add clipseg node

essentials.py

@@ -415,7 +415,7 @@ class MaskBlur:
     CATEGORY = "essentials"
 
     def execute(self, mask, amount):
-        size = int(6 * amount +1)
+        size = int(6 * amount + 1)
         if size % 2 == 0:
             size+= 1
         
@@ -1905,6 +1905,81 @@ class ImageBatchMultiple:
         
         return (out,)
 
+class LoadCLIPSegModels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {},
+        }
+
+    RETURN_TYPES = ("CLIP_SEG",)
+    FUNCTION = "execute"
+    CATEGORY = "essentials"
+
+    def execute(self):
+        from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
+        processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
+        model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
+
+        return ((processor, model),)
+
+class ApplyCLIPSeg:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "clip_seg": ("CLIP_SEG",),
+                "prompt": ("STRING", { "multiline": False, "default": "" }),
+                "threshold": ("FLOAT", { "default": 0.4, "min": 0.0, "max": 1.0, "step": 0.05 }),
+                "smooth": ("INT", { "default": 9, "min": 0, "max": 32, "step": 1 }),
+                "dilate": ("INT", { "default": 0, "min": -32, "max": 32, "step": 1 }),
+                "blur": ("INT", { "default": 0, "min": 0, "max": 64, "step": 1 }),
+            },
+        }
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "execute"
+    CATEGORY = "essentials"
+
+    def execute(self, image, clip_seg, prompt, threshold, smooth, dilate, blur):
+        processor, model = clip_seg
+
+        imagenp = image.mul(255).clamp(0, 255).byte().cpu().numpy()
+
+        outputs = []
+        for i in imagenp:
+            inputs = processor(text=prompt, images=[i], return_tensors="pt")
+            out = model(**inputs)
+            out = out.logits.unsqueeze(1)
+            out = torch.sigmoid(out[0][0])
+            out = (out > threshold)
+            outputs.append(out)
+
+        del imagenp
+
+        outputs = torch.stack(outputs, dim=0)
+
+        if smooth > 0:
+            if smooth % 2 == 0:
+                smooth += 1
+            outputs = T.functional.gaussian_blur(outputs, smooth)
+
+        outputs = outputs.float()
+
+        if dilate != 0:
+            outputs = expand_mask(outputs, dilate, True)
+
+        if blur > 0:
+            if blur % 2 == 0:
+                blur += 1
+            outputs = T.functional.gaussian_blur(outputs, blur)
+        
+        # resize to original size
+        outputs = F.interpolate(outputs.unsqueeze(1), size=(image.shape[1], image.shape[2]), mode='bicubic').squeeze(1)
+
+        return (outputs,)
+
 NODE_CLASS_MAPPINGS = {
     "GetImageSize+": GetImageSize,
 
@@ -1959,6 +2034,9 @@ NODE_CLASS_MAPPINGS = {
     "ConditioningCombineMultiple+": ConditioningCombineMultiple,
     "ImageBatchMultiple+": ImageBatchMultiple,
 
+    "LoadCLIPSegModels+": LoadCLIPSegModels,
+    "ApplyCLIPSeg+": ApplyCLIPSeg,
+
     #"NoiseFromImage~": NoiseFromImage,
 }
 
@@ -2016,5 +2094,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ConditioningCombineMultiple+": "🔧 Conditionings Combine Multiple ",
     "ImageBatchMultiple+": "🔧 Images Batch Multiple",
 
+    "LoadCLIPSegModels+": "🔧 Load CLIPSeg Models",
+    "ApplyCLIPSeg+": "🔧 Apply CLIPSeg",
+
     #"NoiseFromImage~": "🔧 Noise From Image",
 }