diff --git a/flowy/nodes.py b/flowy/nodes.py index e6ba0f5..46558b5 100644 --- a/flowy/nodes.py +++ b/flowy/nodes.py @@ -4,7 +4,7 @@ import logging from .types import STRING from .api_key_manager import save_api_key -# 设置日志 +# Set up logging logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger(__name__) @@ -20,7 +20,7 @@ from .nodes_json import FlowyPreviewJSON, FlowyExtractJSON, ComflowyLoadJSON from .nodes_http import FlowyHttpRequest from .nodes_llm import FlowyLLM from .nodes_upscale import FlowyUpscale -from .nodes_flux import ComflowyFlux +from .nodes_flux import FlowyFlux from .nodes_ideogram import FlowyIdeogram @@ -72,7 +72,7 @@ NODE_CLASS_MAPPINGS = { "Comflowy_Set_API_Key": ComflowySetAPIKey, "Comflowy_Upscale": FlowyUpscale, "Comflowy_Ideogram": FlowyIdeogram, - "Comflowy_Flux": ComflowyFlux, + "Comflowy_Flux": FlowyFlux, } NODE_DISPLAY_NAME_MAPPINGS = { diff --git a/flowy/nodes_flux.py b/flowy/nodes_flux.py index a9153fd..216675d 100644 --- a/flowy/nodes_flux.py +++ b/flowy/nodes_flux.py @@ -7,13 +7,13 @@ import torch import numpy as np import logging import json -from .types import STRING, INT, API_HOST +from .types import STRING, INT, API_HOST, SAFETY_TOLERANCE, BOOLEAN from .utils import logger, get_nested_value from .api_key_manager import load_api_key logger = logging.getLogger(__name__) -class ComflowyFlux: +class FlowyFlux: @classmethod def INPUT_TYPES(s): return { @@ -34,20 +34,15 @@ class ComflowyFlux: ],), "height": ("INT", {"default": 256, "min": 256, "max": 1440}), "width": ("INT", {"default": 256, "min": 256, "max": 1440}), - "prompt_upsampling": (["Off", "On"],), - "safety_tolerance": ([ - "1", - "2", - "3", - "4", - "5" - ]), "seed": ("INT", {"default": 0, "min": 0, "max": 2147483647}), + "prompt_upsampling": BOOLEAN, + "safety_tolerance": (SAFETY_TOLERANCE,), + "output_quality": ("INT", {"default": 80, "min": 1, "max": 100}), } } RETURN_TYPES = ("IMAGE",) - FUNCTION = "generate_image_with_flux" + FUNCTION = "generate" CATEGORY = "Comflowy" DESCRIPTION = """ Nodes from https://comflowy.com: @@ -58,11 +53,12 @@ Nodes from https://comflowy.com: - Height and width are only used when aspect_ratio=custom. Must be a multiple of 32 (if it's not, it will be rounded to nearest multiple of 32). - Prompt Upsampling: Automatically modify the prompt for more creative generation. - Safety tolerance, 1 is most strict and 5 is most permissive. + - Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs. - Make sure to set your API Key using the 'Comflowy Set API Key' node before using this node. - Output: Returns the generated image. """ - def generate_image_with_flux(self, prompt, version, aspect_ratio, height, width, seed, prompt_upsampling, safety_tolerance): + def generate(self, prompt, version, aspect_ratio, height, width, seed, prompt_upsampling, safety_tolerance, output_quality): api_key = load_api_key() if not api_key: @@ -70,7 +66,7 @@ Nodes from https://comflowy.com: logger.error(error_msg) raise ValueError(error_msg) - logger.info(f"开始处理 Flux 图像生成请求。prompt: {prompt}, version: {version}, aspect_ratio: {aspect_ratio}, height: {height}, width: {width}, seed: {seed}, prompt_upsampling: {prompt_upsampling}, safety_tolerance: {safety_tolerance}") + logger.info(f"Starting Flux image generation request. prompt: {prompt}, version: {version}, aspect_ratio: {aspect_ratio}, height: {height}, width: {width}, seed: {seed}, prompt_upsampling: {prompt_upsampling}, safety_tolerance: {safety_tolerance}, output_quality: {output_quality}") try: response = requests.post( @@ -82,68 +78,69 @@ Nodes from https://comflowy.com: "aspect_ratio": aspect_ratio, "height": height, "width": width, + "seed": seed, "prompt_upsampling": prompt_upsampling, "safety_tolerance": safety_tolerance, - "seed": seed, + "output_quality": output_quality, } ) response.raise_for_status() result = response.json() - logger.info(f"API 请求完成。状态码: {response.status_code}") - logger.debug(f"API 响应内容: {json.dumps(result, indent=2)}") + logger.info(f"API request completed. Status code: {response.status_code}") + logger.debug(f"API response content: {json.dumps(result, indent=2)}") if not result.get('success'): - logger.error(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}") - raise Exception(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}") + logger.error(f"API request failed. Response content: {json.dumps(result, indent=2)}") + raise Exception(f"API request failed. Response content: {json.dumps(result, indent=2)}") output_url = result.get('data', {}).get('output') if not output_url or not isinstance(output_url, str): - logger.error(f"完整的 API 响应: {json.dumps(result, indent=2)}") - raise Exception(f"无法获取有效的输出图像 URL。API 响应中没有预期的数据结构。完整响应: {json.dumps(result, indent=2)}") + logger.error(f"Complete API response: {json.dumps(result, indent=2)}") + raise Exception(f"Unable to get valid output image URL. API response does not have expected data structure. Complete response: {json.dumps(result, indent=2)}") - logger.info(f"获取到的输出 URL: {output_url}") + logger.info(f"Obtained output URL: {output_url}") - # 验证 URL 是否可访问 + # Verify URL is accessible try: url_check = requests.head(output_url) url_check.raise_for_status() except requests.RequestException as e: - logger.error(f"无法访问输出 URL: {str(e)}") - raise Exception(f"无法访问输出 URL: {str(e)}") + logger.error(f"Unable to access output URL: {str(e)}") + raise Exception(f"Unable to access output URL: {str(e)}") - # 添加延迟,等待 Replicate 处理完成 + # Add delay, wait for Replicate to process time.sleep(10) img_response = requests.get(output_url, stream=True) img_response.raise_for_status() - # 将图像数据转换为 PIL Image + # Convert image data to PIL Image img = Image.open(img_response.raw) - # 转换为 numpy 数组 + # Convert to numpy array img_np = np.array(img) - # 确保图像是 3 通道 RGB - if len(img_np.shape) == 2: # 灰度图像 + # Ensure image is 3 channel RGB + if len(img_np.shape) == 2: # Grayscale image img_np = np.stack([img_np] * 3, axis=-1) - elif img_np.shape[-1] == 4: # RGBA 图像 + elif img_np.shape[-1] == 4: # RGBA image img_np = img_np[:, :, :3] - # 转换为 float32 并归一化到 0-1 范围 + # Convert to float32 and normalize to 0-1 range img_np = img_np.astype(np.float32) / 255.0 - # 转换为 torch tensor，确保形状为 [B,H,W,C] - img_tensor = torch.from_numpy(img_np).unsqueeze(0) # 添加批次维度 + # Convert to torch tensor, ensuring shape is [B,H,W,C] + img_tensor = torch.from_numpy(img_np).unsqueeze(0) # Add batch dimension - logger.info(f"图像处理完成。输出张量形状: {img_tensor.shape}") + logger.info(f"Image processing completed. Output tensor shape: {img_tensor.shape}") return (img_tensor,) except Exception as e: - error_msg = f"图像生成过程中出错: {str(e)}" + error_msg = f"Error during image generation: {str(e)}" logger.error(error_msg) - logger.exception("详细错误信息:") - # 返回一个错误标记图像，确保形状为 [B,H,W,C] + logger.exception("Detailed error information:") + # Return an error marked image, ensuring shape is [B,H,W,C] error_image = torch.zeros((1, 100, 400, 3), dtype=torch.float32) return (error_image,) diff --git a/flowy/nodes_ideogram.py b/flowy/nodes_ideogram.py index ca14a38..c293d8a 100644 --- a/flowy/nodes_ideogram.py +++ b/flowy/nodes_ideogram.py @@ -52,7 +52,7 @@ Nodes from https://comflowy.com: logger.error(error_msg) raise ValueError(error_msg) - logger.info(f"开始处理 Ideogram 图像生成请求。prompt: {prompt}, negative_prompt: {negative_prompt}, resolution: {resolution}, style_type: {style_type}, aspect_ratio: {aspect_ratio}, magic_prompt_option: {magic_prompt_option}, seed: {seed}") + logger.info(f"Starting Ideogram image generation request. prompt: {prompt}, negative_prompt: {negative_prompt}, resolution: {resolution}, style_type: {style_type}, aspect_ratio: {aspect_ratio}, magic_prompt_option: {magic_prompt_option}, seed: {seed}") try: response = requests.post( @@ -72,60 +72,60 @@ Nodes from https://comflowy.com: response.raise_for_status() result = response.json() - logger.info(f"API 请求完成。状态码: {response.status_code}") - logger.debug(f"API 响应内容: {json.dumps(result, indent=2)}") + logger.info(f"API request completed. Status code: {response.status_code}") + logger.debug(f"API response content: {json.dumps(result, indent=2)}") if not result.get('success'): - logger.error(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}") - raise Exception(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}") + logger.error(f"API request failed. Response content: {json.dumps(result, indent=2)}") + raise Exception(f"API request failed. Response content: {json.dumps(result, indent=2)}") output_url = result.get('data', {}).get('output') if not output_url or not isinstance(output_url, str): - logger.error(f"完整的 API 响应: {json.dumps(result, indent=2)}") - raise Exception(f"无法获取有效的输出图像 URL。API 响应中没有预期的数据结构。完整响应: {json.dumps(result, indent=2)}") + logger.error(f"Complete API response: {json.dumps(result, indent=2)}") + raise Exception(f"Unable to get valid output image URL. API response does not have expected data structure. Complete response: {json.dumps(result, indent=2)}") - logger.info(f"获取到的输出 URL: {output_url}") + logger.info(f"Obtained output URL: {output_url}") - # 验证 URL 是否可访问 + # Verify URL is accessible try: url_check = requests.head(output_url) url_check.raise_for_status() except requests.RequestException as e: - logger.error(f"无法访问输出 URL: {str(e)}") - raise Exception(f"无法访问输出 URL: {str(e)}") + logger.error(f"Unable to access output URL: {str(e)}") + raise Exception(f"Unable to access output URL: {str(e)}") - # 添加延迟,等待 Replicate 处理完成 + # Add delay, wait for Replicate to process time.sleep(10) img_response = requests.get(output_url, stream=True) img_response.raise_for_status() - # 将图像数据转换为 PIL Image + # Convert image data to PIL Image img = Image.open(img_response.raw) - # 转换为 numpy 数组 + # Convert to numpy array img_np = np.array(img) - # 确保图像是 3 通道 RGB - if len(img_np.shape) == 2: # 灰度图像 + # Ensure image is 3 channel RGB + if len(img_np.shape) == 2: # Grayscale image img_np = np.stack([img_np] * 3, axis=-1) - elif img_np.shape[-1] == 4: # RGBA 图像 + elif img_np.shape[-1] == 4: # RGBA image img_np = img_np[:, :, :3] - # 转换为 float32 并归一化到 0-1 范围 + # Convert to float32 and normalize to 0-1 range img_np = img_np.astype(np.float32) / 255.0 - # 转换为 torch tensor，确保形状为 [B,H,W,C] - img_tensor = torch.from_numpy(img_np).unsqueeze(0) # 添加批次维度 + # Convert to torch tensor, ensuring shape is [B,H,W,C] + img_tensor = torch.from_numpy(img_np).unsqueeze(0) # Add batch dimension - logger.info(f"图像处理完成。输出张量形状: {img_tensor.shape}") + logger.info(f"Image processing completed. Output tensor shape: {img_tensor.shape}") return (img_tensor,) except Exception as e: - error_msg = f"图像生成过程中出错: {str(e)}" + error_msg = f"Error during image generation: {str(e)}" logger.error(error_msg) - logger.exception("详细错误信息:") - # 返回一个错误标记图像，确保形状为 [B,H,W,C] + logger.exception("Detailed error information:") + # Return an error marked image, ensuring shape is [B,H,W,C] error_image = torch.zeros((1, 100, 400, 3), dtype=torch.float32) return (error_image,) diff --git a/flowy/nodes_omost.py b/flowy/nodes_omost.py index 66df388..04101d8 100644 --- a/flowy/nodes_omost.py +++ b/flowy/nodes_omost.py @@ -130,7 +130,7 @@ class OmostLLMNode: try: generated_text = llm_request(prompt=prompt, llm_model=llm_model, system_prompt=system_prompt, api_key=api_key, max_tokens=4000, timeout=10) - # 如果生成的字符中包含了多余的字符，比如 "```json" 或者 "```"，则需要去掉改行 + # If the generated text contains extra characters, such as "```json" or "```", remove the line generated_text = generated_text.replace("```json", "").replace("```", "") try: @@ -301,7 +301,7 @@ class OmostToConditioning: ) -# 对于 LLM 动态生成的区域描述，该节点用于预览canvas的节点 +# For LLM-generated region descriptions, this node is used to preview the canvas class ComflowyOmostPreviewNode: @classmethod def INPUT_TYPES(s): @@ -329,7 +329,7 @@ class ComflowyOmostPreviewNode: ) -# 对于高级用户，可以直接编辑python代码，然后加载到这个节点中 +# For advanced users, you can directly edit the python code and load it into this node class ComflowyOmostLoadCanvasPythonCodeNode: """Load python code generated by Omost demo app.""" @@ -350,7 +350,7 @@ class ComflowyOmostLoadCanvasPythonCodeNode: canvas = OmostCanvas.from_python_code(python_str) return (canvas.process(),) -# 定义这个节点可以在后续直接做一个前端编辑器，用于编辑基于区域的条件 +# Define this node to allow for a frontend editor to edit the canvas conditions class ComflowyOmostLoadCanvasConditioningNode: @classmethod def INPUT_TYPES(s): diff --git a/flowy/nodes_upscale.py b/flowy/nodes_upscale.py index d14cc8d..96719f2 100644 --- a/flowy/nodes_upscale.py +++ b/flowy/nodes_upscale.py @@ -45,12 +45,12 @@ Nodes from https://comflowy.com: logger.error(error_msg) raise ValueError(error_msg) - logger.info(f"开始处理图像放大请求。scale_factor: {scale_factor}, model: {model}") + logger.info(f"Starting image upscale request. scale_factor: {scale_factor}, model: {model}") - # 处理输入图像 + # Process input image if isinstance(image, torch.Tensor): if image.dim() == 4: - image = image.squeeze(0) # 移除批次维度 + image = image.squeeze(0) # Remove batch dimension if image.shape[-1] == 3: image = (image.cpu().numpy() * 255).astype(np.uint8) elif image.shape[0] == 3: @@ -68,13 +68,13 @@ Nodes from https://comflowy.com: else: raise ValueError(f"Unsupported image type: {type(image)}") - # 将输入图像转换为 JPEG 格式并压缩 + # Convert input image to JPEG format and compress buffered = io.BytesIO() Image.fromarray(image).save(buffered, format="JPEG", quality=85) img_str = base64.b64encode(buffered.getvalue()).decode() try: - # 使用 API_HOST 构建 API 请求的 URL + # Build the URL for the API request response = requests.post( f"{API_HOST}/api/open/v0/upscale", headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}, @@ -87,62 +87,62 @@ Nodes from https://comflowy.com: response.raise_for_status() result = response.json() - logger.info(f"API 请求完成。状态码: {response.status_code}") - logger.debug(f"API 响应内容: {json.dumps(result, indent=2)}") + logger.info(f"API request completed. Status code: {response.status_code}") + logger.debug(f"API response content: {json.dumps(result, indent=2)}") if not result.get('success'): - logger.error(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}") - raise Exception(f"API 请求失败。响应内容: {json.dumps(result, indent=2)}") + logger.error(f"API request failed. Response content: {json.dumps(result, indent=2)}") + raise Exception(f"API request failed. Response content: {json.dumps(result, indent=2)}") output_url = result.get('data', {}).get('output', [None])[0] if not output_url: - logger.error(f"完整的 API 响应: {json.dumps(result, indent=2)}") - raise Exception(f"无法获取输出图像 URL。API 响应中没有预期的数据结构。完整响应: {json.dumps(result, indent=2)}") + logger.error(f"Complete API response: {json.dumps(result, indent=2)}") + raise Exception(f"Unable to get valid output image URL. API response does not have expected data structure. Complete response: {json.dumps(result, indent=2)}") - logger.info(f"获取到的输出 URL: {output_url}") + logger.info(f"Obtained output URL: {output_url}") - # 验证 URL 是否可访问 + # Verify URL is accessible try: url_check = requests.head(output_url) url_check.raise_for_status() except requests.RequestException as e: - logger.error(f"无法访问输出 URL: {str(e)}") - raise Exception(f"无法访问输出 URL: {str(e)}") + logger.error(f"Unable to access output URL: {str(e)}") + raise Exception(f"Unable to access output URL: {str(e)}") - # 添加延迟,等待 Replicate 处理完成 + # Add delay, wait for Replicate to process time.sleep(10) img_response = requests.get(output_url, stream=True) img_response.raise_for_status() - # 将图像数据转换为 PIL Image + # Convert image data to PIL Image img = Image.open(img_response.raw) - # 转换为 numpy 数组 + # Convert image data to numpy array img_np = np.array(img) - # 确保图像是 3 通道 RGB - if len(img_np.shape) == 2: # 灰度图像 + # Ensure image is 3 channel RGB + if len(img_np.shape) == 2: # Grayscale image img_np = np.stack([img_np] * 3, axis=-1) - elif img_np.shape[-1] == 4: # RGBA 图像 + elif img_np.shape[-1] == 4: # RGBA image img_np = img_np[:, :, :3] - # 转换为 float32 并归一化到 0-1 范围 + # Convert to float32 and normalize to 0-1 range img_np = img_np.astype(np.float32) / 255.0 - # 转换为 torch tensor，确保形状为 [B,H,W,C] - img_tensor = torch.from_numpy(img_np).unsqueeze(0) # 添加批次维度 + # Convert to torch tensor, ensuring shape is [B,H,W,C] + img_tensor = torch.from_numpy(img_np).unsqueeze(0) # Add batch dimension - logger.info(f"图像处理完成。输出张量形状: {img_tensor.shape}") - logger.info(f"API 请求完成。状态码: {response.status_code}") - logger.debug(f"API 响应内容: {response.text}") + logger.info(f"Image processing completed. Output tensor shape: {img_tensor.shape}") + logger.info(f"API request completed. Status code: {response.status_code}") + logger.debug(f"API response content: {response.text}") return (img_tensor,) except Exception as e: - error_msg = f"放大过程中出错: {str(e)}" + error_msg = f"Error during image upscale: {str(e)}" logger.error(error_msg) - logger.exception("详细错误信息:") - # 返回一个错误标记图像，确保形状为 [B,H,W,C] + logger.exception("Detailed error information:") + # Return an error marked image, ensuring shape is [B,H,W,C] error_image = torch.zeros((1, 100, 400, 3), dtype=torch.float32) return (error_image,) diff --git a/flowy/types.py b/flowy/types.py index 80cb63e..56cf340 100644 --- a/flowy/types.py +++ b/flowy/types.py @@ -1,7 +1,7 @@ import sys -# API_HOST = "https://app.comflowy.com" -API_HOST = "http://127.0.0.1:3000" +API_HOST = "https://app.comflowy.com" +# API_HOST = "http://127.0.0.1:3000" FLOAT = ( "FLOAT", {"default": 1, "min": -sys.float_info.max, "max": sys.float_info.max, "step": 0.01}, @@ -38,6 +38,8 @@ LLM_MODELS = [ "internlm/internlm2_5-7b-chat" ] +SAFETY_TOLERANCE = ["1", "2", "3", "4", "5"] + class AnyType(str): """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""