From 1065c0ae22cdea297bb68d5da827fe3097d6c07b Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Mon, 10 Jun 2024 17:53:15 +0800
Subject: [PATCH 01/30] add textract node

---
 nodes/textract.py | 136 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 136 insertions(+)
 create mode 100644 nodes/textract.py

diff --git a/nodes/textract.py b/nodes/textract.py
new file mode 100644
index 0000000..9eed3f5
--- /dev/null
+++ b/nodes/textract.py
@@ -0,0 +1,136 @@
+import json
+import os
+import re
+import base64
+from io import BytesIO
+import requests
+from retry import retry
+import boto3
+from PIL import Image
+import numpy as np
+import torch
+import cv2
+
+
+from .session import get_client
+
+
+s3_client = get_client(service_name="textract")
+
+MAX_RETRY = 3
+
+
+
+
+class ImageOCRByTextract:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",)
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+    OUTPUT_NODE = True
+
+    def ocr_by_textract(self,image_file):
+    # 获取图像原始尺寸
+        with Image.open(image_file) as img:
+            width, height = img.size
+
+        # 调用Textract DetectDocumentText函数
+        with open(image_file, 'rb') as file:
+            img_test = file.read()
+            response = textract.detect_document_text(Document={'Bytes': img_test})
+
+        # 初始化结果列表
+        result = []
+
+        # 提取文本和边界框信息
+        for item in response['Blocks']:
+            if item['BlockType'] == 'LINE':
+                text = item['Text']
+                box = item['Geometry']['BoundingBox']
+                left = int(box['Left'] * width)
+                top = int(box['Top'] * height)
+                width = int(box['Width'] )
+                height = int(box['Height'])
+
+                # 将信息添加到结果列表中
+                result.append({
+                    'Text': text,
+                    'BoundingBox': {
+                        'Left': left,
+                        'Top': top,
+                        'Width': width,
+                        'Height': height
+                    }
+                })
+
+        # 将结果列表转换为JSON格式
+        json_result = json.dumps(result, indent=2)
+        return json_result
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image):
+        return self.ocr_by_textract(image)
+
+
+NODE_CLASS_MAPPINGS = {
+    "Image OCR By Textract": ImageOCRByTextract
+}
+
+
+class ImageToMaskByClip:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "position_info": ("STRING", ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+    OUTPUT_NODE = True
+
+    def genMaskByClip(self,image_file,position_info):
+        position=json.loads(position_info)
+        left = position['Left']
+        top = position['Top']
+        width = position['Width']
+        height = position['Height']
+        # 读取原始图像
+        img = cv2.imread('image.jpg')
+
+        # 指定矩形框的左上角和右下角坐标
+        x1, y1 = int(left), int(top)
+        x2, y2 = int(left + width), int(top + height)
+
+        # 创建一个与原始图像大小相同的黑色遮罩图像
+        mask = np.zeros_like(img)
+
+        # 在遮罩图像上绘制白色矩形框
+        mask = cv2.rectangle(mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+
+        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
+        masked_img = cv2.bitwise_and(img, mask)
+        image = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(
+                    0
+                )
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image, position_info):
+        return self.genMaskByClip(image,position_info)
+
+
+    NODE_CLASS_MAPPINGS = {
+        "get mask by textract clip": ImageToMaskByClip
+    }
+
+

From c8ec86defbb9083f710401358117074fe367fd37 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Tue, 11 Jun 2024 08:50:48 +0800
Subject: [PATCH 02/30] update opencv customer node

---
 nodes/textract.py | 73 ++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 62 insertions(+), 11 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 9eed3f5..d042b7e 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -19,8 +19,60 @@ s3_client = get_client(service_name="textract")
 
 MAX_RETRY = 3
 
+class GetDominatColor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",)
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("color")
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+    OUTPUT_NODE = True
+
+    def get_dominant_color(self, image, box):
+        """
+        获取图像中指定边界框区域内的主要颜色,并返回 #RRGGBB 格式的颜色字符串
+        参数:
+            image: 输入图像
+            box: 边界框坐标,格式为(left, top, width, height)
+        返回值:
+            主要颜色的 #RRGGBB 格式字符串
+        """
+
+        # 从边界框坐标构造ROI区域
+        position = json.loads(box)['BoundingBox']
+        left = position['left']
+        top = position['top']
+        width = position['width']
+        height = position['height']
+
+        roi = image[top:top+height, left:left+width]
+
+        # 计算ROI区域的像素值直方图
+        pixels = np.float32(roi.reshape(-1, 3))
+        n_colors = 8
+        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
+        _, _, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
+
+        # 获取最多像素对应的颜色
+        counts = np.histogram(pixels, bins=np.arange(n_colors+1))[0]
+        dominant_index = np.argmax(counts)
+        dominant_color = palette[dominant_index].astype(np.uint8)
+
+        # 转换为 #RRGGBB 格式的字符串
+        hex_color = '#%02x%02x%02x' % (int(dominant_color[0]), int(dominant_color[1]), int(dominant_color[2]))
+
+        return hex_color
 
 
+    @retry(tries=MAX_RETRY)
+    def forward(self, image,box):
+        return self.ocr_by_textract(image)
 
 class ImageOCRByTextract:
     @classmethod
@@ -32,6 +84,7 @@ class ImageOCRByTextract:
         }
 
     RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("OCR result")
     FUNCTION = "forward"
     CATEGORY = "aws"
     OUTPUT_NODE = True
@@ -56,8 +109,8 @@ class ImageOCRByTextract:
                 box = item['Geometry']['BoundingBox']
                 left = int(box['Left'] * width)
                 top = int(box['Top'] * height)
-                width = int(box['Width'] )
-                height = int(box['Height'])
+                width = int(box['Width'] * width))
+                height = int(box['Height']* height)
 
                 # 将信息添加到结果列表中
                 result.append({
@@ -79,9 +132,7 @@ class ImageOCRByTextract:
         return self.ocr_by_textract(image)
 
 
-NODE_CLASS_MAPPINGS = {
-    "Image OCR By Textract": ImageOCRByTextract
-}
+
 
 
 class ImageToMaskByClip:
@@ -100,7 +151,7 @@ class ImageToMaskByClip:
     OUTPUT_NODE = True
 
     def genMaskByClip(self,image_file,position_info):
-        position=json.loads(position_info)
+        position=json.loads(position_info)['BoundingBox']
         left = position['Left']
         top = position['Top']
         width = position['Width']
@@ -128,9 +179,9 @@ class ImageToMaskByClip:
     def forward(self, image, position_info):
         return self.genMaskByClip(image,position_info)
 
-
-    NODE_CLASS_MAPPINGS = {
-        "get mask by textract clip": ImageToMaskByClip
-    }
-
+NODE_CLASS_MAPPINGS = {
+    "Image OCR By Textract": ImageOCRByTextract,
+    "Get Dominat Color": GetDominatColor,
+    "get mask by textract clip": ImageToMaskByClip
+}
 

From 0e49c3f7a32b9ab3931f1526e88b0f23e48e6f86 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Tue, 11 Jun 2024 13:22:19 +0800
Subject: [PATCH 03/30] update opencv customer node

---
 nodes/textract.py | 141 +++++++++-------------------------------------
 1 file changed, 27 insertions(+), 114 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index d042b7e..1b0719c 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -15,65 +15,10 @@ import cv2
 from .session import get_client
 
 
-s3_client = get_client(service_name="textract")
+textract = get_client(service_name="textract")
 
 MAX_RETRY = 3
 
-class GetDominatColor:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",)
-            }
-        }
-
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("color")
-    FUNCTION = "forward"
-    CATEGORY = "aws"
-    OUTPUT_NODE = True
-
-    def get_dominant_color(self, image, box):
-        """
-        获取图像中指定边界框区域内的主要颜色,并返回 #RRGGBB 格式的颜色字符串
-        参数:
-            image: 输入图像
-            box: 边界框坐标,格式为(left, top, width, height)
-        返回值:
-            主要颜色的 #RRGGBB 格式字符串
-        """
-
-        # 从边界框坐标构造ROI区域
-        position = json.loads(box)['BoundingBox']
-        left = position['left']
-        top = position['top']
-        width = position['width']
-        height = position['height']
-
-        roi = image[top:top+height, left:left+width]
-
-        # 计算ROI区域的像素值直方图
-        pixels = np.float32(roi.reshape(-1, 3))
-        n_colors = 8
-        criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 200, .1)
-        _, _, palette = cv2.kmeans(pixels, n_colors, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
-
-        # 获取最多像素对应的颜色
-        counts = np.histogram(pixels, bins=np.arange(n_colors+1))[0]
-        dominant_index = np.argmax(counts)
-        dominant_color = palette[dominant_index].astype(np.uint8)
-
-        # 转换为 #RRGGBB 格式的字符串
-        hex_color = '#%02x%02x%02x' % (int(dominant_color[0]), int(dominant_color[1]), int(dominant_color[2]))
-
-        return hex_color
-
-
-    @retry(tries=MAX_RETRY)
-    def forward(self, image,box):
-        return self.ocr_by_textract(image)
-
 class ImageOCRByTextract:
     @classmethod
     def INPUT_TYPES(s):
@@ -83,24 +28,27 @@ class ImageOCRByTextract:
             }
         }
 
-    RETURN_TYPES = ("STRING",)
-    RETURN_NAMES = ("OCR result")
+    RETURN_TYPES = ("STRING","IMAGE")
+    RETURN_NAMES = ("Position Info","Mask Image")
     FUNCTION = "forward"
     CATEGORY = "aws"
     OUTPUT_NODE = True
 
-    def ocr_by_textract(self,image_file):
-    # 获取图像原始尺寸
-        with Image.open(image_file) as img:
-            width, height = img.size
+    def ocr_by_textract(self,image_input):
+        image = image_input[0] * 255.0
+        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+        # 获取图像原始尺寸
+        width, height = image.size
 
         # 调用Textract DetectDocumentText函数
-        with open(image_file, 'rb') as file:
-            img_test = file.read()
-            response = textract.detect_document_text(Document={'Bytes': img_test})
+        img_test = image.read()
+        response = textract.detect_document_text(Document={'Bytes': img_test})
 
         # 初始化结果列表
         result = []
+        # 创建一个与原始图像大小相同的黑色遮罩图像
+        mask = np.zeros_like(img)
+
 
         # 提取文本和边界框信息
         for item in response['Blocks']:
@@ -122,10 +70,23 @@ class ImageOCRByTextract:
                         'Height': height
                     }
                 })
+                # 对每个文本信息框绘制mask遮罩
+                # 指定矩形框的左上角和右下角坐标
+                x1, y1 = int(left), int(top)
+                x2, y2 = int(left + width), int(top + height)
+                # 在遮罩图像上绘制白色矩形框
+                mask = cv2.rectangle(mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
 
         # 将结果列表转换为JSON格式
         json_result = json.dumps(result, indent=2)
-        return json_result
+        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
+        masked_img = cv2.bitwise_and(img, mask)
+                        image = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(
+                                            0
+                                        )
+
+        return json_result,masked_img
+
 
     @retry(tries=MAX_RETRY)
     def forward(self, image):
@@ -133,55 +94,7 @@ class ImageOCRByTextract:
 
 
 
-
-
-class ImageToMaskByClip:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "position_info": ("STRING", ),
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "forward"
-    CATEGORY = "aws"
-    OUTPUT_NODE = True
-
-    def genMaskByClip(self,image_file,position_info):
-        position=json.loads(position_info)['BoundingBox']
-        left = position['Left']
-        top = position['Top']
-        width = position['Width']
-        height = position['Height']
-        # 读取原始图像
-        img = cv2.imread('image.jpg')
-
-        # 指定矩形框的左上角和右下角坐标
-        x1, y1 = int(left), int(top)
-        x2, y2 = int(left + width), int(top + height)
-
-        # 创建一个与原始图像大小相同的黑色遮罩图像
-        mask = np.zeros_like(img)
-
-        # 在遮罩图像上绘制白色矩形框
-        mask = cv2.rectangle(mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
-
-        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
-        masked_img = cv2.bitwise_and(img, mask)
-        image = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(
-                    0
-                )
-
-    @retry(tries=MAX_RETRY)
-    def forward(self, image, position_info):
-        return self.genMaskByClip(image,position_info)
-
 NODE_CLASS_MAPPINGS = {
     "Image OCR By Textract": ImageOCRByTextract,
-    "Get Dominat Color": GetDominatColor,
-    "get mask by textract clip": ImageToMaskByClip
 }
 

From d928e317c3e5d8b12082614f06bd5ccf41787133 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Tue, 11 Jun 2024 14:56:33 +0800
Subject: [PATCH 04/30] add anyText model node

---
 nodes/anytext.py | 63 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 nodes/anytext.py

diff --git a/nodes/anytext.py b/nodes/anytext.py
new file mode 100644
index 0000000..c3e4a78
--- /dev/null
+++ b/nodes/anytext.py
@@ -0,0 +1,63 @@
+from modelscope.pipelines import pipeline
+from util import save_images
+import json
+import os
+import re
+import base64
+from io import BytesIO
+import requests
+from retry import retry
+import boto3
+from PIL import Image
+import numpy as np
+import torch
+import cv2
+
+class AnyTextGen:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "ori_image": ("IMAGE",),
+                "mask_image":("IMAGE",),
+                "prompt":("STRING",)
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE")
+    RETURN_NAMES = ("Converted Image")
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+    OUTPUT_NODE = True
+
+    def any_text_edit_image(self,image_input):
+        pipe = pipeline('my-anytext-task', model='damo/cv_anytext_text_generation_editing', model_revision='v1.1.1', use_fp16=True, use_translator=False)
+        img_save_folder = "SaveImages"
+        params = {
+            "show_debug": True,
+            "image_count": 1,
+            "ddim_steps": 20,
+        }
+
+        mode = 'text-editing'
+        input_data = {
+            "prompt": prompt,
+            "seed": 8943410,
+            "draw_pos": ori_image,
+            "ori_image": mask_image
+        }
+        results, rtn_code, rtn_warning, debug_info = pipe(input_data, mode=mode, **params)
+        image = torch.from_numpy(np.array(results[0]).astype(np.float32) / 255.0).unsqueeze(
+                    0
+                )
+        return image
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image):
+        return self.any_text_edit_image(image)
+
+
+
+NODE_CLASS_MAPPINGS = {
+    "AnyText Gen": AnyTextGen,
+}
\ No newline at end of file

From 690ab6cd2087792a13ce28c5bb98ff35ab46de28 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Tue, 11 Jun 2024 20:38:06 +0800
Subject: [PATCH 05/30] add anyText model node

---
 nodes/textract.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 1b0719c..195d87a 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -57,7 +57,7 @@ class ImageOCRByTextract:
                 box = item['Geometry']['BoundingBox']
                 left = int(box['Left'] * width)
                 top = int(box['Top'] * height)
-                width = int(box['Width'] * width))
+                width = int(box['Width'] * width)
                 height = int(box['Height']* height)
 
                 # 将信息添加到结果列表中
@@ -80,10 +80,8 @@ class ImageOCRByTextract:
         # 将结果列表转换为JSON格式
         json_result = json.dumps(result, indent=2)
         # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
-        masked_img = cv2.bitwise_and(img, mask)
-                        image = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(
-                                            0
-                                        )
+        masked_img = cv2.bitwise_and(image, mask)
+        masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
 
         return json_result,masked_img
 
@@ -95,6 +93,6 @@ class ImageOCRByTextract:
 
 
 NODE_CLASS_MAPPINGS = {
-    "Image OCR By Textract": ImageOCRByTextract,
+    "Image OCR By Textract": ImageOCRByTextract
 }
 

From 1b4e3d16689cc6dcb2c67f48b30f7a0990e1adaa Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 13 Jun 2024 13:02:59 +0800
Subject: [PATCH 06/30] udpate textract node

---
 nodes/anytext.py                     |   9 +-
 nodes/bedrock.py                     |   3 +-
 nodes/prompts.py                     |   1 +
 nodes/textract.py                    |  50 ++--
 workflows/claude+t2i.json            | 422 ---------------------------
 workflows/claude3_image_caption.json | 186 ------------
 6 files changed, 35 insertions(+), 636 deletions(-)
 delete mode 100644 workflows/claude+t2i.json
 delete mode 100644 workflows/claude3_image_caption.json

diff --git a/nodes/anytext.py b/nodes/anytext.py
index c3e4a78..e950b16 100644
--- a/nodes/anytext.py
+++ b/nodes/anytext.py
@@ -1,5 +1,4 @@
 from modelscope.pipelines import pipeline
-from util import save_images
 import json
 import os
 import re
@@ -47,17 +46,15 @@ class AnyTextGen:
             "ori_image": mask_image
         }
         results, rtn_code, rtn_warning, debug_info = pipe(input_data, mode=mode, **params)
-        image = torch.from_numpy(np.array(results[0]).astype(np.float32) / 255.0).unsqueeze(
-                    0
-                )
+        image = torch.from_numpy(np.array(results[0]).astype(np.float32) / 255.0).unsqueeze(0)
         return image
 
-    @retry(tries=MAX_RETRY)
+    @retry(tries=3)
     def forward(self, image):
         return self.any_text_edit_image(image)
 
 
 
 NODE_CLASS_MAPPINGS = {
-    "AnyText Gen": AnyTextGen,
+    "AnyText Gen": AnyTextGen
 }
\ No newline at end of file
diff --git a/nodes/bedrock.py b/nodes/bedrock.py
index 3fc41ec..ed12df0 100644
--- a/nodes/bedrock.py
+++ b/nodes/bedrock.py
@@ -250,7 +250,7 @@ class BedrockClaude:
         # The different model providers have individual request and response formats.
         # For the format, ranges, and default values for Anthropic Claude, refer to:
         # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
-
+        print("prompt input:",prompt)
         body = json.dumps(
             {
                 "anthropic_version": "bedrock-2023-05-31",
@@ -278,6 +278,7 @@ class BedrockClaude:
             modelId=model_id,
         )
         message = json.loads(response.get("body").read())["content"][0]["text"]
+        print("output message:",message)
 
         return (message,)
 
diff --git a/nodes/prompts.py b/nodes/prompts.py
index 69814c3..ae62467 100644
--- a/nodes/prompts.py
+++ b/nodes/prompts.py
@@ -18,6 +18,7 @@ class PromptTemplate:
 
     def forward(self, prompt, prompt_template):
         output = prompt_template.replace("{prompt}", prompt).replace("[prompt]", prompt)
+        #print("output prompt:",output)
         return (output,)
 
 
diff --git a/nodes/textract.py b/nodes/textract.py
index 195d87a..484d5b1 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -3,6 +3,7 @@ import os
 import re
 import base64
 from io import BytesIO
+import io
 import requests
 from retry import retry
 import boto3
@@ -19,6 +20,8 @@ textract = get_client(service_name="textract")
 
 MAX_RETRY = 3
 
+
+
 class ImageOCRByTextract:
     @classmethod
     def INPUT_TYPES(s):
@@ -28,26 +31,30 @@ class ImageOCRByTextract:
             }
         }
 
-    RETURN_TYPES = ("STRING","IMAGE")
-    RETURN_NAMES = ("Position Info","Mask Image")
+    RETURN_TYPES = ("STRING","INT","INT","INT","INT","IMAGE")
+    RETURN_NAMES = ("Text","Left","Top","Width","Height","Mask Image")
     FUNCTION = "forward"
     CATEGORY = "aws"
     OUTPUT_NODE = True
 
     def ocr_by_textract(self,image_input):
+        numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
         image = image_input[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
         # 获取图像原始尺寸
-        width, height = image.size
+        img_width, img_height = image.size
 
         # 调用Textract DetectDocumentText函数
-        img_test = image.read()
-        response = textract.detect_document_text(Document={'Bytes': img_test})
+        byte_stream = io.BytesIO()
+        image.save(byte_stream, format='PNG')
+        byte_image = byte_stream.getvalue()
+        response = textract.detect_document_text(Document={'Bytes': byte_image})
 
         # 初始化结果列表
         result = []
         # 创建一个与原始图像大小相同的黑色遮罩图像
-        mask = np.zeros_like(img)
+        mask = np.zeros_like(numpy_image)
+        all_text=""
 
 
         # 提取文本和边界框信息
@@ -55,35 +62,36 @@ class ImageOCRByTextract:
             if item['BlockType'] == 'LINE':
                 text = item['Text']
                 box = item['Geometry']['BoundingBox']
-                left = int(box['Left'] * width)
-                top = int(box['Top'] * height)
-                width = int(box['Width'] * width)
-                height = int(box['Height']* height)
+                left = int(box['Left'] * img_width)
+                top = int(box['Top'] * img_height)
+                width = int(box['Width'] * img_width)
+                height = int(box['Height']* img_height)
 
                 # 将信息添加到结果列表中
                 result.append({
                     'Text': text,
-                    'BoundingBox': {
-                        'Left': left,
-                        'Top': top,
-                        'Width': width,
-                        'Height': height
-                    }
+                    'Left': left,
+                    'Top': top,
+                    'Width': width,
+                    'Height': height
+
                 })
+                all_text=all_text+text
                 # 对每个文本信息框绘制mask遮罩
                 # 指定矩形框的左上角和右下角坐标
+                temp_mask = np.zeros_like(numpy_image)
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
                 # 在遮罩图像上绘制白色矩形框
-                mask = cv2.rectangle(mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+                mask = cv2.bitwise_or(mask, temp_mask)
 
-        # 将结果列表转换为JSON格式
-        json_result = json.dumps(result, indent=2)
         # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
-        masked_img = cv2.bitwise_and(image, mask)
+        masked_img = cv2.bitwise_and(numpy_image, mask)
         masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
+        print("result",result)
 
-        return json_result,masked_img
+        return all_text,result[0]['Left'],result[0]['Top'],result[0]['Width'],result[0]['Height'],masked_img
 
 
     @retry(tries=MAX_RETRY)
diff --git a/workflows/claude+t2i.json b/workflows/claude+t2i.json
deleted file mode 100644
index 54c391b..0000000
--- a/workflows/claude+t2i.json
+++ /dev/null
@@ -1,422 +0,0 @@
-{
-  "last_node_id": 34,
-  "last_link_id": 48,
-  "nodes": [
-    {
-      "id": 29,
-      "type": "Bedrock - SDXL",
-      "pos": [
-        1251,
-        368
-      ],
-      "size": {
-        "0": 400,
-        "1": 268
-      },
-      "flags": {},
-      "order": 4,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "prompt",
-          "type": "STRING",
-          "link": 47,
-          "widget": {
-            "name": "prompt"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            33
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "Bedrock - SDXL"
-      },
-      "widgets_values": [
-        "",
-        "1024 x 1024",
-        "cinematic",
-        6.978,
-        20,
-        "NONE",
-        "Auto",
-        0,
-        "fixed"
-      ]
-    },
-    {
-      "id": 33,
-      "type": "PreviewImage",
-      "pos": [
-        1701.8984375,
-        650.9765625
-      ],
-      "size": {
-        "0": 395.65234375,
-        "1": 415.90234375
-      },
-      "flags": {},
-      "order": 7,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 41
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "PreviewImage"
-      }
-    },
-    {
-      "id": 32,
-      "type": "Bedrock - Titan Image",
-      "pos": [
-        1699.7958090311772,
-        368.64602020487723
-      ],
-      "size": {
-        "0": 400,
-        "1": 220.00001525878906
-      },
-      "flags": {},
-      "order": 5,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "prompt",
-          "type": "STRING",
-          "link": 48,
-          "widget": {
-            "name": "prompt"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            41
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "Bedrock - Titan Image"
-      },
-      "widgets_values": [
-        "",
-        1,
-        "premium",
-        "1024 x 1024",
-        8,
-        0,
-        "fixed"
-      ]
-    },
-    {
-      "id": 27,
-      "type": "PreviewImage",
-      "pos": [
-        1254,
-        677
-      ],
-      "size": {
-        "0": 395.65234375,
-        "1": 415.90234375
-      },
-      "flags": {},
-      "order": 6,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 33
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "PreviewImage"
-      }
-    },
-    {
-      "id": 8,
-      "type": "ShowText|pysssss",
-      "pos": [
-        824,
-        904
-      ],
-      "size": {
-        "0": 367.124755859375,
-        "1": 159.00286865234375
-      },
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "text",
-          "type": "STRING",
-          "link": 46,
-          "widget": {
-            "name": "text"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": null,
-          "shape": 6,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ShowText|pysssss"
-      },
-      "widgets_values": [
-        "",
-        "A playful puppy with a soft, fluffy coat frolics in a lush, verdant meadow, its bright eyes filled with curiosity and its tail wagging joyfully as it explores the vibrant, sun-dappled landscape."
-      ]
-    },
-    {
-      "id": 23,
-      "type": "Prompt Template",
-      "pos": [
-        820.4378327684955,
-        369.0918288922683
-      ],
-      "size": {
-        "0": 376.08880615234375,
-        "1": 212.24671936035156
-      },
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "prompt",
-          "type": "STRING",
-          "link": 17,
-          "widget": {
-            "name": "prompt"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            45
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "Prompt Template"
-      },
-      "widgets_values": [
-        "",
-        "Describe a beautiful picture in detail using one sentence of \"[prompt]\", please answer in English words only, skip the preamble."
-      ]
-    },
-    {
-      "id": 34,
-      "type": "Bedrock - Claude",
-      "pos": [
-        825,
-        632
-      ],
-      "size": [
-        364.671875,
-        196
-      ],
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "prompt",
-          "type": "STRING",
-          "link": 45,
-          "widget": {
-            "name": "prompt"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            46,
-            47,
-            48
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "Bedrock - Claude"
-      },
-      "widgets_values": [
-        "",
-        "anthropic.claude-3-haiku-20240307-v1:0",
-        200,
-        0.5,
-        1,
-        250
-      ]
-    },
-    {
-      "id": 5,
-      "type": "Text Multiline",
-      "pos": [
-        362,
-        368
-      ],
-      "size": {
-        "0": 379.65625,
-        "1": 198.87109375
-      },
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            17
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "Text Multiline"
-      },
-      "widgets_values": [
-        "一只小狗"
-      ]
-    }
-  ],
-  "links": [
-    [
-      17,
-      5,
-      0,
-      23,
-      0,
-      "STRING"
-    ],
-    [
-      33,
-      29,
-      0,
-      27,
-      0,
-      "IMAGE"
-    ],
-    [
-      41,
-      32,
-      0,
-      33,
-      0,
-      "IMAGE"
-    ],
-    [
-      45,
-      23,
-      0,
-      34,
-      0,
-      "STRING"
-    ],
-    [
-      46,
-      34,
-      0,
-      8,
-      0,
-      "STRING"
-    ],
-    [
-      47,
-      34,
-      0,
-      29,
-      0,
-      "STRING"
-    ],
-    [
-      48,
-      34,
-      0,
-      32,
-      0,
-      "STRING"
-    ]
-  ],
-  "groups": [
-    {
-      "title": "Bedrock - Cluade",
-      "bounding": [
-        810,
-        295,
-        396,
-        790
-      ],
-      "color": "#b58b2a",
-      "font_size": 24,
-      "locked": false
-    },
-    {
-      "title": "Bedrock - TitanImage",
-      "bounding": [
-        1690,
-        295,
-        420,
-        782
-      ],
-      "color": "#88A",
-      "font_size": 24,
-      "locked": false
-    },
-    {
-      "title": "Bedrock - SDXL",
-      "bounding": [
-        1241,
-        294,
-        422,
-        814
-      ],
-      "color": "#a1309b",
-      "font_size": 24,
-      "locked": false
-    }
-  ],
-  "config": {},
-  "extra": {
-    "workspace_info": {
-      "id": "c350a89c-0edd-4bef-9fc6-f83c239228a8"
-    }
-  },
-  "version": 0.4
-}
\ No newline at end of file
diff --git a/workflows/claude3_image_caption.json b/workflows/claude3_image_caption.json
deleted file mode 100644
index 431c562..0000000
--- a/workflows/claude3_image_caption.json
+++ /dev/null
@@ -1,186 +0,0 @@
-{
-  "last_node_id": 5,
-  "last_link_id": 3,
-  "nodes": [
-    {
-      "id": 1,
-      "type": "Image From URL",
-      "pos": [
-        68,
-        283
-      ],
-      "size": {
-        "0": 315,
-        "1": 58
-      },
-      "flags": {},
-      "order": 0,
-      "mode": 0,
-      "outputs": [
-        {
-          "name": "IMAGE",
-          "type": "IMAGE",
-          "links": [
-            1,
-            2
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "Image From URL"
-      },
-      "widgets_values": [
-        "https://upload.wikimedia.org/wikipedia/commons/a/a7/Camponotus_flavomarginatus_ant.jpg"
-      ]
-    },
-    {
-      "id": 3,
-      "type": "Bedrock - Claude Multimodal",
-      "pos": [
-        461,
-        123
-      ],
-      "size": {
-        "0": 400,
-        "1": 200
-      },
-      "flags": {},
-      "order": 2,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "image",
-          "type": "IMAGE",
-          "link": 2
-        }
-      ],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": [
-            3
-          ],
-          "shape": 3,
-          "slot_index": 0
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "Bedrock - Claude Multimodal"
-      },
-      "widgets_values": [
-        "Describe this image.",
-        "anthropic.claude-3-haiku-20240307-v1:0",
-        2048,
-        0.5,
-        1,
-        250
-      ]
-    },
-    {
-      "id": 2,
-      "type": "PreviewImage",
-      "pos": [
-        484,
-        400
-      ],
-      "size": [
-        266.63671875,
-        246
-      ],
-      "flags": {},
-      "order": 1,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "images",
-          "type": "IMAGE",
-          "link": 1
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "PreviewImage"
-      }
-    },
-    {
-      "id": 5,
-      "type": "ShowText|pysssss",
-      "pos": [
-        916,
-        154
-      ],
-      "size": [
-        323.16015625,
-        179.2890625
-      ],
-      "flags": {},
-      "order": 3,
-      "mode": 0,
-      "inputs": [
-        {
-          "name": "text",
-          "type": "STRING",
-          "link": 3,
-          "widget": {
-            "name": "text"
-          }
-        }
-      ],
-      "outputs": [
-        {
-          "name": "STRING",
-          "type": "STRING",
-          "links": null,
-          "shape": 6
-        }
-      ],
-      "properties": {
-        "Node name for S&R": "ShowText|pysssss"
-      },
-      "widgets_values": [
-        "",
-        "The image shows a close-up view of a black ant on a textured surface. The ant appears to be in a crouched or alert position, with its legs and antennae extended. The lighting and focus create a dramatic, almost cinematic effect, highlighting the intricate details of the ant's body and the rough texture of the surface it is standing on."
-      ]
-    }
-  ],
-  "links": [
-    [
-      1,
-      1,
-      0,
-      2,
-      0,
-      "IMAGE"
-    ],
-    [
-      2,
-      1,
-      0,
-      3,
-      0,
-      "IMAGE"
-    ],
-    [
-      3,
-      3,
-      0,
-      5,
-      0,
-      "STRING"
-    ]
-  ],
-  "groups": [],
-  "config": {},
-  "extra": {
-    "ds": {
-      "scale": 1,
-      "offset": [
-        -0.125,
-        0.2421875
-      ]
-    }
-  },
-  "version": 0.4
-}
\ No newline at end of file

From 1c33cac806ad1b734f61aa22069990ce9d9ccd26 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 11 Jul 2024 13:56:06 +0800
Subject: [PATCH 07/30] udpate textract node

---
 nodes/textract.py | 87 ++++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 82 insertions(+), 5 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 484d5b1..65736f4 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -11,17 +11,16 @@ from PIL import Image
 import numpy as np
 import torch
 import cv2
-
-
+import folder_paths
 from .session import get_client
 
 
+current_directory = os.path.dirname(os.path.abspath(__file__))
+temp_img_path = os.path.join(current_directory, "temp_dir", "AnyText_manual_mask_pos_img.png")
 textract = get_client(service_name="textract")
-
 MAX_RETRY = 3
 
 
-
 class ImageOCRByTextract:
     @classmethod
     def INPUT_TYPES(s):
@@ -89,9 +88,10 @@ class ImageOCRByTextract:
         # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
         masked_img = cv2.bitwise_and(numpy_image, mask)
         masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
+
         print("result",result)
 
-        return all_text,result[0]['Left'],result[0]['Top'],result[0]['Width'],result[0]['Height'],masked_img
+        return all_text,temp_img_path
 
 
     @retry(tries=MAX_RETRY)
@@ -100,7 +100,84 @@ class ImageOCRByTextract:
 
 
 
+class ImageOCRByTextractV2:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",)
+            }
+        }
+
+    RETURN_TYPES = ("STRING","STRING","STRING")
+    RETURN_NAMES = ("Texts","Original Image Path","Mask Image Path")
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+    OUTPUT_NODE = True
+
+    def ocr_by_textract(self,image_input):
+        ori_image_path = folder_paths.get_annotated_filepath(image_input)
+        pos_img_path = os.path.join(temp_img_path)
+        numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
+        image = image_input[0] * 255.0
+        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+        # 获取图像原始尺寸
+        img_width, img_height = image.size
+
+        # 调用Textract DetectDocumentText函数
+        byte_stream = io.BytesIO()
+        image.save(byte_stream, format='PNG')
+        byte_image = byte_stream.getvalue()
+        response = textract.detect_document_text(Document={'Bytes': byte_image})
+
+        # 初始化结果列表
+        result = []
+        # 创建一个与原始图像大小相同的黑色遮罩图像
+        mask = np.zeros_like(numpy_image)
+        all_text=""
+
+
+        # 提取文本和边界框信息
+        for item in response['Blocks']:
+            if item['BlockType'] == 'LINE':
+                text = item['Text']
+                box = item['Geometry']['BoundingBox']
+                left = int(box['Left'] * img_width)
+                top = int(box['Top'] * img_height)
+                width = int(box['Width'] * img_width)
+                height = int(box['Height']* img_height)
+
+                # 将信息添加到结果列表中
+                result.append(text)
+                # 对每个文本信息框绘制mask遮罩
+                # 指定矩形框的左上角和右下角坐标
+                temp_mask = np.zeros_like(numpy_image)
+                x1, y1 = int(left), int(top)
+                x2, y2 = int(left + width), int(top + height)
+                # 在遮罩图像上绘制白色矩形框
+                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+                mask = cv2.bitwise_or(mask, temp_mask)
+
+        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
+        masked_img = cv2.bitwise_and(numpy_image, mask)
+        masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
+        masked_img.save(temp_img_path)
+        all_text="|".join(result)
+
+        print("result",result)
+
+        return all_text ,ori_image_path,temp_img_path
+
+
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image):
+        return self.ocr_by_textract(image)
+
+
 NODE_CLASS_MAPPINGS = {
     "Image OCR By Textract": ImageOCRByTextract
+    "Image OCR By Textract V2":ImageOCRByTextractV2
 }
 

From b74ebe04c71e9bddc5d2a9d94ce4343303548772 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Mon, 15 Jul 2024 10:36:48 +0800
Subject: [PATCH 08/30] udpate textract node

---
 nodes/textract.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 65736f4..829e530 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -82,7 +82,7 @@ class ImageOCRByTextract:
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
                 # 在遮罩图像上绘制白色矩形框
-                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (0, 0, 0), -1)
                 mask = cv2.bitwise_or(mask, temp_mask)
 
         # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
@@ -91,7 +91,7 @@ class ImageOCRByTextract:
 
         print("result",result)
 
-        return all_text,temp_img_path
+        return all_text,result[0]['Left'],result[0]['Top'],result[0]['Width'],result[0]['Height'],masked_img
 
 
     @retry(tries=MAX_RETRY)

From 38a45e35fb294ea207a4a582235ad3f00f1722fa Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Mon, 15 Jul 2024 11:08:48 +0800
Subject: [PATCH 09/30] udpate textract node

---
 nodes/textract.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 829e530..c92ced3 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -82,7 +82,7 @@ class ImageOCRByTextract:
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
                 # 在遮罩图像上绘制白色矩形框
-                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (0, 0, 0), -1)
+                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
                 mask = cv2.bitwise_or(mask, temp_mask)
 
         # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
@@ -156,7 +156,7 @@ class ImageOCRByTextractV2:
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
                 # 在遮罩图像上绘制白色矩形框
-                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (0, 0, 0), -1)
                 mask = cv2.bitwise_or(mask, temp_mask)
 
         # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
@@ -177,7 +177,7 @@ class ImageOCRByTextractV2:
 
 
 NODE_CLASS_MAPPINGS = {
-    "Image OCR By Textract": ImageOCRByTextract
+    "Image OCR By Textract": ImageOCRByTextract,
     "Image OCR By Textract V2":ImageOCRByTextractV2
 }
 

From df06edfc0d32e9e72fc393d31e0babd8d6de85b0 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Mon, 15 Jul 2024 15:03:44 +0800
Subject: [PATCH 10/30] udpate textract node

---
 nodes/textract.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index c92ced3..2fa09b5 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -104,11 +104,13 @@ class ImageOCRByTextractV2:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",)
-            }
-        }
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":
+                  {
+                      "image": (sorted(files), {"image_upload": True}),
+                    },
+                }
 
     RETURN_TYPES = ("STRING","STRING","STRING")
     RETURN_NAMES = ("Texts","Original Image Path","Mask Image Path")

From 4b05e36cf30c0975f25592829a1fe30503debe71 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 18 Jul 2024 07:06:45 +0800
Subject: [PATCH 11/30] udpate textract node

---
 nodes/textract.py | 40 ++++++++++++++++++++++++++++++----------
 1 file changed, 30 insertions(+), 10 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 2fa09b5..4081f83 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -13,6 +13,8 @@ import torch
 import cv2
 import folder_paths
 from .session import get_client
+import torch
+from torchvision import transforms
 
 
 current_directory = os.path.dirname(os.path.abspath(__file__))
@@ -112,8 +114,8 @@ class ImageOCRByTextractV2:
                     },
                 }
 
-    RETURN_TYPES = ("STRING","STRING","STRING")
-    RETURN_NAMES = ("Texts","Original Image Path","Mask Image Path")
+    RETURN_TYPES = ("STRING","STRING","STRING","IMAGE")
+    RETURN_NAMES = ("Texts","Original Image Path","Mask Image Path","AnyText Mask Image")
     FUNCTION = "forward"
     CATEGORY = "aws"
     OUTPUT_NODE = True
@@ -121,6 +123,15 @@ class ImageOCRByTextractV2:
     def ocr_by_textract(self,image_input):
         ori_image_path = folder_paths.get_annotated_filepath(image_input)
         pos_img_path = os.path.join(temp_img_path)
+
+        ## 转换ori_image为tensor张量
+        pil_image = Image.open(ori_image_path)
+        transform = transforms.Compose([
+            transforms.ToTensor(),  # 将 PIL Image 转换为 tensor，并将像素值归一化到 [0, 1]
+        ])
+        image_input = transform(pil_image)
+
+        ## image input已经是标准comfyui的image张量格式
         numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
         image = image_input[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
@@ -136,7 +147,8 @@ class ImageOCRByTextractV2:
         # 初始化结果列表
         result = []
         # 创建一个与原始图像大小相同的黑色遮罩图像
-        mask = np.zeros_like(numpy_image)
+        #mask = np.zeros_like(numpy_image)
+        mask = np.ones_like(numpy_image) * 255
         all_text=""
 
 
@@ -157,19 +169,27 @@ class ImageOCRByTextractV2:
                 temp_mask = np.zeros_like(numpy_image)
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
-                # 在遮罩图像上绘制白色矩形框
-                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (0, 0, 0), -1)
-                mask = cv2.bitwise_or(mask, temp_mask)
+                # 在遮罩图像上绘制黑色矩形框
+                #cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (0, 0, 0), -1)
+                #mask = cv2.bitwise_or(mask, temp_mask)
+
+                cv2.rectangle(mask, (left, top), (left + width, top + height), (0, 0, 0), -1)
 
         # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
-        masked_img = cv2.bitwise_and(numpy_image, mask)
+        masked_img = np.where(mask == 0, 0, numpy_image)
+        #masked_img = cv2.bitwise_and(numpy_image, mask)
         masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
-        masked_img.save(temp_img_path)
-        all_text="|".join(result)
+        #masked_img.save(temp_img_path)
+        # 将 PyTorch 张量转换为 PIL 图像
+        to_pil = transforms.ToPILImage()
+        pil_image = to_pil(masked_img.squeeze(0))
+        # 保存 PIL 图像
+        pil_image.save(temp_img_path)
 
+        all_text="|".join(result)
         print("result",result)
 
-        return all_text ,ori_image_path,temp_img_path
+        return all_text ,ori_image_path,temp_img_path, masked_img
 
 
 

From c8dbc9615845c19aef5c731ee64e9adc97783744 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 18 Jul 2024 12:47:18 +0800
Subject: [PATCH 12/30] udpate textract node

---
 nodes/anytext.py  |  60 ------------------------
 nodes/textract.py | 117 +++++++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 115 insertions(+), 62 deletions(-)
 delete mode 100644 nodes/anytext.py

diff --git a/nodes/anytext.py b/nodes/anytext.py
deleted file mode 100644
index e950b16..0000000
--- a/nodes/anytext.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from modelscope.pipelines import pipeline
-import json
-import os
-import re
-import base64
-from io import BytesIO
-import requests
-from retry import retry
-import boto3
-from PIL import Image
-import numpy as np
-import torch
-import cv2
-
-class AnyTextGen:
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "ori_image": ("IMAGE",),
-                "mask_image":("IMAGE",),
-                "prompt":("STRING",)
-            }
-        }
-
-    RETURN_TYPES = ("IMAGE")
-    RETURN_NAMES = ("Converted Image")
-    FUNCTION = "forward"
-    CATEGORY = "aws"
-    OUTPUT_NODE = True
-
-    def any_text_edit_image(self,image_input):
-        pipe = pipeline('my-anytext-task', model='damo/cv_anytext_text_generation_editing', model_revision='v1.1.1', use_fp16=True, use_translator=False)
-        img_save_folder = "SaveImages"
-        params = {
-            "show_debug": True,
-            "image_count": 1,
-            "ddim_steps": 20,
-        }
-
-        mode = 'text-editing'
-        input_data = {
-            "prompt": prompt,
-            "seed": 8943410,
-            "draw_pos": ori_image,
-            "ori_image": mask_image
-        }
-        results, rtn_code, rtn_warning, debug_info = pipe(input_data, mode=mode, **params)
-        image = torch.from_numpy(np.array(results[0]).astype(np.float32) / 255.0).unsqueeze(0)
-        return image
-
-    @retry(tries=3)
-    def forward(self, image):
-        return self.any_text_edit_image(image)
-
-
-
-NODE_CLASS_MAPPINGS = {
-    "AnyText Gen": AnyTextGen
-}
\ No newline at end of file
diff --git a/nodes/textract.py b/nodes/textract.py
index 4081f83..2b7bf20 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -101,7 +101,7 @@ class ImageOCRByTextract:
         return self.ocr_by_textract(image)
 
 
-
+### for anyText Nodes
 class ImageOCRByTextractV2:
 
     @classmethod
@@ -198,8 +198,121 @@ class ImageOCRByTextractV2:
         return self.ocr_by_textract(image)
 
 
+## for layer style nodes
+class ImageOCRByTextractV3:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":
+                  {
+                      "image": (sorted(files), {"image_upload": True}),
+                    },
+                }
+
+    RETURN_TYPES = ("STRING","STRING","STRING","STRING","STRING","IMAGE")
+    RETURN_NAMES = ("Texts","x_offsets","y_offsets","widths","heights","AnyText Mask Image")
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+    OUTPUT_NODE = True
+
+    def ocr_by_textract(self,image_input):
+        ori_image_path = folder_paths.get_annotated_filepath(image_input)
+
+        ## 转换ori_image为tensor张量
+        pil_image = Image.open(ori_image_path)
+        transform = transforms.Compose([
+            transforms.ToTensor(),  # 将 PIL Image 转换为 tensor，并将像素值归一化到 [0, 1]
+        ])
+        image_input = transform(pil_image)
+
+        ## image input已经是标准comfyui的image张量格式
+        numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
+        image = image_input[0] * 255.0
+        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+        # 获取图像原始尺寸
+        img_width, img_height = image.size
+
+        # 调用Textract DetectDocumentText函数
+        byte_stream = io.BytesIO()
+        image.save(byte_stream, format='PNG')
+        byte_image = byte_stream.getvalue()
+        response = textract.detect_document_text(Document={'Bytes': byte_image})
+
+        # 初始化结果列表
+        result = []
+        # 创建一个与原始图像大小相同的黑色遮罩图像
+        #mask = np.zeros_like(numpy_image)
+        mask = np.ones_like(numpy_image) * 255
+        all_text=""
+        x_offsets=[]
+        y_offsets=[]
+        widths=[]
+        heights=[]
+
+
+
+        # 提取文本和边界框信息
+        for item in response['Blocks']:
+            if item['BlockType'] == 'LINE':
+                text = item['Text']
+                box = item['Geometry']['BoundingBox']
+
+                left = int(box['Left'] * img_width)
+                x_offsets.append(str(left))
+
+                top = int(box['Top'] * img_height)
+                y_offsets.append(str(top))
+
+                width = int(box['Width'] * img_width)
+                widths.append(str(width))
+
+                height = int(box['Height']* img_height)
+                heights.append(str(height))
+
+                # 将信息添加到结果列表中
+                result.append(text)
+                # 对每个文本信息框绘制mask遮罩
+                # 指定矩形框的左上角和右下角坐标
+                temp_mask = np.zeros_like(numpy_image)
+                x1, y1 = int(left), int(top)
+                x2, y2 = int(left + width), int(top + height)
+                # 在遮罩图像上绘制黑色矩形框
+                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+
+
+        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
+        masked_img = np.where(mask == 0, 0, numpy_image)
+        #masked_img = cv2.bitwise_and(numpy_image, mask)
+        masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
+        #masked_img.save(temp_img_path)
+        # 将 PyTorch 张量转换为 PIL 图像
+        to_pil = transforms.ToPILImage()
+        pil_image = to_pil(masked_img.squeeze(0))
+        # 保存 PIL 图像
+        pil_image.save(temp_img_path)
+
+        ###汇总结果输出
+        all_text="|".join(result)
+        x_offsets="|".join(x_offsets)
+        y_offsets="|".join(y_offsets)
+        widths="|".join(widths)
+        heights="|".join(heights)
+
+        print("result",result)
+
+        return all_text ,x_offsets,y_offsets,widths,heights, masked_img
+
+
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image):
+        return self.ocr_by_textract(image)
+
 NODE_CLASS_MAPPINGS = {
     "Image OCR By Textract": ImageOCRByTextract,
-    "Image OCR By Textract V2":ImageOCRByTextractV2
+    "Image OCR By Textract V2":ImageOCRByTextractV2,
+    "Image OCR By Textract V3":ImageOCRByTextractV3
 }
 

From 4ec50c953dd447af14acc2d6e97b8e369fe13e6f Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 25 Jul 2024 17:37:46 +0800
Subject: [PATCH 13/30] udpate textract node

---
 nodes/textract.py | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 2b7bf20..251a222 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -211,8 +211,8 @@ class ImageOCRByTextractV3:
                     },
                 }
 
-    RETURN_TYPES = ("STRING","STRING","STRING","STRING","STRING","IMAGE")
-    RETURN_NAMES = ("Texts","x_offsets","y_offsets","widths","heights","AnyText Mask Image")
+    RETURN_TYPES = ("STRING","STRING","STRING","STRING","STRING","STRING","STRING","IMAGE","IMAGE")
+    RETURN_NAMES = ("Texts","x_offsets","y_offsets","widths","heights","img_width","img_height","Mask Image","Original Image")
     FUNCTION = "forward"
     CATEGORY = "aws"
     OUTPUT_NODE = True
@@ -242,9 +242,8 @@ class ImageOCRByTextractV3:
 
         # 初始化结果列表
         result = []
-        # 创建一个与原始图像大小相同的黑色遮罩图像
-        #mask = np.zeros_like(numpy_image)
-        mask = np.ones_like(numpy_image) * 255
+        # 创建一个与原始图像大小相同的遮罩图像
+        masked_img = numpy_image.copy()
         all_text=""
         x_offsets=[]
         y_offsets=[]
@@ -275,17 +274,14 @@ class ImageOCRByTextractV3:
                 result.append(text)
                 # 对每个文本信息框绘制mask遮罩
                 # 指定矩形框的左上角和右下角坐标
-                temp_mask = np.zeros_like(numpy_image)
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
                 # 在遮罩图像上绘制黑色矩形框
-                cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
+                cv2.rectangle(masked_img, (x1, y1), (x2, y2), (0, 0, 0), -1)
 
 
-        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
-        masked_img = np.where(mask == 0, 0, numpy_image)
-        #masked_img = cv2.bitwise_and(numpy_image, mask)
-        masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
+        #masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
+        masked_img = torch.from_numpy(masked_img.astype(np.float32) / 255.0).permute(2, 0, 1)
         #masked_img.save(temp_img_path)
         # 将 PyTorch 张量转换为 PIL 图像
         to_pil = transforms.ToPILImage()
@@ -302,7 +298,10 @@ class ImageOCRByTextractV3:
 
         print("result",result)
 
-        return all_text ,x_offsets,y_offsets,widths,heights, masked_img
+        # 添加原始图像输出
+        original_img = image_input
+
+        return all_text ,x_offsets,y_offsets,widths,heights,img_width,img_height, masked_img,original_img
 
 
 

From 0fa336b448296a8c6b180e8aa1820d93e7e3805d Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Mon, 29 Jul 2024 14:02:24 +0800
Subject: [PATCH 14/30] udpate textract node

---
 nodes/textract.py | 27 ++++++---------------------
 1 file changed, 6 insertions(+), 21 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 251a222..ea0db54 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -205,10 +205,9 @@ class ImageOCRByTextractV3:
     def INPUT_TYPES(s):
         input_dir = folder_paths.get_input_directory()
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
-        return {"required":
-                  {
-                      "image": (sorted(files), {"image_upload": True}),
-                    },
+        return {"required":{
+                  "image": ("IMAGE",),
+                  }
                 }
 
     RETURN_TYPES = ("STRING","STRING","STRING","STRING","STRING","STRING","STRING","IMAGE","IMAGE")
@@ -218,19 +217,12 @@ class ImageOCRByTextractV3:
     OUTPUT_NODE = True
 
     def ocr_by_textract(self,image_input):
-        ori_image_path = folder_paths.get_annotated_filepath(image_input)
-
-        ## 转换ori_image为tensor张量
-        pil_image = Image.open(ori_image_path)
-        transform = transforms.Compose([
-            transforms.ToTensor(),  # 将 PIL Image 转换为 tensor，并将像素值归一化到 [0, 1]
-        ])
-        image_input = transform(pil_image)
 
         ## image input已经是标准comfyui的image张量格式
-        numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
         image = image_input[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+        numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
+
         # 获取图像原始尺寸
         img_width, img_height = image.size
 
@@ -281,14 +273,7 @@ class ImageOCRByTextractV3:
 
 
         #masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
-        masked_img = torch.from_numpy(masked_img.astype(np.float32) / 255.0).permute(2, 0, 1)
-        #masked_img.save(temp_img_path)
-        # 将 PyTorch 张量转换为 PIL 图像
-        to_pil = transforms.ToPILImage()
-        pil_image = to_pil(masked_img.squeeze(0))
-        # 保存 PIL 图像
-        pil_image.save(temp_img_path)
-
+        masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
         ###汇总结果输出
         all_text="|".join(result)
         x_offsets="|".join(x_offsets)

From d21bde7a6645c8157aeb626349c5cb8844e8bf1c Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Fri, 16 Aug 2024 13:41:42 +0800
Subject: [PATCH 15/30] udpate textract node

---
 nodes/textract.py | 117 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 116 insertions(+), 1 deletion(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index ea0db54..82badb2 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -15,6 +15,9 @@ import folder_paths
 from .session import get_client
 import torch
 from torchvision import transforms
+from paddleocr import PaddleOCR, draw_ocr
+from paddleocr import PPStructure,draw_structure_result
+import tempfile
 
 
 current_directory = os.path.dirname(os.path.abspath(__file__))
@@ -198,6 +201,116 @@ class ImageOCRByTextractV2:
         return self.ocr_by_textract(image)
 
 
+## for layer style nodes
+class ImageOCRByTextractV4:
+
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required":{
+                  "image": ("IMAGE",),
+                  }
+                }
+
+    RETURN_TYPES = ("STRING","STRING","STRING","STRING","STRING","STRING","STRING","IMAGE","IMAGE")
+    RETURN_NAMES = ("Texts","x_offsets","y_offsets","widths","heights","img_width","img_height","Mask Image","Original Image")
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+    OUTPUT_NODE = True
+
+    def convert_to_xywh(coordinates):
+        # 提取所有的 x 和 y 坐标
+        x_coords = [coord[0] for coord in coordinates]
+        y_coords = [coord[1] for coord in coordinates]
+
+        # 计算 x offset 和 y offset
+        x_offset = min(x_coords)
+        y_offset = min(y_coords)
+
+        # 计算 width 和 height
+        width = max(x_coords) - x_offset
+        height = max(y_coords) - y_offset
+
+        return x_offset, y_offset, width, height
+
+    def ocr_by_paddleocr(self,image_input):
+
+        ## image input已经是标准comfyui的image张量格式
+        image = image_input[0] * 255.0
+        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+        numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
+
+
+        # 创建临时文件
+        with tempfile.NamedTemporaryFile(delete=False, suffix='.png', dir='/tmp/') as temp_file:
+            temp_filename = temp_file.name
+
+        # 保存numpy_image为临时文件
+        cv2.imwrite(temp_filename, cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR))
+
+        ocr = PaddleOCR(
+        det_model_dir='/home/ubuntu/ComfyUI/models/checkpoints/PaddleOCR/det',
+        rec_model_dir='/home/ubuntu/ComfyUI/models/checkpoints/PaddleOCR/rec',
+        det_limit_side_len=2048,
+        use_angle_cls=True,
+        #use_gpu=False,
+        )
+
+        result = ocr.ocr(temp_filename, cls=True)[0]
+
+        # 创建一个与原始图像大小相同的遮罩图像
+        masked_img = numpy_image.copy()
+        all_text=""
+        x_offsets=[]
+        y_offsets=[]
+        widths=[]
+        heights=[]
+
+
+        # 提取文本和边界框信息
+        for line in result:
+             boxes = line[0]
+             x_offset,y_offset,width,height = convert_to_xywh(boxes)
+             x_offsets.append(str(x_offset))
+             y_offsets.append(str(y_offset))
+             widths.append(str(width))
+             heights.append(str(height))
+
+             text = line[1][0]
+             result.append(text)
+
+             # 对每个文本信息框绘制mask遮罩
+             # 指定矩形框的左上角和右下角坐标
+             x1, y1 = int(x_offset), int(y_offset)
+             x2, y2 = int(x_offset + width), int(y_offset + height)
+             # 在遮罩图像上绘制黑色矩形框
+             cv2.rectangle(masked_img, (x1, y1), (x2, y2), (0, 0, 0), -1)
+
+        masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
+        ###汇总结果输出
+        all_text="|".join(result)
+        x_offsets="|".join(x_offsets)
+        y_offsets="|".join(y_offsets)
+        widths="|".join(widths)
+        heights="|".join(heights)
+
+        print("result",result)
+
+        # 添加原始图像输出
+        original_img = image_input
+        # 删除临时文件
+        os.unlink(temp_filename)
+
+        return all_text ,x_offsets,y_offsets,widths,heights,img_width,img_height, masked_img,original_img
+
+
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image):
+        return self.ocr_by_paddleocr(image)
+
+
 ## for layer style nodes
 class ImageOCRByTextractV3:
 
@@ -294,9 +407,11 @@ class ImageOCRByTextractV3:
     def forward(self, image):
         return self.ocr_by_textract(image)
 
+
 NODE_CLASS_MAPPINGS = {
     "Image OCR By Textract": ImageOCRByTextract,
     "Image OCR By Textract V2":ImageOCRByTextractV2,
-    "Image OCR By Textract V3":ImageOCRByTextractV3
+    "Image OCR By Textract V3":ImageOCRByTextractV3,
+    "Image OCR by PaddleOCR": ImageOCRByTextractV4
 }
 

From 7dcd34be383273ef639b52ef8bbde8bc764b8f72 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Tue, 20 Aug 2024 07:56:40 +0800
Subject: [PATCH 16/30] udpate paddle ocr node

---
 nodes/textract.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 82badb2..1fedcd3 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -219,7 +219,7 @@ class ImageOCRByTextractV4:
     CATEGORY = "aws"
     OUTPUT_NODE = True
 
-    def convert_to_xywh(coordinates):
+    def convert_to_xywh(self,coordinates):
         # 提取所有的 x 和 y 坐标
         x_coords = [coord[0] for coord in coordinates]
         y_coords = [coord[1] for coord in coordinates]
@@ -232,7 +232,8 @@ class ImageOCRByTextractV4:
         width = max(x_coords) - x_offset
         height = max(y_coords) - y_offset
 
-        return x_offset, y_offset, width, height
+        print(x_offset,y_offset,width,height)
+        return int(x_offset), int(y_offset), int(width), int(height)
 
     def ocr_by_paddleocr(self,image_input):
 
@@ -241,6 +242,9 @@ class ImageOCRByTextractV4:
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
         numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
 
+        # 获取图像原始尺寸
+        img_width, img_height = image.size
+
 
         # 创建临时文件
         with tempfile.NamedTemporaryFile(delete=False, suffix='.png', dir='/tmp/') as temp_file:
@@ -257,9 +261,10 @@ class ImageOCRByTextractV4:
         #use_gpu=False,
         )
 
-        result = ocr.ocr(temp_filename, cls=True)[0]
+        ocr_results = ocr.ocr(temp_filename, cls=True)[0]
 
         # 创建一个与原始图像大小相同的遮罩图像
+        result = []
         masked_img = numpy_image.copy()
         all_text=""
         x_offsets=[]
@@ -269,15 +274,19 @@ class ImageOCRByTextractV4:
 
 
         # 提取文本和边界框信息
-        for line in result:
+        for line in ocr_results:
+             if not isinstance(line, list):
+                continue
              boxes = line[0]
-             x_offset,y_offset,width,height = convert_to_xywh(boxes)
+             x_offset,y_offset,width,height = self.convert_to_xywh(boxes)
              x_offsets.append(str(x_offset))
              y_offsets.append(str(y_offset))
              widths.append(str(width))
              heights.append(str(height))
 
              text = line[1][0]
+             print("text")
+             print(text)
              result.append(text)
 
              # 对每个文本信息框绘制mask遮罩

From fa78e81c7b70f0db7103984e7e2f3432e0ff7578 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Wed, 25 Dec 2024 20:30:45 +0800
Subject: [PATCH 17/30] add nova canvas nodes

---
 .idea/.gitignore                              |   3 +
 .idea/comfyui-llm-node-for-amazon-bedrock.iml |   9 +
 .idea/misc.xml                                |   6 +
 .idea/modules.xml                             |   8 +
 .idea/vcs.xml                                 |   6 +
 images/edit12.png                             | Bin 0 -> 401641 bytes
 images/edit7.png                              | Bin 0 -> 167803 bytes
 images/gen9.png                               | Bin 0 -> 14835 bytes
 images/ref7.jpg                               | Bin 0 -> 64320 bytes
 nodes/bedrock_nova_image.py                   | 369 ++++++++
 nodes/file_utils.py                           |  62 ++
 nodes/nova_image_gen.py                       | 115 +++
 workflows/lama.json                           | 788 ++++++++++++++++++
 workflows/sensitive erase.json                | 701 ++++++++++++++++
 workflows/subtitles_translate.json            | 777 +++++++++++++++++
 15 files changed, 2844 insertions(+)
 create mode 100644 .idea/.gitignore
 create mode 100644 .idea/comfyui-llm-node-for-amazon-bedrock.iml
 create mode 100644 .idea/misc.xml
 create mode 100644 .idea/modules.xml
 create mode 100644 .idea/vcs.xml
 create mode 100644 images/edit12.png
 create mode 100644 images/edit7.png
 create mode 100644 images/gen9.png
 create mode 100644 images/ref7.jpg
 create mode 100644 nodes/bedrock_nova_image.py
 create mode 100644 nodes/file_utils.py
 create mode 100644 nodes/nova_image_gen.py
 create mode 100644 workflows/lama.json
 create mode 100644 workflows/sensitive erase.json
 create mode 100644 workflows/subtitles_translate.json

diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..26d3352
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# Default ignored files
+/shelf/
+/workspace.xml
diff --git a/.idea/comfyui-llm-node-for-amazon-bedrock.iml b/.idea/comfyui-llm-node-for-amazon-bedrock.iml
new file mode 100644
index 0000000..d6ebd48
--- /dev/null
+++ b/.idea/comfyui-llm-node-for-amazon-bedrock.iml
@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="JAVA_MODULE" version="4">
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..639900d
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager">
+    <output url="file://$PROJECT_DIR$/out" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..8d7435e
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/comfyui-llm-node-for-amazon-bedrock.iml" filepath="$PROJECT_DIR$/.idea/comfyui-llm-node-for-amazon-bedrock.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/images/edit12.png b/images/edit12.png
new file mode 100644
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diff --git a/nodes/bedrock_nova_image.py b/nodes/bedrock_nova_image.py
new file mode 100644
index 0000000..598c836
--- /dev/null
+++ b/nodes/bedrock_nova_image.py
@@ -0,0 +1,369 @@
+import json
+import os
+import re
+import base64
+from io import BytesIO
+from random import randint
+import nova_image_gen
+import file_utils
+import logging
+from datetime import datetime
+import requests
+from retry import retry
+from PIL import Image
+import numpy as np
+import torch
+from .session import get_client
+
+MAX_RETRY = 3
+
+bedrock_runtime_client = get_client(service_name="bedrock-runtime")
+
+class BedrockNovaTextImage:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": ("STRING", {"multiline": True}),
+                "num_images": (
+                    "INT",
+                    {
+                        "default": 1,
+                        "min": 1,
+                        "max": 5,
+                        "step": 1,
+                        "round": 1,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "number",
+                    },
+                ),
+                "quality": (
+                    [
+                        "standard",
+                        "premium",
+                    ],
+                ),
+                "resolution": (
+                    [
+                        "1024 x 1024",
+                        "768 x 768",
+                        "512 x 512",
+                        "1152 x 768",
+                        "576 x 384",
+                        "768 x 1280",
+                        "384 x 640",
+                        "1280 x 768"
+                    ],
+                ),
+                "cfg_scale": (
+                    "FLOAT",
+                    {
+                        "default": 7.0,
+                        "min": 0.0,
+                        "max": 35.0,
+                        "step": 0.1,
+                        "round": 0.001,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "slider",
+                    },
+                ),
+                "seed": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2147483646,
+                        "step": 1,
+                        "round": 1,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "number",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, prompt, num_images, quality, resolution, cfg_scale, seed):
+
+        height, width = map(int, re.findall(r"\d+", resolution))
+
+        # The different model providers have individual request and response formats.
+        # For the format, ranges, and default values for Titan Image models refer to:
+        # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html
+
+        inference_params =
+        {
+                "taskType": "TEXT_IMAGE",
+                "textToImageParams": {"text": prompt},
+                "imageGenerationConfig": {
+                    "numberOfImages": num_images,
+                    "quality": quality,
+                    "cfgScale": cfg_scale,
+                    "height": height,
+                    "width": width,
+                    "seed": seed,
+                },
+        }
+
+        # Define an output directory with a unique name.
+        generation_id = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        output_directory = f"output/{generation_id}"
+
+        # Generate the image(s).
+        response = nova_image_gen.generate_images(
+            inference_params=inference_params,
+            model_id="amazon.nova-canvas-v1:0",
+            output_directory=output_directory
+        )
+
+        response_body = json.loads(response["body"].read())
+        # base64_image_data = response_body["images"][0]
+        images = [
+            np.array(Image.open(BytesIO(base64.b64decode(base64_image))))
+            for base64_image in response_body.get("images")
+        ]
+        images = [
+            torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+            for image in images
+        ]
+        if len(images) == 1:
+            images = images[0]
+        else:
+            images = torch.cat(images, 0)
+        return (images,)
+
+
+class BedrockNovaIpAdatper:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "prompt": ("STRING", {"multiline": True}),
+                "negative_prompt": ("STRING", {"multiline": True}),
+                "similarity_strength": (
+                    "FLOAT",
+                    {
+                          "default": 0.9,
+                          "min": 0.2,
+                          "max": 1.0,
+                          "step": 0.1,
+                          "round": 0.01,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                          "display": "slider",
+                    },
+                ),
+                "num_images": (
+                    "INT",
+                    {
+                        "default": 1,
+                        "min": 1,
+                        "max": 5,
+                        "step": 1,
+                        "round": 1,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "number",
+                    },
+                ),
+                "cfg_scale": (
+                    "FLOAT",
+                    {
+                        "default": 8.0,
+                        "min": 1.1,
+                        "max": 10.0,
+                        "step": 0.1,
+                        "round": 0.001,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "slider",
+                    },
+                ),
+                "resolution": (
+                    [
+                        "1024 x 1024",
+                        "1024 x 1024",
+                        "768 x 768",
+                        "512 x 512",
+                        "1152 x 768",
+                        "576 x 384",
+                        "768 x 1280"
+                    ],
+                ),
+                "seed": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2147483646,
+                        "step": 1,
+                        "round": 1,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "number",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image, prompt, negative_prompt, similarity_strength, num_images, cfg_scale, resolution):
+
+        image = image[0] * 255.0
+        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+
+        height, width = map(int, re.findall(r"\d+", resolution))
+
+        buffer = BytesIO()
+        image.save(buffer, format="PNG")
+
+        image_data = buffer.getvalue()
+
+        image_base64 = base64.b64encode(image_data).decode("utf-8")
+
+        # The different model providers have individual request and response formats.
+        # For the format, ranges, and default values for Titan Image models refer to:
+        # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html
+
+        # add a key value to json only when the a parameter is not empty
+        inference_params = {
+                "taskType": "IMAGE_VARIATION",
+                "inPaintingParams": {
+                    "image": image_base64,                         
+                    "text": prompt,
+                    "negativeText": negative_prompt,        
+                    "similarityStrength": similarity_strength,
+                },                                                 
+                "imageGenerationConfig": {
+                    "numberOfImages": num_images,
+                    "quality": "premium",
+                    "height": height,
+                    "width": width,
+                    "cfgScale": cfg_scale,
+                    "seed": seed
+                }
+            }
+        
+        response_body = nova_image_gen.generate_images(
+            inference_params=inference_params,
+            model_id="amazon.nova-canvas-v1:0",
+            output_directory=output_directory
+        )
+
+        images = [
+            np.array(Image.open(BytesIO(base64.b64decode(base64_image))))
+            for base64_image in response_body.get("images")
+        ]
+        images = [
+            torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+            for image in images
+        ]
+        if len(images) == 1:
+            images = images[0]
+        else:
+            images = torch.cat(images, 0)
+        return (images,)
+
+
+class BedrockNovaBackgroundPEReplace:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "prompt": ("STRING", {"multiline": True}),
+                "mask_prompt": ("STRING", {"multiline": True}),
+                "num_images": (
+                    "INT",
+                    {
+                        "default": 1,
+                        "min": 1,
+                        "max": 5,
+                        "step": 1,
+                        "round": 1,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "number",
+                    },
+                ),
+                "cfg_scale": (
+                    "FLOAT",
+                    {
+                        "default": 8.0,
+                        "min": 1.1,
+                        "max": 10.0,
+                        "step": 0.1,
+                        "round": 0.001,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "slider",
+                    },
+                ),
+                "seed": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2147483646,
+                        "step": 1,
+                        "round": 1,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "number",
+                    },
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image, prompt, mask_prompt, num_images, cfg_scale):
+        image = image[0] * 255.0
+        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+
+
+        buffer = BytesIO()
+        image.save(buffer, format="PNG")
+
+        image_data = buffer.getvalue()
+
+        image_base64 = base64.b64encode(image_data).decode("utf-8")
+
+        inference_params = {
+                "taskType": "OUTPAINTING",
+                "inPaintingParams": {
+                    "image": image_base64,
+                    "text": prompt,
+                    "maskPrompt": mask_prompt,
+                    "outPaintingMode": "PRECISE",
+                },
+                "imageGenerationConfig": {
+                    "numberOfImages": num_images,
+                    "quality": "premium",
+                    "cfgScale": cfg_scale,
+                    "seed": seed
+                }
+            }
+
+        response_body = nova_image_gen.generate_images(
+            inference_params=inference_params,
+            model_id="amazon.nova-canvas-v1:0",
+            output_directory=output_directory
+        )
+
+        images = [
+            np.array(Image.open(BytesIO(base64.b64decode(base64_image))))
+            for base64_image in response_body.get("images")
+        ]
+        images = [
+            torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
+            for image in images
+        ]
+        if len(images) == 1:
+            images = images[0]
+        else:
+            images = torch.cat(images, 0)
+        return (images,)
+
+
+
+NODE_CLASS_MAPPINGS = {
+    "Bedrock - Nova Text to Image": BedrockNovaTextImage,
+    "Bedrock - Nova IpAdapter": BedrockNovaIpAdatper,
+    "Bedrock - Nova Background Prompt Replace": BedrockNovaBackgroundPEReplace,
+}
\ No newline at end of file
diff --git a/nodes/file_utils.py b/nodes/file_utils.py
new file mode 100644
index 0000000..5643b21
--- /dev/null
+++ b/nodes/file_utils.py
@@ -0,0 +1,62 @@
+import base64
+import io
+import os
+from datetime import datetime
+
+from PIL import Image
+
+
+def save_base64_image(base64_image, output_directory, base_name="image", suffix="_1"):
+    """
+    Saves a base64 encoded image to a specified output directory with a timestamp and a suffix.
+
+    Args:
+        base64_image (str): The base64 encoded image string.
+        output_directory (str): The directory where the image will be saved.
+        suffix (str, optional): A suffix to be added to the filename. Defaults to "_1".
+    Returns:
+        PIL.Image.Image: The Pillow Image object representing the saved image.
+    """
+    image_bytes = base64.b64decode(base64_image)
+    image = Image.open(io.BytesIO(image_bytes))
+    save_image(image, output_directory, base_name, suffix)
+    return image
+
+
+def save_image(image, output_directory, base_name="image", suffix="_1"):
+    """
+    Saves a Pillow Image object to a specified output directory with a timestamp and a suffix.
+
+    Args:
+        image (PIL.Image.Image): The Pillow Image object to be saved.
+        output_directory (str): The directory where the image will be saved.
+        suffix (str, optional): A suffix to be added to the filename. Defaults to "_1".
+    Returns:
+        None
+    """
+    if not os.path.exists(output_directory):
+        os.makedirs(output_directory)
+
+    file_name = f"{base_name}{suffix}.png"
+    file_path = os.path.join(output_directory, file_name)
+    image.save(file_path)
+
+
+def save_base64_images(base64_images, output_directory, base_name="image"):
+    """
+    Saves a list of base64 encoded images to a specified output directory.
+
+    Args:
+        base64_images (list): A list of base64 encoded image strings.
+        output_directory (str): The directory where the images will be saved.
+    Returns:
+        An array of Pillow Image objects representing the saved images.
+    """
+    images = []
+    for i, base64_image in enumerate(base64_images):
+        image = save_base64_image(
+            base64_image, output_directory, base_name=base_name, suffix=f"_{i+1}"
+        )
+        images.append(image)
+
+    return images
diff --git a/nodes/nova_image_gen.py b/nodes/nova_image_gen.py
new file mode 100644
index 0000000..09f4b35
--- /dev/null
+++ b/nodes/nova_image_gen.py
@@ -0,0 +1,115 @@
+import json
+import logging
+import os
+from datetime import datetime
+
+import boto3
+
+logger = logging.getLogger(__name__)
+
+
+def generate_images(
+    inference_params,
+    model_id="amazon.nova-canvas-v1:0",
+    region_name="us-east-1",
+    endpoint_url=None,
+    output_directory="~/ComfyUI/output",
+):
+    """
+    Generate an image using using Titan Image Generator or Olympus Image Generator.
+    Args:
+        body (dict) : The request body to use.
+        model_id (str): The model ID to use. Defaults to "amazon.titan-image-generator-v1".
+        region_name (str): The AWS region name to use. Defaults to "us-east-1".
+        endpoint_url (str): The endpoint URL to use. Defaults to None.
+        output_directory (str): The directory to save the generated images. Defaults to "./output".
+    Returns:
+        response_body (dict): The response body from the model.
+        job_output_directory (str): The directory where the request and response JSON values are saved.
+        generation_id (str): The generation ID for the request.
+    Usage:
+        response_body, job_output_directory, generation_id = generate_images(inference_params)
+    Raises:
+        Exception: Any exception thrown by .invoke_model().
+    """
+    os.makedirs(output_directory, exist_ok=True)
+
+    image_count = 1
+    if "imageGenerationConfig" in inference_params:
+        if "numberOfImages" in inference_params["imageGenerationConfig"]:
+            image_count = inference_params["imageGenerationConfig"]["numberOfImages"]
+
+    logger.info(f"Generating {image_count} image(s) with {model_id}")
+
+    # Display the seed value if one is being used.
+    if "imageGenerationConfig" in inference_params:
+        if "seed" in inference_params["imageGenerationConfig"]:
+            logger.info(
+                f"Using seed: {inference_params['imageGenerationConfig']['seed']}"
+            )
+
+    bedrock_client_optional_args = (
+        {} if endpoint_url is None else {"endpoint_url": endpoint_url}
+    )
+    bedrock = boto3.client(
+        service_name="bedrock-runtime",
+        region_name=region_name,
+        **bedrock_client_optional_args,
+    )
+
+    body_json = json.dumps(inference_params, indent=2)
+
+    # Write request body to JSON file.
+    request_file_path = os.path.join(output_directory, "request.json")
+    with open(request_file_path, "w") as f:
+        f.write(body_json)
+
+    try:
+        response = bedrock.invoke_model(
+            body=body_json,
+            modelId=model_id,
+            accept="application/json",
+            contentType="application/json",
+        )
+
+        response_metadata = response.get("ResponseMetadata")
+        # WRite response metadata to JSON file.
+        response_metadata_file_path = os.path.join(
+            output_directory, "response_metadata.json"
+        )
+        with open(response_metadata_file_path, "w") as f:
+            json.dump(response_metadata, f, indent=2)
+
+        response_body = json.loads(response.get("body").read())
+
+        # Write response body to JSON file.
+        response_file_path = os.path.join(output_directory, "response_body.json")
+        with open(response_file_path, "w") as f:
+            json.dump(response_body, f, indent=2)
+
+        # Log the request ID.
+        logger.info(f"Request ID: {response['ResponseMetadata']['RequestId']}")
+
+        # Check for non-exception errors.
+        if "error" in response_body:
+            if response_body["error"] == "":
+                logger.warning(
+                    """Response included 'error' of "" (empty string). This indicates a bug with the Bedrock API."""
+                )
+            else:
+                logger.warning(f"Error: {response_body['error']}")
+
+        return response_body
+
+    except Exception as e:
+        # If e has "response", write it to disk as JSON.
+        if hasattr(e, "response"):
+            error_response = e.response
+            error_response_file_path = os.path.join(
+                output_directory, "error_response.json"
+            )
+            with open(error_response_file_path, "w") as f:
+                json.dump(error_response, f, indent=2)
+
+        logger.error(e)
+        raise e
diff --git a/workflows/lama.json b/workflows/lama.json
new file mode 100644
index 0000000..4e366ea
--- /dev/null
+++ b/workflows/lama.json
@@ -0,0 +1,788 @@
+{
+  "last_node_id": 35,
+  "last_link_id": 71,
+  "nodes": [
+    {
+      "id": 15,
+      "type": "PreviewImage",
+      "pos": [
+        1680,
+        650
+      ],
+      "size": {
+        "0": 295.9647216796875,
+        "1": 320.621337890625
+      },
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 23
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 29,
+      "type": "PreviewImage",
+      "pos": [
+        1680,
+        1040
+      ],
+      "size": {
+        "0": 295.9647216796875,
+        "1": 320.621337890625
+      },
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 62
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 8,
+      "type": "PreviewImage",
+      "pos": [
+        1680,
+        230
+      ],
+      "size": {
+        "0": 299.51416015625,
+        "1": 327.3353271484375
+      },
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 8
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 31,
+      "type": "PreviewImage",
+      "pos": [
+        2410,
+        240
+      ],
+      "size": {
+        "0": 299.51416015625,
+        "1": 327.3353271484375
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 65
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 33,
+      "type": "PreviewImage",
+      "pos": [
+        2410,
+        630
+      ],
+      "size": {
+        "0": 299.51416015625,
+        "1": 327.3353271484375
+      },
+      "flags": {},
+      "order": 14,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 68
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 35,
+      "type": "PreviewImage",
+      "pos": [
+        2420,
+        1020
+      ],
+      "size": {
+        "0": 299.51416015625,
+        "1": 327.3353271484375
+      },
+      "flags": {},
+      "order": 15,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 71
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 25,
+      "type": "Reroute",
+      "pos": [
+        1132,
+        727
+      ],
+      "size": [
+        75,
+        26
+      ],
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "",
+          "type": "*",
+          "link": 54
+        }
+      ],
+      "outputs": [
+        {
+          "name": "",
+          "type": "MASK",
+          "links": [
+            58,
+            59,
+            61,
+            64,
+            67,
+            70
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "showOutputText": false,
+        "horizontal": false
+      }
+    },
+    {
+      "id": 27,
+      "type": "Reroute",
+      "pos": [
+        1129,
+        439
+      ],
+      "size": [
+        75,
+        26
+      ],
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "",
+          "type": "*",
+          "link": 55
+        }
+      ],
+      "outputs": [
+        {
+          "name": "",
+          "type": "IMAGE",
+          "links": [
+            56,
+            57,
+            60,
+            63,
+            66,
+            69
+          ],
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "showOutputText": false,
+        "horizontal": false
+      }
+    },
+    {
+      "id": 24,
+      "type": "LayerMask: RemBgUltra",
+      "pos": [
+        785,
+        772
+      ],
+      "size": {
+        "0": 315,
+        "1": 150
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 36
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "links": [
+            54
+          ],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerMask: RemBgUltra"
+      },
+      "widgets_values": [
+        8,
+        0.01,
+        0.99,
+        false
+      ]
+    },
+    {
+      "id": 2,
+      "type": "LoadImage",
+      "pos": [
+        431,
+        439
+      ],
+      "size": {
+        "0": 323.62158203125,
+        "1": 420.20391845703125
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            36,
+            55
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "512x512with_background (24).png",
+        "image"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "LayerUtility: LaMa",
+      "pos": [
+        1300,
+        320
+      ],
+      "size": {
+        "0": 315,
+        "1": 174
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 56
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 58
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            8
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerUtility: LaMa"
+      },
+      "widgets_values": [
+        "lama",
+        "cuda",
+        false,
+        25,
+        6
+      ]
+    },
+    {
+      "id": 14,
+      "type": "LayerUtility: LaMa",
+      "pos": [
+        1300,
+        710
+      ],
+      "size": {
+        "0": 315,
+        "1": 174
+      },
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 57
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 59
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            23
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerUtility: LaMa"
+      },
+      "widgets_values": [
+        "ldm",
+        "cuda",
+        false,
+        25,
+        6
+      ]
+    },
+    {
+      "id": 28,
+      "type": "LayerUtility: LaMa",
+      "pos": [
+        1290,
+        1030
+      ],
+      "size": {
+        "0": 315,
+        "1": 174
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 60
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 61
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            62
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerUtility: LaMa"
+      },
+      "widgets_values": [
+        "zits",
+        "cuda",
+        false,
+        25,
+        6
+      ]
+    },
+    {
+      "id": 30,
+      "type": "LayerUtility: LaMa",
+      "pos": [
+        2036,
+        314
+      ],
+      "size": {
+        "0": 315,
+        "1": 174
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 63
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 64
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            65
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerUtility: LaMa"
+      },
+      "widgets_values": [
+        "mat",
+        "cuda",
+        false,
+        25,
+        6
+      ]
+    },
+    {
+      "id": 32,
+      "type": "LayerUtility: LaMa",
+      "pos": [
+        2037,
+        724
+      ],
+      "size": {
+        "0": 315,
+        "1": 174
+      },
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 66
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 67
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            68
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerUtility: LaMa"
+      },
+      "widgets_values": [
+        "fcf",
+        "cuda",
+        false,
+        25,
+        6
+      ]
+    },
+    {
+      "id": 34,
+      "type": "LayerUtility: LaMa",
+      "pos": [
+        2043,
+        1042
+      ],
+      "size": {
+        "0": 315,
+        "1": 174
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 69
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "link": 70
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            71
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerUtility: LaMa"
+      },
+      "widgets_values": [
+        "spread",
+        "cuda",
+        false,
+        25,
+        6
+      ]
+    }
+  ],
+  "links": [
+    [
+      8,
+      7,
+      0,
+      8,
+      0,
+      "IMAGE"
+    ],
+    [
+      23,
+      14,
+      0,
+      15,
+      0,
+      "IMAGE"
+    ],
+    [
+      36,
+      2,
+      0,
+      24,
+      0,
+      "IMAGE"
+    ],
+    [
+      54,
+      24,
+      1,
+      25,
+      0,
+      "*"
+    ],
+    [
+      55,
+      2,
+      0,
+      27,
+      0,
+      "*"
+    ],
+    [
+      56,
+      27,
+      0,
+      7,
+      0,
+      "IMAGE"
+    ],
+    [
+      57,
+      27,
+      0,
+      14,
+      0,
+      "IMAGE"
+    ],
+    [
+      58,
+      25,
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+      7,
+      1,
+      "MASK"
+    ],
+    [
+      59,
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+      14,
+      1,
+      "MASK"
+    ],
+    [
+      60,
+      27,
+      0,
+      28,
+      0,
+      "IMAGE"
+    ],
+    [
+      61,
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+      28,
+      1,
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+    ],
+    [
+      62,
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+      29,
+      0,
+      "IMAGE"
+    ],
+    [
+      63,
+      27,
+      0,
+      30,
+      0,
+      "IMAGE"
+    ],
+    [
+      64,
+      25,
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+      1,
+      "MASK"
+    ],
+    [
+      65,
+      30,
+      0,
+      31,
+      0,
+      "IMAGE"
+    ],
+    [
+      66,
+      27,
+      0,
+      32,
+      0,
+      "IMAGE"
+    ],
+    [
+      67,
+      25,
+      0,
+      32,
+      1,
+      "MASK"
+    ],
+    [
+      68,
+      32,
+      0,
+      33,
+      0,
+      "IMAGE"
+    ],
+    [
+      69,
+      27,
+      0,
+      34,
+      0,
+      "IMAGE"
+    ],
+    [
+      70,
+      25,
+      0,
+      34,
+      1,
+      "MASK"
+    ],
+    [
+      71,
+      34,
+      0,
+      35,
+      0,
+      "IMAGE"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {},
+  "version": 0.4
+}
diff --git a/workflows/sensitive erase.json b/workflows/sensitive erase.json
new file mode 100644
index 0000000..340f98a
--- /dev/null
+++ b/workflows/sensitive erase.json	
@@ -0,0 +1,701 @@
+{
+  "last_node_id": 52,
+  "last_link_id": 83,
+  "nodes": [
+    {
+      "id": 25,
+      "type": "PreviewImage",
+      "pos": [
+        1789,
+        644
+      ],
+      "size": {
+        "0": 210,
+        "1": 246
+      },
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
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diff --git a/workflows/subtitles_translate.json b/workflows/subtitles_translate.json
new file mode 100644
index 0000000..209428b
--- /dev/null
+++ b/workflows/subtitles_translate.json
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+          "type": "INT",
+          "link": 39,
+          "widget": {
+            "name": "x_offset"
+          }
+        },
+        {
+          "name": "y_offset",
+          "type": "INT",
+          "link": 40,
+          "widget": {
+            "name": "y_offset"
+          }
+        },
+        {
+          "name": "width",
+          "type": "INT",
+          "link": 41,
+          "widget": {
+            "name": "width"
+          }
+        },
+        {
+          "name": "height",
+          "type": "INT",
+          "link": 42,
+          "widget": {
+            "name": "height"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "links": [
+            28
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "mask",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LayerUtility: SimpleTextImage"
+      },
+      "widgets_values": [
+        "Are you going to Scarborough Fair?\nParsley, sage, rosemary, and thyme.\nRemember me to one who lives there,\nShe once was the true love of mine.",
+        "Alibaba-PuHuiTi-Heavy.ttf",
+        "left",
+        80,
+        8,
+        50,
+        "#FFFFFF",
+        0,
+        "#FF8000",
+        0,
+        300,
+        512,
+        512
+      ]
+    },
+    {
+      "id": 40,
+      "type": "String",
+      "pos": [
+        -99.1735806485697,
+        1134.5376124549446
+      ],
+      "size": {
+        "0": 315,
+        "1": 58
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            58
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "String"
+      },
+      "widgets_values": [
+        "浪人"
+      ]
+    },
+    {
+      "id": 39,
+      "type": "Compare",
+      "pos": [
+        306,
+        1091
+      ],
+      "size": {
+        "0": 315,
+        "1": 78
+      },
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "a",
+          "type": "*",
+          "link": 57
+        },
+        {
+          "name": "b",
+          "type": "*",
+          "link": 58
+        }
+      ],
+      "outputs": [
+        {
+          "name": "B",
+          "type": "BOOLEAN",
+          "links": null,
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Compare"
+      },
+      "widgets_values": [
+        "a == b"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "LoadImage",
+      "pos": [
+        -1091,
+        346
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            36,
+            44
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": [],
+          "shape": 3,
+          "slot_index": 1
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "textract_demo005.png",
+        "image"
+      ]
+    },
+    {
+      "id": 17,
+      "type": "Prompt Template",
+      "pos": [
+        -398,
+        661
+      ],
+      "size": {
+        "0": 521.4636840820312,
+        "1": 208.76742553710938
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "prompt",
+          "type": "STRING",
+          "link": 38,
+          "widget": {
+            "name": "prompt"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            30,
+            57
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "Prompt Template"
+      },
+      "widgets_values": [
+        "",
+        "翻译如下英文为日语\n[prompt]\n直接返回翻译后文本"
+      ]
+    }
+  ],
+  "links": [
+    [
+      20,
+      11,
+      0,
+      2,
+      0,
+      "IMAGE"
+    ],
+    [
+      28,
+      9,
+      0,
+      11,
+      1,
+      "IMAGE"
+    ],
+    [
+      29,
+      15,
+      0,
+      9,
+      1,
+      "STRING"
+    ],
+    [
+      30,
+      17,
+      0,
+      15,
+      0,
+      "STRING"
+    ],
+    [
+      36,
+      4,
+      0,
+      23,
+      0,
+      "IMAGE"
+    ],
+    [
+      38,
+      23,
+      0,
+      17,
+      0,
+      "STRING"
+    ],
+    [
+      39,
+      23,
+      1,
+      9,
+      2,
+      "INT"
+    ],
+    [
+      40,
+      23,
+      2,
+      9,
+      3,
+      "INT"
+    ],
+    [
+      41,
+      23,
+      3,
+      9,
+      4,
+      "INT"
+    ],
+    [
+      42,
+      23,
+      4,
+      9,
+      5,
+      "INT"
+    ],
+    [
+      44,
+      4,
+      0,
+      24,
+      0,
+      "IMAGE"
+    ],
+    [
+      45,
+      24,
+      0,
+      9,
+      0,
+      "*"
+    ],
+    [
+      46,
+      24,
+      0,
+      11,
+      0,
+      "IMAGE"
+    ],
+    [
+      47,
+      24,
+      0,
+      25,
+      0,
+      "IMAGE"
+    ],
+    [
+      49,
+      23,
+      5,
+      32,
+      0,
+      "IMAGE"
+    ],
+    [
+      50,
+      32,
+      0,
+      33,
+      0,
+      "MASK"
+    ],
+    [
+      56,
+      32,
+      0,
+      24,
+      1,
+      "MASK"
+    ],
+    [
+      57,
+      17,
+      0,
+      39,
+      0,
+      "*"
+    ],
+    [
+      58,
+      40,
+      0,
+      39,
+      1,
+      "*"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 1.1000000000000005,
+      "offset": {
+        "0": 986.1125011031153,
+        "1": -372.55607836403607
+      }
+    }
+  },
+  "version": 0.4
+}
\ No newline at end of file

From eb43716a6c5b4b5fcd8842dc7f9df80287839640 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Wed, 25 Dec 2024 22:01:03 +0800
Subject: [PATCH 18/30] update nova node

---
 nodes/bedrock_nova_image.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/nodes/bedrock_nova_image.py b/nodes/bedrock_nova_image.py
index 598c836..585ce0c 100644
--- a/nodes/bedrock_nova_image.py
+++ b/nodes/bedrock_nova_image.py
@@ -92,8 +92,7 @@ class BedrockNovaTextImage:
         # For the format, ranges, and default values for Titan Image models refer to:
         # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html
 
-        inference_params =
-        {
+        inference_params ={
                 "taskType": "TEXT_IMAGE",
                 "textToImageParams": {"text": prompt},
                 "imageGenerationConfig": {

From 96f063499aa79a5ad5c12665c7df965b9d1d206b Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 26 Dec 2024 15:37:53 +0800
Subject: [PATCH 19/30] update comments

---
 nodes/bedrock_nova_image.py | 128 +++++++++++++++++++----
 nodes/bedrock_nova_video.py | 196 ++++++++++++++++++++++++++++++++++++
 nodes/nova_image_gen.py     | 115 ---------------------
 3 files changed, 303 insertions(+), 136 deletions(-)
 create mode 100644 nodes/bedrock_nova_video.py
 delete mode 100644 nodes/nova_image_gen.py

diff --git a/nodes/bedrock_nova_image.py b/nodes/bedrock_nova_image.py
index 585ce0c..8ad32cc 100644
--- a/nodes/bedrock_nova_image.py
+++ b/nodes/bedrock_nova_image.py
@@ -4,8 +4,6 @@ import re
 import base64
 from io import BytesIO
 from random import randint
-import nova_image_gen
-import file_utils
 import logging
 from datetime import datetime
 import requests
@@ -14,11 +12,104 @@ from PIL import Image
 import numpy as np
 import torch
 from .session import get_client
+import boto3
 
 MAX_RETRY = 3
 
-bedrock_runtime_client = get_client(service_name="bedrock-runtime")
+def generate_images(
+    inference_params,
+    model_id="amazon.nova-canvas-v1:0",
+    region_name="us-east-1",
+    endpoint_url=None,
+    output_directory="~/ComfyUI/output",
+):
+    
+    os.makedirs(output_directory, exist_ok=True)
 
+    image_count = 1
+    if "imageGenerationConfig" in inference_params:
+        if "numberOfImages" in inference_params["imageGenerationConfig"]:
+            image_count = inference_params["imageGenerationConfig"]["numberOfImages"]
+
+    print(f"Generating {image_count} image(s) with {model_id}")
+
+    # Display the seed value if one is being used.
+    if "imageGenerationConfig" in inference_params:
+        if "seed" in inference_params["imageGenerationConfig"]:
+            print(
+                f"Using seed: {inference_params['imageGenerationConfig']['seed']}"
+            )
+
+    bedrock_client_optional_args = (
+        {} if endpoint_url is None else {"endpoint_url": endpoint_url}
+    )
+    bedrock = boto3.client(
+        service_name="bedrock-runtime",
+        region_name=region_name,
+        **bedrock_client_optional_args,
+    )
+
+    body_json = json.dumps(inference_params, indent=2)
+    print("here1===",body_json)
+
+    # Write request body to JSON file.
+    request_file_path = os.path.join(output_directory, "request.json")
+    with open(request_file_path, "w") as f:
+        f.write(body_json)
+
+    try:
+        response = bedrock.invoke_model(
+            body=body_json,
+            modelId=model_id,
+            accept="application/json",
+            contentType="application/json",
+        )
+
+        response_metadata = response.get("ResponseMetadata")
+        # WRite response metadata to JSON file.
+        response_metadata_file_path = os.path.join(
+            output_directory, "response_metadata.json"
+        )
+        with open(response_metadata_file_path, "w") as f:
+            json.dump(response_metadata, f, indent=2)
+
+        response_body = json.loads(response.get("body").read())
+
+        # Write response body to JSON file.
+        response_file_path = os.path.join(output_directory, "response_body.json")
+        with open(response_file_path, "w") as f:
+            json.dump(response_body, f, indent=2)
+
+        # Log the request ID.
+        print(f"Request ID: {response['ResponseMetadata']['RequestId']}")
+
+        # Check for non-exception errors.
+        if "error" in response_body:
+            if response_body["error"] == "":
+                print(
+                    """Response included 'error' of "" (empty string). This indicates a bug with the Bedrock API."""
+                )
+            else:
+                printf("Error: {response_body['error']}")
+
+        return response_body
+
+    except Exception as e:
+        # If e has "response", write it to disk as JSON.
+        if hasattr(e, "response"):
+            error_response = e.response
+            error_response_file_path = os.path.join(
+                output_directory, "error_response.json"
+            )
+            with open(error_response_file_path, "w") as f:
+                json.dump(error_response, f, indent=2)
+
+        print(e)
+        raise e
+
+
+bedrock_runtime_client = get_client(service_name="bedrock-runtime")
+output_directory = "/home/ubuntu/ComfyUI/output/"
 class BedrockNovaTextImage:
     @classmethod
     def INPUT_TYPES(s):
@@ -76,6 +167,9 @@ class BedrockNovaTextImage:
                         "display": "number",
                     },
                 ),
+            },
+            "optional": {
+                "negative_prompt":(("STRING", {"multiline": True}),)
             }
         }
 
@@ -84,17 +178,14 @@ class BedrockNovaTextImage:
     CATEGORY = "aws"
 
     @retry(tries=MAX_RETRY)
-    def forward(self, prompt, num_images, quality, resolution, cfg_scale, seed):
+    def forward(self, prompt, negative_prompt,num_images, quality, resolution, cfg_scale, seed):
 
         height, width = map(int, re.findall(r"\d+", resolution))
 
-        # The different model providers have individual request and response formats.
-        # For the format, ranges, and default values for Titan Image models refer to:
-        # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html
-
         inference_params ={
                 "taskType": "TEXT_IMAGE",
-                "textToImageParams": {"text": prompt},
+                "textToImageParams": {"text": prompt,
+                                      "negativeText":negative_prompt},
                 "imageGenerationConfig": {
                     "numberOfImages": num_images,
                     "quality": quality,
@@ -110,7 +201,7 @@ class BedrockNovaTextImage:
         output_directory = f"output/{generation_id}"
 
         # Generate the image(s).
-        response = nova_image_gen.generate_images(
+        response = generate_images(
             inference_params=inference_params,
             model_id="amazon.nova-canvas-v1:0",
             output_directory=output_directory
@@ -204,7 +295,7 @@ class BedrockNovaIpAdatper:
     CATEGORY = "aws"
 
     @retry(tries=MAX_RETRY)
-    def forward(self, image, prompt, negative_prompt, similarity_strength, num_images, cfg_scale, resolution):
+    def forward(self, image, prompt, negative_prompt, similarity_strength, num_images, cfg_scale, resolution,seed):
 
         image = image[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
@@ -218,14 +309,9 @@ class BedrockNovaIpAdatper:
 
         image_base64 = base64.b64encode(image_data).decode("utf-8")
 
-        # The different model providers have individual request and response formats.
-        # For the format, ranges, and default values for Titan Image models refer to:
-        # https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-titan-image.html
-
-        # add a key value to json only when the a parameter is not empty
         inference_params = {
                 "taskType": "IMAGE_VARIATION",
-                "inPaintingParams": {
+                "imageVariationParams": {
                     "image": image_base64,                         
                     "text": prompt,
                     "negativeText": negative_prompt,        
@@ -241,7 +327,7 @@ class BedrockNovaIpAdatper:
                 }
             }
         
-        response_body = nova_image_gen.generate_images(
+        response_body = generate_images(
             inference_params=inference_params,
             model_id="amazon.nova-canvas-v1:0",
             output_directory=output_directory
@@ -311,7 +397,7 @@ class BedrockNovaBackgroundPEReplace:
     CATEGORY = "aws"
 
     @retry(tries=MAX_RETRY)
-    def forward(self, image, prompt, mask_prompt, num_images, cfg_scale):
+    def forward(self, image, prompt, mask_prompt, num_images, cfg_scale,seed):
         image = image[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
 
@@ -325,7 +411,7 @@ class BedrockNovaBackgroundPEReplace:
 
         inference_params = {
                 "taskType": "OUTPAINTING",
-                "inPaintingParams": {
+                "outPaintingParams": {
                     "image": image_base64,
                     "text": prompt,
                     "maskPrompt": mask_prompt,
@@ -339,7 +425,7 @@ class BedrockNovaBackgroundPEReplace:
                 }
             }
 
-        response_body = nova_image_gen.generate_images(
+        response_body = generate_images(
             inference_params=inference_params,
             model_id="amazon.nova-canvas-v1:0",
             output_directory=output_directory
diff --git a/nodes/bedrock_nova_video.py b/nodes/bedrock_nova_video.py
new file mode 100644
index 0000000..b18a852
--- /dev/null
+++ b/nodes/bedrock_nova_video.py
@@ -0,0 +1,196 @@
+import json
+import os
+import re
+import base64
+from io import BytesIO
+from random import randint
+import logging
+from datetime import datetime
+import requests
+from retry import retry
+from PIL import Image
+import numpy as np
+import torch
+from .session import get_client
+import time
+import boto3
+
+MAX_RETRY = 3
+def get_default_region():
+    """获取默认的 AWS 区域"""
+    session = boto3.Session()
+    return session.region_name
+
+def get_account_id():
+    """获取当前 AWS 账户 ID"""
+    sts_client = boto3.client('sts')
+    return sts_client.get_caller_identity().get('Account')
+
+
+def download_video_for_invocation_arn(invocation_arn, bucket_name, destination_folder):
+    """
+    This function downloads the video file for the given invocation ARN.
+    """
+    invocation_id = invocation_arn.split("/")[-1]
+
+    # Create the local file path
+    file_name = f"{invocation_id}.mp4"
+    import os
+
+    output_folder = os.path.abspath(destination_folder)
+    local_file_path = os.path.join(output_folder, file_name)
+
+    # Ensure the output folder exists
+    os.makedirs(output_folder, exist_ok=True)
+
+    # Create an S3 client
+    s3 = boto3.client("s3")
+
+    # List objects in the specified folder
+    response = s3.list_objects_v2(Bucket=bucket_name, Prefix=invocation_id)
+
+    # Find the first MP4 file and download it.
+    for obj in response.get("Contents", []):
+        object_key = obj["Key"]
+        if object_key.endswith(".mp4"):
+            print(f"""Downloading "{object_key}"...""")
+            s3.download_file(bucket_name, object_key, local_file_path)
+            print(f"Downloaded to {local_file_path}")
+            return local_file_path
+
+    # If we reach this point, no MP4 file was found.
+    print(f"Problem: No MP4 file was found in S3 at {bucket_name}/{invocation_id}")
+
+def get_job_id_from_arn(invocation_arn):
+    return invocation_arn.split("/")[-1]
+
+def save_completed_job(job, output_folder="output"):
+    job_id = get_job_id_from_arn(job["invocationArn"])
+
+    output_folder_abs = os.path.abspath(
+        f"{output_folder}/{get_folder_name_for_job(job)}"
+    )
+
+    # Ensure the output folder exists
+    os.makedirs(output_folder_abs, exist_ok=True)
+
+    status_file = os.path.join(output_folder_abs, "completed.json")
+
+    if is_video_downloaded_for_invocation_job(job, output_folder=output_folder):
+        print(f"Skipping completed job {job_id}, video already downloaded.")
+        return
+
+    s3_bucket_name = (
+        job["outputDataConfig"]["s3OutputDataConfig"]["s3Uri"]
+        .split("//")[1]
+        .split("/")[0]
+    )
+
+    localPath = download_video_for_invocation_arn(
+        job["invocationArn"], s3_bucket_name, output_folder_abs
+    )
+    return localPath
+
+
+bedrock_runtime_client = get_client(service_name="bedrock-runtime")
+region = get_default_region()
+account_id = get_account_id()
+s3_destination_bucket = f"sagemaker-{region}-{account-id}"
+
+class BedrockNovaVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "prompt": ("STRING", {"multiline": True}),
+                "dimension": (
+                    [
+                        "1024 x 720"
+                    ],
+                ),
+                "seed": (
+                    "INT",
+                    {
+                        "default": 0,
+                        "min": 0,
+                        "max": 2147483646,
+                        "step": 1,
+                        "round": 1,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "number",
+                    },
+                ),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+
+    @retry(tries=MAX_RETRY)
+    def forward(self, image, prompt, dimension,seed):
+        input_image_base64=None
+        if image:
+            image = image[0] * 255.0
+            image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+            buffer = BytesIO()
+            image.save(buffer, format="PNG")
+
+            image_data = buffer.getvalue()
+            input_image_base64 = base64.b64encode(image_data).decode("utf-8")
+
+        model_input = {
+            "taskType": "TEXT_VIDEO",
+            "textToVideoParams": {
+                "text": video_prompt,
+                "images": [
+                    {
+                        "format": "png",  # May be "png" or "jpeg"
+                        "source": {
+                            "bytes": input_image_base64
+                        }
+                    }
+                ]
+                },
+            "videoGenerationConfig": {
+                "durationSeconds": 6,  # 6 is the only supported value currently.
+                "fps": 24,  # 24 is the only supported value currently.
+                "dimension": dimension,  # "1280x720" is the only supported value currently.
+                "seed": seed # A random seed guarantees we'll get a different result each time this code runs.
+            },
+        }
+        
+        
+        invocation = bedrock_runtime.start_async_invoke(
+        modelId="amazon.olympus-video-generator-v1:0",
+        modelInput=model_input,
+        outputDataConfig={"s3OutputDataConfig": {"s3Uri": f"s3://{s3_destination_bucket}"}},
+    )
+
+        invocation_arn = invocation["invocationArn"]
+        print("\nResponse:")
+        print(json.dumps(invocation, indent=2, default=str))
+        
+        save_local_path = ""
+    
+        # Save the invocation details for later reference. Helpful for debugging and reporting feedback.
+        while True:
+            job_update = bedrock_runtime.get_async_invoke(invocationArn=invocation_arn)
+            status = job_update["status"]
+
+            if status == "Completed":
+                save_local_path = save_completed_job(job_update, output_folder="/home/ubuntu/ComfyUI/output/")
+                break
+            else:
+                time.sleep(5)
+        return (save_local_path,)
+
+
+
+
+
+NODE_CLASS_MAPPINGS = {
+    "Bedrock - Nova Video": BedrockNovaVideo
+}
\ No newline at end of file
diff --git a/nodes/nova_image_gen.py b/nodes/nova_image_gen.py
deleted file mode 100644
index 09f4b35..0000000
--- a/nodes/nova_image_gen.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import json
-import logging
-import os
-from datetime import datetime
-
-import boto3
-
-logger = logging.getLogger(__name__)
-
-
-def generate_images(
-    inference_params,
-    model_id="amazon.nova-canvas-v1:0",
-    region_name="us-east-1",
-    endpoint_url=None,
-    output_directory="~/ComfyUI/output",
-):
-    """
-    Generate an image using using Titan Image Generator or Olympus Image Generator.
-    Args:
-        body (dict) : The request body to use.
-        model_id (str): The model ID to use. Defaults to "amazon.titan-image-generator-v1".
-        region_name (str): The AWS region name to use. Defaults to "us-east-1".
-        endpoint_url (str): The endpoint URL to use. Defaults to None.
-        output_directory (str): The directory to save the generated images. Defaults to "./output".
-    Returns:
-        response_body (dict): The response body from the model.
-        job_output_directory (str): The directory where the request and response JSON values are saved.
-        generation_id (str): The generation ID for the request.
-    Usage:
-        response_body, job_output_directory, generation_id = generate_images(inference_params)
-    Raises:
-        Exception: Any exception thrown by .invoke_model().
-    """
-    os.makedirs(output_directory, exist_ok=True)
-
-    image_count = 1
-    if "imageGenerationConfig" in inference_params:
-        if "numberOfImages" in inference_params["imageGenerationConfig"]:
-            image_count = inference_params["imageGenerationConfig"]["numberOfImages"]
-
-    logger.info(f"Generating {image_count} image(s) with {model_id}")
-
-    # Display the seed value if one is being used.
-    if "imageGenerationConfig" in inference_params:
-        if "seed" in inference_params["imageGenerationConfig"]:
-            logger.info(
-                f"Using seed: {inference_params['imageGenerationConfig']['seed']}"
-            )
-
-    bedrock_client_optional_args = (
-        {} if endpoint_url is None else {"endpoint_url": endpoint_url}
-    )
-    bedrock = boto3.client(
-        service_name="bedrock-runtime",
-        region_name=region_name,
-        **bedrock_client_optional_args,
-    )
-
-    body_json = json.dumps(inference_params, indent=2)
-
-    # Write request body to JSON file.
-    request_file_path = os.path.join(output_directory, "request.json")
-    with open(request_file_path, "w") as f:
-        f.write(body_json)
-
-    try:
-        response = bedrock.invoke_model(
-            body=body_json,
-            modelId=model_id,
-            accept="application/json",
-            contentType="application/json",
-        )
-
-        response_metadata = response.get("ResponseMetadata")
-        # WRite response metadata to JSON file.
-        response_metadata_file_path = os.path.join(
-            output_directory, "response_metadata.json"
-        )
-        with open(response_metadata_file_path, "w") as f:
-            json.dump(response_metadata, f, indent=2)
-
-        response_body = json.loads(response.get("body").read())
-
-        # Write response body to JSON file.
-        response_file_path = os.path.join(output_directory, "response_body.json")
-        with open(response_file_path, "w") as f:
-            json.dump(response_body, f, indent=2)
-
-        # Log the request ID.
-        logger.info(f"Request ID: {response['ResponseMetadata']['RequestId']}")
-
-        # Check for non-exception errors.
-        if "error" in response_body:
-            if response_body["error"] == "":
-                logger.warning(
-                    """Response included 'error' of "" (empty string). This indicates a bug with the Bedrock API."""
-                )
-            else:
-                logger.warning(f"Error: {response_body['error']}")
-
-        return response_body
-
-    except Exception as e:
-        # If e has "response", write it to disk as JSON.
-        if hasattr(e, "response"):
-            error_response = e.response
-            error_response_file_path = os.path.join(
-                output_directory, "error_response.json"
-            )
-            with open(error_response_file_path, "w") as f:
-                json.dump(error_response, f, indent=2)
-
-        logger.error(e)
-        raise e

From 2b8a53f8c2320ace7c24603720d4f7d585f27c6d Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Wed, 8 Jan 2025 20:32:09 +0800
Subject: [PATCH 20/30] update default nova region

---
 nodes/bedrock_nova_image.py |  6 +++---
 nodes/bedrock_nova_video.py | 43 +++++++++++++++++++++++++++++--------
 2 files changed, 37 insertions(+), 12 deletions(-)

diff --git a/nodes/bedrock_nova_image.py b/nodes/bedrock_nova_image.py
index 8ad32cc..9721920 100644
--- a/nodes/bedrock_nova_image.py
+++ b/nodes/bedrock_nova_image.py
@@ -45,12 +45,12 @@ def generate_images(
     )
     bedrock = boto3.client(
         service_name="bedrock-runtime",
-        region_name=region_name,
+        #region_name=region_name,
         **bedrock_client_optional_args,
     )
 
     body_json = json.dumps(inference_params, indent=2)
-    print("here1===",body_json)
+    #print("here1===",body_json)
 
     # Write request body to JSON file.
     request_file_path = os.path.join(output_directory, "request.json")
@@ -312,7 +312,7 @@ class BedrockNovaIpAdatper:
         inference_params = {
                 "taskType": "IMAGE_VARIATION",
                 "imageVariationParams": {
-                    "image": image_base64,                         
+                    "images": [image_base64],                         
                     "text": prompt,
                     "negativeText": negative_prompt,        
                     "similarityStrength": similarity_strength,
diff --git a/nodes/bedrock_nova_video.py b/nodes/bedrock_nova_video.py
index b18a852..8e4d292 100644
--- a/nodes/bedrock_nova_video.py
+++ b/nodes/bedrock_nova_video.py
@@ -26,6 +26,25 @@ def get_account_id():
     sts_client = boto3.client('sts')
     return sts_client.get_caller_identity().get('Account')
 
+def is_video_downloaded_for_invocation_job(invocation_job, output_folder="output"):
+    """
+    This function checks if the video file for the given invocation job has been downloaded.
+    """
+    invocation_arn = invocation_job["invocationArn"]
+    invocation_id = invocation_arn.split("/")[-1]
+    folder_name = get_folder_name_for_job(invocation_job)
+    output_folder = os.path.abspath(f"{output_folder}/{folder_name}")
+    file_name = f"{invocation_id}.mp4"
+    local_file_path = os.path.join(output_folder, file_name)
+    return os.path.exists(local_file_path)
+
+def get_folder_name_for_job(invocation_job):
+    invocation_arn = invocation_job["invocationArn"]
+    invocation_id = invocation_arn.split("/")[-1]
+    submit_time = invocation_job["submitTime"]
+    timestamp = submit_time.astimezone().strftime("%Y-%m-%d_%H-%M-%S")
+    folder_name = f"{timestamp}_{invocation_id}"
+    return folder_name
 
 def download_video_for_invocation_arn(invocation_arn, bucket_name, destination_folder):
     """
@@ -95,7 +114,7 @@ def save_completed_job(job, output_folder="output"):
 bedrock_runtime_client = get_client(service_name="bedrock-runtime")
 region = get_default_region()
 account_id = get_account_id()
-s3_destination_bucket = f"sagemaker-{region}-{account-id}"
+s3_destination_bucket = f"sagemaker-{region}-{account_id}"
 
 class BedrockNovaVideo:
     @classmethod
@@ -105,7 +124,7 @@ class BedrockNovaVideo:
                 "prompt": ("STRING", {"multiline": True}),
                 "dimension": (
                     [
-                        "1024 x 720"
+                        "1280x720"
                     ],
                 ),
                 "seed": (
@@ -130,9 +149,9 @@ class BedrockNovaVideo:
     CATEGORY = "aws"
 
     @retry(tries=MAX_RETRY)
-    def forward(self, image, prompt, dimension,seed):
+    def forward(self, prompt, dimension,seed,image):
         input_image_base64=None
-        if image:
+        if image is not None:
             image = image[0] * 255.0
             image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
             buffer = BytesIO()
@@ -144,7 +163,7 @@ class BedrockNovaVideo:
         model_input = {
             "taskType": "TEXT_VIDEO",
             "textToVideoParams": {
-                "text": video_prompt,
+                "text": prompt,
                 "images": [
                     {
                         "format": "png",  # May be "png" or "jpeg"
@@ -163,8 +182,8 @@ class BedrockNovaVideo:
         }
         
         
-        invocation = bedrock_runtime.start_async_invoke(
-        modelId="amazon.olympus-video-generator-v1:0",
+        invocation = bedrock_runtime_client.start_async_invoke(
+        modelId="amazon.nova-reel-v1:0",
         modelInput=model_input,
         outputDataConfig={"s3OutputDataConfig": {"s3Uri": f"s3://{s3_destination_bucket}"}},
     )
@@ -177,14 +196,20 @@ class BedrockNovaVideo:
     
         # Save the invocation details for later reference. Helpful for debugging and reporting feedback.
         while True:
-            job_update = bedrock_runtime.get_async_invoke(invocationArn=invocation_arn)
+            job_update = bedrock_runtime_client.get_async_invoke(invocationArn=invocation_arn)
             status = job_update["status"]
+            start_time = time.time()
 
             if status == "Completed":
                 save_local_path = save_completed_job(job_update, output_folder="/home/ubuntu/ComfyUI/output/")
                 break
             else:
-                time.sleep(5)
+                elapsed_time = time.time() - start_time
+                if elapsed_time > 600:
+                    print("Job timed out after 60 seconds.")
+                    break
+                else:
+                    time.sleep(5)
         return (save_local_path,)
 
 

From 4daaf3bacd1e6df4f897ee3f374107b684418424 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 9 Jan 2025 08:42:36 +0800
Subject: [PATCH 21/30] add nova llm node

---
 nodes/bedrock.py | 125 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 125 insertions(+)

diff --git a/nodes/bedrock.py b/nodes/bedrock.py
index ed12df0..c459a37 100644
--- a/nodes/bedrock.py
+++ b/nodes/bedrock.py
@@ -20,6 +20,131 @@ CLAUDE3_MAX_SIZE = 1568
 bedrock_runtime_client = get_client(service_name="bedrock-runtime")
 
 
+
+class BedrockNovaMultimodal:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "prompt": ("STRING", {"multiline": True}),
+                "model_id": (
+                    [
+                        "amazon.nova-pro-v1:0",
+                        "amazon.nova-lite-v1:0",
+                    ],
+                ),
+                "maxTokens": (
+                    "INT",
+                    {
+                        "default": 2048,
+                        "min": 0,  # Minimum value
+                        "max": 4096,  # Maximum value
+                        "step": 1,  # Slider's step
+                        "display": "number",  # Cosmetic only: display as "number" or "slider"
+                    },
+                ),
+                "temperature": (
+                    "FLOAT",
+                    {
+                        "default": 0.5,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.1,
+                        "round": 0.001,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "slider",
+                    },
+                ),
+                "topP": (
+                    "FLOAT",
+                    {
+                        "default": 1.0,
+                        "min": 0.0,
+                        "max": 1.0,
+                        "step": 0.1,
+                        "round": 0.001,  # The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                        "display": "slider",
+                    },
+                )
+            }
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "forward"
+    CATEGORY = "aws"
+
+    @retry(tries=MAX_RETRY)
+    def forward(
+        self,
+        image,
+        prompt,
+        model_id,
+        max_tokens,
+        temperature,
+        top_p,
+        top_k,
+    ):
+        """
+        Invokes the Anthropic Claude model to run an inference using the input
+        provided in the request body.
+
+        :param prompt: The prompt that you want Claude to complete.
+        :return: Inference response from the model.
+        """
+
+        image = image[0] * 255.0
+        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+
+        width, height = image.size
+        max_size = max(width, height)
+        if max_size > CLAUDE3_MAX_SIZE:
+            width = round(width * CLAUDE3_MAX_SIZE / max_size)
+            height = round(height * CLAUDE3_MAX_SIZE / max_size)
+            image = image.resize((width, height))
+
+        buffer = BytesIO()
+        image.save(buffer, format="webp", quality=80)
+        image_data = buffer.getvalue()
+
+        image_base64 = base64.b64encode(image_data).decode("utf-8")
+
+        body = json.dumps(
+            {
+                "anthropic_version": "bedrock-2023-05-31",
+                "maxTokens": max_tokens,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            {
+                                "type": "image",
+                                "source": {
+                                    "type": "base64",
+                                    "media_type": "image/webp",
+                                    "data": image_base64,
+                                },
+                            },
+                            {
+                                "type": "text",
+                                "text": prompt,
+                            },
+                        ],
+                    }
+                ],
+                "temperature": temperature,
+                "topP": top_p,
+            },
+            ensure_ascii=False,
+        )
+
+        response = bedrock_runtime_client.invoke_model(
+            body=body,
+            modelId=model_id,
+        )
+        message = json.loads(response.get("body").read())["content"][0]["text"]
+
+        return (message,)
+
 class BedrockClaudeMultimodal:
     @classmethod
     def INPUT_TYPES(s):

From ba7a0dd21787ec69872dba5c8b54d89251cf7957 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 9 Jan 2025 08:47:18 +0800
Subject: [PATCH 22/30] add nova llm node

---
 nodes/bedrock.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/nodes/bedrock.py b/nodes/bedrock.py
index c459a37..025bcd2 100644
--- a/nodes/bedrock.py
+++ b/nodes/bedrock.py
@@ -574,4 +574,5 @@ NODE_CLASS_MAPPINGS = {
     "Bedrock - Claude": BedrockClaude,
     "Bedrock - Claude Multimodal": BedrockClaudeMultimodal,
     "Bedrock - SDXL": BedrockSDXL,
+    "Bedrock = Nova": BedrockNovaMultimodal
 }

From 76256e8d14c53cfef747a50faef9b77798dfb876 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 9 Jan 2025 08:58:14 +0800
Subject: [PATCH 23/30] update nova llm input option

---
 nodes/bedrock.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/nodes/bedrock.py b/nodes/bedrock.py
index 025bcd2..d1ea418 100644
--- a/nodes/bedrock.py
+++ b/nodes/bedrock.py
@@ -26,7 +26,6 @@ class BedrockNovaMultimodal:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "image": ("IMAGE",),
                 "prompt": ("STRING", {"multiline": True}),
                 "model_id": (
                     [
@@ -66,6 +65,9 @@ class BedrockNovaMultimodal:
                         "display": "slider",
                     },
                 )
+            },
+            "optional": {
+                "image": ("IMAGE",),
             }
         }
 
@@ -574,5 +576,5 @@ NODE_CLASS_MAPPINGS = {
     "Bedrock - Claude": BedrockClaude,
     "Bedrock - Claude Multimodal": BedrockClaudeMultimodal,
     "Bedrock - SDXL": BedrockSDXL,
-    "Bedrock = Nova": BedrockNovaMultimodal
+    "Bedrock - Nova": BedrockNovaMultimodal
 }

From 4c0ed234aaf569bd776e48e05c8a0d1e8b2b7a72 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 9 Jan 2025 09:02:46 +0800
Subject: [PATCH 24/30] update nova llm input option

---
 nodes/bedrock.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nodes/bedrock.py b/nodes/bedrock.py
index d1ea418..58fec8a 100644
--- a/nodes/bedrock.py
+++ b/nodes/bedrock.py
@@ -81,7 +81,7 @@ class BedrockNovaMultimodal:
         image,
         prompt,
         model_id,
-        max_tokens,
+        maxTokens,
         temperature,
         top_p,
         top_k,

From cce3ee7a40ebdc0e9cfd202c9e86f438dae09e7d Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 9 Jan 2025 09:06:32 +0800
Subject: [PATCH 25/30] update nova llm input option

---
 nodes/bedrock.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/nodes/bedrock.py b/nodes/bedrock.py
index 58fec8a..c60b5b3 100644
--- a/nodes/bedrock.py
+++ b/nodes/bedrock.py
@@ -83,8 +83,7 @@ class BedrockNovaMultimodal:
         model_id,
         maxTokens,
         temperature,
-        top_p,
-        top_k,
+        topP,
     ):
         """
         Invokes the Anthropic Claude model to run an inference using the input

From b096adb43d96097e410dfee5ac9aa13234937ede Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Thu, 9 Jan 2025 14:03:52 +0800
Subject: [PATCH 26/30] update nova video input parameter

---
 nodes/bedrock.py            | 91 ++++++++++++++++++-------------------
 nodes/bedrock_nova_video.py | 30 +++++++-----
 2 files changed, 62 insertions(+), 59 deletions(-)

diff --git a/nodes/bedrock.py b/nodes/bedrock.py
index c60b5b3..2d61090 100644
--- a/nodes/bedrock.py
+++ b/nodes/bedrock.py
@@ -67,7 +67,7 @@ class BedrockNovaMultimodal:
                 )
             },
             "optional": {
-                "image": ("IMAGE",),
+                "image": ("IMAGE",{"default": None}),
             }
         }
 
@@ -78,62 +78,58 @@ class BedrockNovaMultimodal:
     @retry(tries=MAX_RETRY)
     def forward(
         self,
-        image,
-        prompt,
-        model_id,
-        maxTokens,
-        temperature,
-        topP,
+        **kwargs
     ):
-        """
-        Invokes the Anthropic Claude model to run an inference using the input
-        provided in the request body.
+        
+        prompt = kwargs.get('prompt')
+        model_id = kwargs.get('model_id')
+        maxTokens = kwargs.get('maxTokens')
+        temperature = kwargs.get('temperature')
+        topP = kwargs.get('topP')
+        image = kwargs.get('image')
+        
+        content = []
+        content.append({
+                            "text": prompt,
+                        })
 
-        :param prompt: The prompt that you want Claude to complete.
-        :return: Inference response from the model.
-        """
+        if image is not None:
+            image = image[0] * 255.0
+            image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
+    
+            width, height = image.size
+            max_size = max(width, height)
+            if max_size > CLAUDE3_MAX_SIZE:
+                width = round(width * CLAUDE3_MAX_SIZE / max_size)
+                height = round(height * CLAUDE3_MAX_SIZE / max_size)
+                image = image.resize((width, height))
+    
+            buffer = BytesIO()
+            image.save(buffer, format="png", quality=80)
+            image_data = buffer.getvalue()
+    
+            image_base64 = base64.b64encode(image_data).decode("utf-8")
+            content.append( {
+                "image": {
+                    "format": "png",
+                    "source": {"bytes": image_base64},
+                }
+              }
+            )
+            
+        inf_params = {"max_new_tokens": maxTokens, "top_p": topP, "temperature": temperature}
 
-        image = image[0] * 255.0
-        image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
-
-        width, height = image.size
-        max_size = max(width, height)
-        if max_size > CLAUDE3_MAX_SIZE:
-            width = round(width * CLAUDE3_MAX_SIZE / max_size)
-            height = round(height * CLAUDE3_MAX_SIZE / max_size)
-            image = image.resize((width, height))
-
-        buffer = BytesIO()
-        image.save(buffer, format="webp", quality=80)
-        image_data = buffer.getvalue()
-
-        image_base64 = base64.b64encode(image_data).decode("utf-8")
 
         body = json.dumps(
             {
-                "anthropic_version": "bedrock-2023-05-31",
-                "maxTokens": max_tokens,
+                "schemaVersion": "messages-v1",
                 "messages": [
                     {
                         "role": "user",
-                        "content": [
-                            {
-                                "type": "image",
-                                "source": {
-                                    "type": "base64",
-                                    "media_type": "image/webp",
-                                    "data": image_base64,
-                                },
-                            },
-                            {
-                                "type": "text",
-                                "text": prompt,
-                            },
-                        ],
+                        "content": content,
                     }
                 ],
-                "temperature": temperature,
-                "topP": top_p,
+                "inferenceConfig": inf_params,
             },
             ensure_ascii=False,
         )
@@ -142,8 +138,9 @@ class BedrockNovaMultimodal:
             body=body,
             modelId=model_id,
         )
-        message = json.loads(response.get("body").read())["content"][0]["text"]
 
+        message = json.loads(response.get("body").read())['output']['message']['content'][0]['text']
+        print("here1===",message)
         return (message,)
 
 class BedrockClaudeMultimodal:
diff --git a/nodes/bedrock_nova_video.py b/nodes/bedrock_nova_video.py
index 8e4d292..c53efb4 100644
--- a/nodes/bedrock_nova_video.py
+++ b/nodes/bedrock_nova_video.py
@@ -149,7 +149,13 @@ class BedrockNovaVideo:
     CATEGORY = "aws"
 
     @retry(tries=MAX_RETRY)
-    def forward(self, prompt, dimension,seed,image):
+    def forward(self, **kwargs):
+        prompt = kwargs.get('prompt')
+        dimension = kwargs.get('dimension')
+        seed = kwargs.get('seed')
+        image = kwargs.get('image')
+
+        
         input_image_base64=None
         if image is not None:
             image = image[0] * 255.0
@@ -160,19 +166,19 @@ class BedrockNovaVideo:
             image_data = buffer.getvalue()
             input_image_base64 = base64.b64encode(image_data).decode("utf-8")
 
+        textToVideoParams={ "text": prompt}
+        if input_image_base64 is not None:
+            textToVideoParams["images"] = [
+                {
+                    "format": "png",  # May be "png" or "jpeg"
+                    "source": {
+                        "bytes": input_image_base64
+                    }
+                }
+            ]
         model_input = {
             "taskType": "TEXT_VIDEO",
-            "textToVideoParams": {
-                "text": prompt,
-                "images": [
-                    {
-                        "format": "png",  # May be "png" or "jpeg"
-                        "source": {
-                            "bytes": input_image_base64
-                        }
-                    }
-                ]
-                },
+            "textToVideoParams": textToVideoParams,
             "videoGenerationConfig": {
                 "durationSeconds": 6,  # 6 is the only supported value currently.
                 "fps": 24,  # 24 is the only supported value currently.

From 6e7fa947ab8ce9a848de6e14ac5e2387d87be47d Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Fri, 10 Jan 2025 14:13:49 +0800
Subject: [PATCH 27/30] fix nova text2image issue

---
 nodes/bedrock_nova_image.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nodes/bedrock_nova_image.py b/nodes/bedrock_nova_image.py
index 9721920..7d10c73 100644
--- a/nodes/bedrock_nova_image.py
+++ b/nodes/bedrock_nova_image.py
@@ -201,13 +201,13 @@ class BedrockNovaTextImage:
         output_directory = f"output/{generation_id}"
 
         # Generate the image(s).
-        response = generate_images(
+        response_body = generate_images(
             inference_params=inference_params,
             model_id="amazon.nova-canvas-v1:0",
             output_directory=output_directory
         )
 
-        response_body = json.loads(response["body"].read())
+        # response_body = json.loads(response["body"].read())
         # base64_image_data = response_body["images"][0]
         images = [
             np.array(Image.open(BytesIO(base64.b64decode(base64_image))))

From 9b92104c32c679c487be2cb90490bcb06338e8d9 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Wed, 22 Jan 2025 18:11:58 +0800
Subject: [PATCH 28/30] update comments & readme modes

---
 README.md                    |   8 +++
 assets/nova_video_output.png | Bin 0 -> 679576 bytes
 nodes/bedrock_nova_image.py  |   2 +-
 nodes/textract.py            |  97 +++++++++++++++++------------------
 4 files changed, 56 insertions(+), 51 deletions(-)
 create mode 100644 assets/nova_video_output.png

diff --git a/README.md b/README.md
index 094cfd4..5e7bf6e 100644
--- a/README.md
+++ b/README.md
@@ -108,6 +108,14 @@ Here are models ready for use, more models are coming soon.
   - [X] Claude Instant (1.x)
 
 - Amazon:
+  - Nova Canvas
+    - [X] text to image
+    - [X] variation
+    - [X] background replacement(with prompt)
+  
+  - Nova Reel
+    - [X] text to video
+    - [X] image to video
 
   - Titan Image Generator G1 (1.x)
     - [X] text to image
diff --git a/assets/nova_video_output.png b/assets/nova_video_output.png
new file mode 100644
index 0000000000000000000000000000000000000000..c9148f7706467df1bfbeac78a7d68d5bd6daa0c6
GIT binary patch
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HcmV?d00001

diff --git a/nodes/bedrock_nova_image.py b/nodes/bedrock_nova_image.py
index 7d10c73..300811a 100644
--- a/nodes/bedrock_nova_image.py
+++ b/nodes/bedrock_nova_image.py
@@ -169,7 +169,7 @@ class BedrockNovaTextImage:
                 ),
             },
             "optional": {
-                "negative_prompt":(("STRING", {"multiline": True}),)
+                "negative_prompt":("STRING", {"multiline": True}),
             }
         }
 
diff --git a/nodes/textract.py b/nodes/textract.py
index 1fedcd3..68917c6 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -48,20 +48,20 @@ class ImageOCRByTextract:
         # 获取图像原始尺寸
         img_width, img_height = image.size
 
-        # 调用Textract DetectDocumentText函数
+        ## Call Textract DetectDocumentText function
         byte_stream = io.BytesIO()
         image.save(byte_stream, format='PNG')
         byte_image = byte_stream.getvalue()
         response = textract.detect_document_text(Document={'Bytes': byte_image})
 
-        # 初始化结果列表
+        #initial the scan result
         result = []
-        # 创建一个与原始图像大小相同的黑色遮罩图像
+        # Create a black mask image of the same size as the original image
         mask = np.zeros_like(numpy_image)
         all_text=""
 
 
-        # 提取文本和边界框信息
+        # Extract text and bounding box information
         for item in response['Blocks']:
             if item['BlockType'] == 'LINE':
                 text = item['Text']
@@ -71,7 +71,7 @@ class ImageOCRByTextract:
                 width = int(box['Width'] * img_width)
                 height = int(box['Height']* img_height)
 
-                # 将信息添加到结果列表中
+                ##Add information to the result list
                 result.append({
                     'Text': text,
                     'Left': left,
@@ -81,16 +81,16 @@ class ImageOCRByTextract:
 
                 })
                 all_text=all_text+text
-                # 对每个文本信息框绘制mask遮罩
-                # 指定矩形框的左上角和右下角坐标
+                #Draw mask for each text information box
+                #Specify the coordinates of the top-left and bottom-right corners of the rectangle
                 temp_mask = np.zeros_like(numpy_image)
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
-                # 在遮罩图像上绘制白色矩形框
+                # Draw white rectangular boxes on the mask image
                 cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (255, 255, 255), -1)
                 mask = cv2.bitwise_or(mask, temp_mask)
 
-        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
+        # Perform bitwise operation between the mask image and the original image to generate the masked image
         masked_img = cv2.bitwise_and(numpy_image, mask)
         masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
 
@@ -130,32 +130,32 @@ class ImageOCRByTextractV2:
         ## 转换ori_image为tensor张量
         pil_image = Image.open(ori_image_path)
         transform = transforms.Compose([
-            transforms.ToTensor(),  # 将 PIL Image 转换为 tensor，并将像素值归一化到 [0, 1]
+            transforms.ToTensor(),  # Convert PIL Image to tensor and normalize pixel values to [0, 1]
         ])
         image_input = transform(pil_image)
 
-        ## image input已经是标准comfyui的image张量格式
+        ## The image input is already in the standard ComfyUI image tensor format
         numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
         image = image_input[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
-        # 获取图像原始尺寸
+        # Get the original dimensions of the image
         img_width, img_height = image.size
 
-        # 调用Textract DetectDocumentText函数
+        # Call the Textract DetectDocumentText function
         byte_stream = io.BytesIO()
         image.save(byte_stream, format='PNG')
         byte_image = byte_stream.getvalue()
         response = textract.detect_document_text(Document={'Bytes': byte_image})
 
-        # 初始化结果列表
+        # Initialize the results list
         result = []
-        # 创建一个与原始图像大小相同的黑色遮罩图像
+        # Create a black mask image with the same size as the original image
         #mask = np.zeros_like(numpy_image)
         mask = np.ones_like(numpy_image) * 255
         all_text=""
 
 
-        # 提取文本和边界框信息
+        # Extract text and bounding box information
         for item in response['Blocks']:
             if item['BlockType'] == 'LINE':
                 text = item['Text']
@@ -165,28 +165,28 @@ class ImageOCRByTextractV2:
                 width = int(box['Width'] * img_width)
                 height = int(box['Height']* img_height)
 
-                # 将信息添加到结果列表中
+                # Add the information to the result list
                 result.append(text)
-                # 对每个文本信息框绘制mask遮罩
-                # 指定矩形框的左上角和右下角坐标
+                # Draw mask for each text information box
+                # Specify the coordinates of the top-left and bottom-right corners of the rectangle
                 temp_mask = np.zeros_like(numpy_image)
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
-                # 在遮罩图像上绘制黑色矩形框
+                # Draw black rectangular boxes on the mask image
                 #cv2.rectangle(temp_mask, (x1, y1), (x2, y2), (0, 0, 0), -1)
                 #mask = cv2.bitwise_or(mask, temp_mask)
 
                 cv2.rectangle(mask, (left, top), (left + width, top + height), (0, 0, 0), -1)
 
-        # 将遮罩图像和原始图像进行位运算,生成遮罩后的图像
+        # Perform bitwise operation between the mask image and the original image to generate the masked image
         masked_img = np.where(mask == 0, 0, numpy_image)
         #masked_img = cv2.bitwise_and(numpy_image, mask)
         masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
         #masked_img.save(temp_img_path)
-        # 将 PyTorch 张量转换为 PIL 图像
+        # Convert PyTorch tensor to PIL image
         to_pil = transforms.ToPILImage()
         pil_image = to_pil(masked_img.squeeze(0))
-        # 保存 PIL 图像
+        # save PIL image
         pil_image.save(temp_img_path)
 
         all_text="|".join(result)
@@ -220,15 +220,15 @@ class ImageOCRByTextractV4:
     OUTPUT_NODE = True
 
     def convert_to_xywh(self,coordinates):
-        # 提取所有的 x 和 y 坐标
+        # Extract all x and y coordinates
         x_coords = [coord[0] for coord in coordinates]
         y_coords = [coord[1] for coord in coordinates]
 
-        # 计算 x offset 和 y offset
+        # Calculate x offset and y offset
         x_offset = min(x_coords)
         y_offset = min(y_coords)
 
-        # 计算 width 和 height
+        # Calculate width and height
         width = max(x_coords) - x_offset
         height = max(y_coords) - y_offset
 
@@ -237,20 +237,18 @@ class ImageOCRByTextractV4:
 
     def ocr_by_paddleocr(self,image_input):
 
-        ## image input已经是标准comfyui的image张量格式
         image = image_input[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
         numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
 
-        # 获取图像原始尺寸
         img_width, img_height = image.size
 
 
-        # 创建临时文件
+        # Create temporary file
         with tempfile.NamedTemporaryFile(delete=False, suffix='.png', dir='/tmp/') as temp_file:
             temp_filename = temp_file.name
 
-        # 保存numpy_image为临时文件
+        # Save numpy_image as a temporary file
         cv2.imwrite(temp_filename, cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR))
 
         ocr = PaddleOCR(
@@ -263,7 +261,7 @@ class ImageOCRByTextractV4:
 
         ocr_results = ocr.ocr(temp_filename, cls=True)[0]
 
-        # 创建一个与原始图像大小相同的遮罩图像
+        # Create a mask image with the same size as the original image
         result = []
         masked_img = numpy_image.copy()
         all_text=""
@@ -273,7 +271,7 @@ class ImageOCRByTextractV4:
         heights=[]
 
 
-        # 提取文本和边界框信息
+        # Extract text and bounding box information
         for line in ocr_results:
              if not isinstance(line, list):
                 continue
@@ -289,15 +287,15 @@ class ImageOCRByTextractV4:
              print(text)
              result.append(text)
 
-             # 对每个文本信息框绘制mask遮罩
-             # 指定矩形框的左上角和右下角坐标
+             # Draw mask for each text information box
+             # Specify the coordinates of the top-left and bottom-right corners of the rectangle
              x1, y1 = int(x_offset), int(y_offset)
              x2, y2 = int(x_offset + width), int(y_offset + height)
-             # 在遮罩图像上绘制黑色矩形框
+             # Draw a black rectangle on the mask image
              cv2.rectangle(masked_img, (x1, y1), (x2, y2), (0, 0, 0), -1)
 
         masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
-        ###汇总结果输出
+
         all_text="|".join(result)
         x_offsets="|".join(x_offsets)
         y_offsets="|".join(y_offsets)
@@ -306,9 +304,9 @@ class ImageOCRByTextractV4:
 
         print("result",result)
 
-        # 添加原始图像输出
+        # Add original image output
         original_img = image_input
-        # 删除临时文件
+        # delete temp files
         os.unlink(temp_filename)
 
         return all_text ,x_offsets,y_offsets,widths,heights,img_width,img_height, masked_img,original_img
@@ -340,23 +338,23 @@ class ImageOCRByTextractV3:
 
     def ocr_by_textract(self,image_input):
 
-        ## image input已经是标准comfyui的image张量格式
+
         image = image_input[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
         numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
 
-        # 获取图像原始尺寸
+
         img_width, img_height = image.size
 
-        # 调用Textract DetectDocumentText函数
+
         byte_stream = io.BytesIO()
         image.save(byte_stream, format='PNG')
         byte_image = byte_stream.getvalue()
         response = textract.detect_document_text(Document={'Bytes': byte_image})
 
-        # 初始化结果列表
+
         result = []
-        # 创建一个与原始图像大小相同的遮罩图像
+
         masked_img = numpy_image.copy()
         all_text=""
         x_offsets=[]
@@ -366,7 +364,6 @@ class ImageOCRByTextractV3:
 
 
 
-        # 提取文本和边界框信息
         for item in response['Blocks']:
             if item['BlockType'] == 'LINE':
                 text = item['Text']
@@ -384,19 +381,19 @@ class ImageOCRByTextractV3:
                 height = int(box['Height']* img_height)
                 heights.append(str(height))
 
-                # 将信息添加到结果列表中
+
                 result.append(text)
-                # 对每个文本信息框绘制mask遮罩
-                # 指定矩形框的左上角和右下角坐标
+                # Draw mask for each text information box
+                # Specify the coordinates of the top-left and bottom-right corners of the rectangle
                 x1, y1 = int(left), int(top)
                 x2, y2 = int(left + width), int(top + height)
-                # 在遮罩图像上绘制黑色矩形框
+                # Draw black rectangular boxes on the mask image
                 cv2.rectangle(masked_img, (x1, y1), (x2, y2), (0, 0, 0), -1)
 
 
         #masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
         masked_img = torch.from_numpy(np.array(masked_img).astype(np.float32) / 255.0).unsqueeze(0)
-        ###汇总结果输出
+        ###summary the output
         all_text="|".join(result)
         x_offsets="|".join(x_offsets)
         y_offsets="|".join(y_offsets)
@@ -405,7 +402,7 @@ class ImageOCRByTextractV3:
 
         print("result",result)
 
-        # 添加原始图像输出
+        # Add original image output
         original_img = image_input
 
         return all_text ,x_offsets,y_offsets,widths,heights,img_width,img_height, masked_img,original_img

From e2faddced45bd3be0fa562ae62edee0220fca462 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Wed, 22 Jan 2025 18:17:08 +0800
Subject: [PATCH 29/30] update comments & readme

---
 nodes/textract.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index 68917c6..fb49d86 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -127,7 +127,7 @@ class ImageOCRByTextractV2:
         ori_image_path = folder_paths.get_annotated_filepath(image_input)
         pos_img_path = os.path.join(temp_img_path)
 
-        ## 转换ori_image为tensor张量
+        ## Convert orignal image to tensor
         pil_image = Image.open(ori_image_path)
         transform = transforms.Compose([
             transforms.ToTensor(),  # Convert PIL Image to tensor and normalize pixel values to [0, 1]

From 3230c5758f9c82e9be88ea767cbb8049452da187 Mon Sep 17 00:00:00 2001
From: tangqy <121102723@qq.com>
Date: Wed, 22 Jan 2025 18:18:19 +0800
Subject: [PATCH 30/30] update comments & readme

---
 nodes/textract.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nodes/textract.py b/nodes/textract.py
index fb49d86..ff5f9b1 100644
--- a/nodes/textract.py
+++ b/nodes/textract.py
@@ -45,7 +45,7 @@ class ImageOCRByTextract:
         numpy_image = (image_input[0] * 255.0).clamp(0, 255).numpy()
         image = image_input[0] * 255.0
         image = Image.fromarray(image.clamp(0, 255).numpy().round().astype(np.uint8))
-        # 获取图像原始尺寸
+        # Get the original dimensions of the image
         img_width, img_height = image.size
 
         ## Call Textract DetectDocumentText function