SAM2Ultra and ObjectDetector nodes support image batch
This commit is contained in:
chflame163
@@ -150,6 +150,7 @@ Please try downgrading the ```protobuf``` dependency package to 3.20.3, or set e
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<font size="4">**If the dependency package error after updating, please double clicking ```repair_dependency.bat``` (for Official ComfyUI Protable) or ```repair_dependency_aki.bat``` (for ComfyUI-aki-v1.x) in the plugin folder to reinstall the dependency packages. </font><br />
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* [SAM2Ultra](#SAM2Ultra) and ObjectDetector nodes support image batch.
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* [SAM2Ultra](#SAM2Ultra) and [SAM2VideoUltra](#SAM2VideoUltra) nodes add support for SAM2.1 model, including [kijai](https://github.com/kijai)'s FP16 model. Download model files from [BaiduNetdisk](https://pan.baidu.com/s/1xaQYBA6ktxvAxm310HXweQ?pwd=auki) or [huggingface.co/Kijai/sam2-safetensors](https://huggingface.co/Kijai/sam2-safetensors/tree/main) and copy to ```ComfyUI/models/sam2``` folder.
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* Commit [JoyCaption2Split](#JoyCaption2Split) and [LoadJoyCaption2Model](#LoadJoyCaption2Model) nodes, Sharing the model across multiple JoyCaption2 nodes improves efficiency.
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* [SegmentAnythingUltra](#SegmentAnythingUltra) and [SegmentAnythingUltraV2](#SegmentAnythingUltraV2) add the ```cache_model``` option, Easy to flexibly manage VRAM usage.
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@@ -127,6 +127,7 @@ If this call came from a _pb2.py file, your generated code is out of date and mu
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## 更新说明
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<font size="4">**如果本插件更新后出现依赖包错误，请双击运行插件目录下的```install_requirements.bat```(官方便携包)，或 ```install_requirements_aki.bat```(秋叶整合包) 重新安装依赖包。
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* [SAM2Ultra](#SAM2Ultra) 及 ObjectDetector 节点支持图像批次。
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* [SAM2Ultra](#SAM2Ultra) 及 [SAM2VideoUltra](#SAM2VideoUltra) 节点增加支持SAM2.1模型，包括[kijai](https://github.com/kijai)量化版fp16模型。请从请从[百度网盘](https://pan.baidu.com/s/1xaQYBA6ktxvAxm310HXweQ?pwd=auki) 或者 [huggingface.co/Kijai/sam2-safetensors](https://huggingface.co/Kijai/sam2-safetensors/tree/main)下载模型文件并复制到```ComfyUI/models/sam2```文件夹。
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* 添加 [JoyCaption2Split](#JoyCaption2Split) 和 [LoadJoyCaption2Model](#LoadJoyCaption2Model) 节点，在多个JoyCaption2节点时共用模型提高效率。
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* [SegmentAnythingUltra](#SegmentAnythingUltra) 和 [SegmentAnythingUltraV2](#SegmentAnythingUltraV2) 增加 ```cache_model``` 参数，便于灵活管理显存。
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+100
-79
@@ -105,6 +105,7 @@ class LS_OBJECT_DETECTOR_FL2:
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def object_detector_fl2(self, image, prompt, florence2_model, sort_method, bbox_select, select_index):
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ret_bboxes = []
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bboxes = []
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ret_previews = []
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max_new_tokens = 512
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@@ -115,27 +116,30 @@ class LS_OBJECT_DETECTOR_FL2:
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model = florence2_model['model']
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processor = florence2_model['processor']
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img = tensor2pil(image[0]).convert("RGB")
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task = 'caption to phrase grounding'
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from .florence2_ultra import process_image
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results, _ = process_image(model, processor, img, task,
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max_new_tokens, num_beams, do_sample,
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fill_mask, prompt)
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for img in image:
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img = tensor2pil(img.unsqueeze(0)).convert("RGB")
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task = 'caption to phrase grounding'
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from .florence2_ultra import process_image
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results, _ = process_image(model, processor, img, task,
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max_new_tokens, num_beams, do_sample,
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fill_mask, prompt)
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if isinstance(results, dict):
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results["width"] = img.width
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results["height"] = img.height
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if isinstance(results, dict):
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results["width"] = img.width
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results["height"] = img.height
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bboxes = self.fbboxes_to_list(results)
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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preview = draw_bounding_boxes(img, bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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return (standardize_bbox(bboxes), torch.cat(ret_previews, dim=0))
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bboxes = self.fbboxes_to_list(results)
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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preview = draw_bounding_boxes(img, bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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ret_bboxes.append(standardize_bbox(bboxes))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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return (ret_bboxes, torch.cat(ret_previews, dim=0))
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def fbboxes_to_list(self, F_BBOXES) -> list:
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if isinstance(F_BBOXES, str):
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@@ -210,31 +214,34 @@ class LS_OBJECT_DETECTOR_MASK:
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def object_detector_mask(self, object_mask, sort_method, bbox_select, select_index):
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ret_bboxes = []
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ret_previews = []
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bboxes = []
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if object_mask.dim() == 2:
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object_mask = torch.unsqueeze(object_mask, 0)
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cv_mask = tensor2cv2(object_mask[0])
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cv_mask = cv2.cvtColor(cv_mask, cv2.COLOR_BGR2GRAY)
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_, binary = cv2.threshold(cv_mask, 127, 255, cv2.THRESH_BINARY)
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# invert mask
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# binary = cv2.bitwise_not(binary)
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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bboxes.append([x, y, x + w, y + h])
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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ret_previews = []
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preview = draw_bounding_boxes(tensor2pil(object_mask[0]).convert("RGB"), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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for msk in object_mask:
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cv_mask = tensor2cv2(msk)
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cv_mask = cv2.cvtColor(cv_mask, cv2.COLOR_BGR2GRAY)
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_, binary = cv2.threshold(cv_mask, 127, 255, cv2.THRESH_BINARY)
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# invert mask
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# binary = cv2.bitwise_not(binary)
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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bboxes.append([x, y, x + w, y + h])
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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preview = draw_bounding_boxes(tensor2pil(msk).convert("RGB"), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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ret_bboxes.append(standardize_bbox(bboxes))
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return (standardize_bbox(bboxes), torch.cat(ret_previews, dim=0))
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return (ret_bboxes, torch.cat(ret_previews, dim=0))
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class LS_OBJECT_DETECTOR_YOLO8:
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@@ -271,31 +278,34 @@ class LS_OBJECT_DETECTOR_YOLO8:
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model_path = os.path.join(folder_paths.models_dir, 'yolo')
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yolo_model = YOLO(os.path.join(model_path, yolo_model))
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ret_bboxes = []
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bboxes = []
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ret_previews = []
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img = torch.unsqueeze(image[0], 0)
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_image = tensor2pil(img)
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results = yolo_model(_image, retina_masks=True)
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for result in results:
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yolo_plot_image = cv2.cvtColor(result.plot(), cv2.COLOR_BGR2RGB)
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for img in image:
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img = torch.unsqueeze(img.unsqueeze(0), 0)
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_image = tensor2pil(img)
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results = yolo_model(_image, retina_masks=True)
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for result in results:
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yolo_plot_image = cv2.cvtColor(result.plot(), cv2.COLOR_BGR2RGB)
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# no mask, if have box, draw box
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if result.boxes is not None and len(result.boxes.xyxy) > 0:
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for box in result.boxes:
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
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bboxes.append([x1, y1, x2, y2])
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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preview = draw_bounding_boxes(_image.convert("RGB"), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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# no mask, if have box, draw box
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if result.boxes is not None and len(result.boxes.xyxy) > 0:
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for box in result.boxes:
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x1, y1, x2, y2 = box.xyxy[0].cpu().numpy()
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bboxes.append([x1, y1, x2, y2])
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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preview = draw_bounding_boxes(_image.convert("RGB"), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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ret_bboxes.append(standardize_bbox(bboxes))
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return (standardize_bbox(bboxes), torch.cat(ret_previews, dim=0),)
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return (ret_bboxes, torch.cat(ret_previews, dim=0),)
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class LS_OBJECT_DETECTOR_YOLOWORLD:
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@@ -332,33 +342,44 @@ class LS_OBJECT_DETECTOR_YOLOWORLD:
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def object_detector_yoloworld(self, image, yolo_world_model,
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confidence_threshold, nms_iou_threshold, prompt,
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sort_method, bbox_select, select_index):
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ret_previews = []
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ret_bboxes = []
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import supervision as sv
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model=self.load_yolo_world_model(yolo_world_model, prompt)
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infer_outputs = []
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img = (255 * image[0].cpu().numpy()).astype(np.uint8)
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results = model.infer(
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img, confidence=confidence_threshold)
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detections = sv.Detections.from_inference(results)
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detections = detections.with_nms(
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class_agnostic=False,
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threshold=nms_iou_threshold
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)
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infer_outputs.append(detections)
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bboxes = infer_outputs[0].xyxy.tolist()
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bboxes = [[int(value) for value in sublist] for sublist in bboxes]
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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ret_previews = []
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preview = draw_bounding_boxes(tensor2pil(image[0]).convert('RGB'), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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for i in image:
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infer_outputs = []
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# img = (255 * img.unsqueeze(0).cpu().numpy()).astype(np.uint8)
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img = tensor2np(i)
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results = model.infer(
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img, confidence=confidence_threshold)
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detections = sv.Detections.from_inference(results)
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detections = detections.with_nms(
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class_agnostic=False,
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threshold=nms_iou_threshold
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)
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infer_outputs.append(detections)
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return (standardize_bbox(bboxes), torch.cat(ret_previews, dim=0))
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if len(infer_outputs[0].xyxy) > 0:
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bboxes = infer_outputs[0].xyxy.tolist()
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bboxes = [[int(value) for value in sublist] for sublist in bboxes]
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bboxes = sort_bboxes(bboxes, sort_method)
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bboxes = select_bboxes(bboxes, bbox_select, select_index)
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else:
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bboxes = [[0, 0, i.shape[1], i.shape[0]]]
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preview = draw_bounding_boxes(tensor2pil(i.unsqueeze(0)).convert('RGB'), bboxes, color="random", line_width=-1)
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ret_previews.append(pil2tensor(preview))
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} no object found", message_type='warning')
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else:
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log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info')
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ret_bboxes.append(standardize_bbox(bboxes))
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return (ret_bboxes, torch.cat(ret_previews, dim=0))
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def process_categories(self, categories: str) -> List[str]:
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return [category.strip().lower() for category in categories.split(',')]
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+98
-98
@@ -285,9 +285,9 @@ class LS_SAM2_ULTRA:
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log(f"{self.NODE_NAME}: Downloading SAM2 model to: {model_path}")
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="Kijai/sam2-safetensors",
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allow_patterns=[f"*{sam2_model}*"],
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local_dir=sam2_path,
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local_dir_use_symlinks=False)
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allow_patterns=[f"*{sam2_model}*"],
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local_dir=sam2_path,
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local_dir_use_symlinks=False)
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model_mapping = {
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"2.0": {
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@@ -311,81 +311,115 @@ class LS_SAM2_ULTRA:
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None
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)
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log(f"{self.NODE_NAME}: Using model config: {model_cfg_path}")
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model = load_model(model_path, model_cfg_path, segmentor, dtype, device)
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offload_device = mm.unet_offload_device()
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# B, H, W, C = image.shape
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indexs = extract_numbers(select_index)
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# Handle possible bboxes
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} skipped, because bboxes is empty.", message_type='error')
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return (image, None)
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else:
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boxes_np_batch = []
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for bbox_list in bboxes:
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boxes_np = []
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for bbox in bbox_list:
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boxes_np.append(bbox)
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boxes_np = np.array(boxes_np)
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boxes_np_batch.append(boxes_np)
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if bbox_select == "all":
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final_box = np.array(boxes_np_batch)
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elif bbox_select == "by_index":
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final_box = []
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try:
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for i in indexs:
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final_box.append(boxes_np_batch[i])
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except IndexError:
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log(f"{self.NODE_NAME} invalid bbox index {i}", message_type='warning')
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else:
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final_box = np.array(boxes_np_batch[0])
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# final_labels = None
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mask_list = []
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try:
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model.to(device)
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except:
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model.model.to(device)
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autocast_condition = not mm.is_device_mps(device)
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with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
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image_np = (image.contiguous() * 255).byte().numpy()
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comfy_pbar = ProgressBar(len(image_np))
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tqdm_pbar = tqdm(total=len(image_np), desc="Processing Images")
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for i in range(len(image_np)):
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model.set_image(image_np[i])
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# if len(image_np) > 1:
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# input_box = final_box[i]
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input_box = final_box
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for index in range(len(image)):
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img = image[index].unsqueeze(0)
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out_masks, scores, logits = model.predict(
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point_coords=None,
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point_labels=None,
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box=input_box,
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multimask_output=True,
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mask_input=None,
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)
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if out_masks.ndim == 3:
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sorted_ind = np.argsort(scores)[::-1]
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out_masks = out_masks[sorted_ind][0] # choose only the best result for now
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# scores = scores[sorted_ind]
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# logits = logits[sorted_ind]
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mask_list.append(np.expand_dims(out_masks, axis=0))
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# Handle possible bboxes
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if len(bboxes) == 0:
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log(f"{self.NODE_NAME} skipped, because bboxes is empty.", message_type='error')
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return (image, None)
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else:
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boxes_np_batch = []
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for bbox_list in bboxes[index]:
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boxes_np = []
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for bbox in bbox_list:
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boxes_np.append(bbox)
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boxes_np = np.array(boxes_np)
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boxes_np_batch.append(boxes_np)
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if bbox_select == "all":
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final_box = np.array(boxes_np_batch)
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elif bbox_select == "by_index":
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final_box = []
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try:
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for i in indexs:
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final_box.append(boxes_np_batch[i])
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except IndexError:
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log(f"{self.NODE_NAME} invalid bbox index {i}", message_type='warning')
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else:
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_, _, H, W = out_masks.shape
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# Combine masks for all object IDs in the frame
|
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combined_mask = np.zeros((H, W), dtype=bool)
|
||||
for out_mask in out_masks:
|
||||
combined_mask = np.logical_or(combined_mask, out_mask)
|
||||
combined_mask = combined_mask.astype(np.uint8)
|
||||
mask_list.append(combined_mask)
|
||||
comfy_pbar.update(1)
|
||||
tqdm_pbar.update(1)
|
||||
final_box = np.array(boxes_np_batch[0])
|
||||
|
||||
mask_list = []
|
||||
|
||||
with torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocast_condition else nullcontext():
|
||||
|
||||
image_np = (img.contiguous() * 255).byte().numpy()
|
||||
comfy_pbar = ProgressBar(len(image_np))
|
||||
tqdm_pbar = tqdm(total=len(image_np), desc="Processing Images")
|
||||
for i in range(len(image_np)):
|
||||
model.set_image(image_np[i])
|
||||
# if len(image_np) > 1:
|
||||
# input_box = final_box[i]
|
||||
input_box = final_box
|
||||
|
||||
out_masks, scores, logits = model.predict(
|
||||
point_coords=None,
|
||||
point_labels=None,
|
||||
box=input_box,
|
||||
multimask_output=True,
|
||||
mask_input=None,
|
||||
)
|
||||
|
||||
if out_masks.ndim == 3:
|
||||
sorted_ind = np.argsort(scores)[::-1]
|
||||
out_masks = out_masks[sorted_ind][0] # choose only the best result for now
|
||||
# scores = scores[sorted_ind]
|
||||
# logits = logits[sorted_ind]
|
||||
mask_list.append(np.expand_dims(out_masks, axis=0))
|
||||
else:
|
||||
_, _, H, W = out_masks.shape
|
||||
# Combine masks for all object IDs in the frame
|
||||
combined_mask = np.zeros((H, W), dtype=bool)
|
||||
for out_mask in out_masks:
|
||||
combined_mask = np.logical_or(combined_mask, out_mask)
|
||||
combined_mask = combined_mask.astype(np.uint8)
|
||||
mask_list.append(combined_mask)
|
||||
comfy_pbar.update(1)
|
||||
tqdm_pbar.update(1)
|
||||
|
||||
out_list = []
|
||||
for mask in mask_list:
|
||||
mask_tensor = torch.from_numpy(mask)
|
||||
mask_tensor = mask_tensor.permute(1, 2, 0)
|
||||
mask_tensor = mask_tensor[:, :, 0]
|
||||
out_list.append(mask_tensor)
|
||||
mask_tensor = torch.stack(out_list, dim=0).cpu().float()
|
||||
_mask = mask_tensor.squeeze()
|
||||
|
||||
if detail_method == 'VITMatte(local)':
|
||||
local_files_only = True
|
||||
else:
|
||||
local_files_only = False
|
||||
orig_image = tensor2pil(img)
|
||||
detail_range = detail_erode + detail_dilate
|
||||
if process_detail:
|
||||
if detail_method == 'GuidedFilter':
|
||||
_mask = guided_filter_alpha(pil2tensor(orig_image), _mask, detail_range // 6 + 1)
|
||||
_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
|
||||
elif detail_method == 'PyMatting':
|
||||
_mask = tensor2pil(mask_edge_detail(pil2tensor(orig_image), _mask, detail_range // 8 + 1, black_point, white_point))
|
||||
else:
|
||||
_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
|
||||
_mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device,
|
||||
max_megapixels=max_megapixels)
|
||||
_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
|
||||
else:
|
||||
_mask = tensor2pil(_mask)
|
||||
|
||||
ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
|
||||
ret_images.append(pil2tensor(ret_image))
|
||||
ret_masks.append(image2mask(_mask))
|
||||
|
||||
if cache_model:
|
||||
try:
|
||||
@@ -398,40 +432,6 @@ class LS_SAM2_ULTRA:
|
||||
else:
|
||||
del model
|
||||
clear_memory()
|
||||
|
||||
out_list = []
|
||||
for mask in mask_list:
|
||||
mask_tensor = torch.from_numpy(mask)
|
||||
mask_tensor = mask_tensor.permute(1, 2, 0)
|
||||
mask_tensor = mask_tensor[:, :, 0]
|
||||
out_list.append(mask_tensor)
|
||||
mask_tensor = torch.stack(out_list, dim=0).cpu().float()
|
||||
_mask = mask_tensor.squeeze()
|
||||
|
||||
if detail_method == 'VITMatte(local)':
|
||||
local_files_only = True
|
||||
else:
|
||||
local_files_only = False
|
||||
orig_image = tensor2pil(image[0])
|
||||
detail_range = detail_erode + detail_dilate
|
||||
if process_detail:
|
||||
if detail_method == 'GuidedFilter':
|
||||
_mask = guided_filter_alpha(pil2tensor(orig_image), _mask, detail_range // 6 + 1)
|
||||
_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
|
||||
elif detail_method == 'PyMatting':
|
||||
_mask = tensor2pil(mask_edge_detail(pil2tensor(orig_image), _mask, detail_range // 8 + 1, black_point, white_point))
|
||||
else:
|
||||
_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
|
||||
_mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device,
|
||||
max_megapixels=max_megapixels)
|
||||
_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
|
||||
else:
|
||||
_mask = tensor2pil(_mask)
|
||||
|
||||
ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
|
||||
ret_images.append(pil2tensor(ret_image))
|
||||
ret_masks.append(image2mask(_mask))
|
||||
|
||||
log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
|
||||
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0))
|
||||
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "comfyui_layerstyle"
|
||||
description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress."
|
||||
version = "1.0.84"
|
||||
version = "1.0.85"
|
||||
license = "MIT"
|
||||
dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "scikit_learn", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "transparent-background", "huggingface_hub", "accelerate", "bitsandbytes", "torchscale", "wandb", "hydra-core", "psd-tools", "inference-cli[yolo-world]", "inference-gpu[yolo-world]", "onnxruntime", "peft", "iopath"]
|
||||
|
||||
|
||||
Reference in New Issue
Block a user