os.path.dirname(__file__), '.qwen_token') try: - with open(token_path, 'w', encoding='utf-8') as f: - f.write(token) cfg = load_config() cfg["api_token"] = token - config_path = os.path.join(os.path.dirname(__file__), 'config.json') - with open(config_path, 'w', encoding='utf-8') as f: - json.dump(cfg, f, ensure_ascii=False, indent=2) - return True + return save_config(cfg) except Exception as e: print(f"保存token失败: {e}") return False @@ -83,7 +80,7 @@ class ModelScopeTextNode: "default": config.get("default_user_prompt", "你好") }), "api_token": ("STRING", { - "default": "", + "default": saved_token, "placeholder": "请输入您的魔搭API Token", "multiline": False }), @@ -132,7 +129,7 @@ class ModelScopeTextNode: saved_token = load_api_token() if api_token != saved_token: if save_api_token(api_token): - print("✅ API Token已自动保存") + print("✅ API Token已自动保存到modelscope_config.json") else: print("⚠️ API Token保存失败，但不影响当前使用") diff --git a/modelscope_vision_node.py b/modelscope_vision_node.py index 10d30e3..5f4ef65 100644 --- a/modelscope_vision_node.py +++ b/modelscope_vision_node.py @@ -19,7 +19,7 @@ except ImportError: OpenAI = None def load_config(): - config_path = os.path.join(os.path.dirname(__file__), 'modelscope_config.json') + config_path = os.path.join.join(os.path.dirname(__file__), 'modelscope_config.json') try: with open(config_path, 'r', encoding='utf-8') as f: return json.load(f) @@ -28,39 +28,36 @@ def load_config(): "default_model": "Qwen/Qwen-Image", "timeout": 720, "image_download_timeout": 30, - "default_prompt": "A beautiful landscape" + "default_prompt": "A beautiful landscape", + "api_token": "" # 确保默认默认配置中添加api_token字段 } +def save_config(config): + """保存配置到modelscope_config.json""" + config_path = os.path.join(os.path.dirname(__file__), 'modelscope_config.json') + try: + with open(config_path, 'w', encoding='utf-8') as f: + json.dump(config, f, ensure_ascii=False, indent=2) + return True + except Exception as e: + print(f"保存配置失败: {e}") + return False + def load_api_token(): - token_path = os.path.join(os.path.dirname(__file__), '.qwen_token') + """仅从modelscope_config.json读取API Token""" try: cfg = load_config() - token_from_cfg = cfg.get("api_token", "").strip() - if token_from_cfg: - return token_from_cfg + return cfg.get("api_token", "").strip() except Exception as e: print(f"读取config.json中的token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 "" - except Exception as e: - print(f"加载token失败: {e}") return "" def save_api_token(token): - token_path = os.path.join(os.path.dirname(__file__), '.qwen_token') + """仅将API Token保存到modelscope_config.json""" try: - with open(token_path, 'w', encoding='utf-8') as f: - f.write(token) cfg = load_config() - cfg["api_token"] = token - config_path = os.path.join(os.path.dirname(__file__), 'config.json') - with open(config_path, 'w', encoding='utf-8') as f: - json.dump(cfg, f, ensure_ascii=False, indent=2) - return True + cfg["api_token"] = token.strip() + return save_config(cfg) except Exception as e: print(f"保存token失败: {e}") return False @@ -154,7 +151,7 @@ class ModelScopeVisionNode: saved_token = load_api_token() if api_token != saved_token: if save_api_token(api_token): - print("✅ API Token已自动保存") + print("✅ API Token已自动保存到modelscope_config.json") else: print("⚠️ API Token保存失败，但不影响当前使用")