Merge remote-tracking branch 'origin/master' into dom-widgets

comfy/model_sampling.py

@@ -77,9 +77,9 @@ def sigma(self, timestep):
 
     def percent_to_sigma(self, percent):
         if percent <= 0.0:
-            return torch.tensor(999999999.9)
+            return 999999999.9
         if percent >= 1.0:
-            return torch.tensor(0.0)
+            return 0.0
         percent = 1.0 - percent
-        return self.sigma(torch.tensor(percent * 999.0))
+        return self.sigma(torch.tensor(percent * 999.0)).item()
 

comfy_extras/nodes_images.py

@@ -0,0 +1,44 @@
+import nodes
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+class ImageCrop:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "crop"
+
+    CATEGORY = "image/transform"
+
+    def crop(self, image, width, height, x, y):
+        x = min(x, image.shape[2] - 1)
+        y = min(y, image.shape[1] - 1)
+        to_x = width + x
+        to_y = height + y
+        img = image[:,y:to_y, x:to_x, :]
+        return (img,)
+
+class RepeatImageBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "amount": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "repeat"
+
+    CATEGORY = "image/batch"
+
+    def repeat(self, image, amount):
+        s = image.repeat((amount, 1,1,1))
+        return (s,)
+
+NODE_CLASS_MAPPINGS = {
+    "ImageCrop": ImageCrop,
+    "RepeatImageBatch": RepeatImageBatch,
+}

comfy_extras/nodes_model_advanced.py

@@ -67,11 +67,11 @@ def sigma(self, timestep):
 
     def percent_to_sigma(self, percent):
         if percent <= 0.0:
-            return torch.tensor(999999999.9)
+            return 999999999.9
         if percent >= 1.0:
-            return torch.tensor(0.0)
+            return 0.0
         percent = 1.0 - percent
-        return self.sigma(torch.tensor(percent * 999.0))
+        return self.sigma(torch.tensor(percent * 999.0)).item()
 
 
 def rescale_zero_terminal_snr_sigmas(sigmas):

comfy_extras/nodes_model_downscale.py

@@ -16,8 +16,8 @@ def INPUT_TYPES(s):
     CATEGORY = "_for_testing"
 
     def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip):
-        sigma_start = model.model.model_sampling.percent_to_sigma(start_percent).item()
-        sigma_end = model.model.model_sampling.percent_to_sigma(end_percent).item()
+        sigma_start = model.model.model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model.model.model_sampling.percent_to_sigma(end_percent)
 
         def input_block_patch(h, transformer_options):
             if transformer_options["block"][1] == block_number:

nodes.py

@@ -1800,6 +1800,7 @@ def init_custom_nodes():
         "nodes_hypertile.py",
         "nodes_model_advanced.py",
         "nodes_model_downscale.py",
+        "nodes_images.py",
     ]
 
     for node_file in extras_files: