Merge branch 'comfyanonymous:master' into master

Author: kinqsradio

.ci/windows_nightly_base_files/run_nvidia_gpu_fast.bat

@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast
+pause

.github/workflows/windows_release_nightly_pytorch.yml

@@ -67,6 +67,7 @@ jobs:
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
             cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
 
             echo "call update_comfyui.bat nopause
             ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2

comfy/model_management.py

@@ -987,16 +987,13 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if props.major < 6:
         return False
 
-    fp16_works = False
-    #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled
-    #when the model doesn't actually fit on the card
-    #TODO: actually test if GP106 and others have the same type of behavior
+    #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32
     nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
     for x in nvidia_10_series:
         if x in props.name.lower():
-            fp16_works = True
+            return True
 
-    if fp16_works or manual_cast:
+    if manual_cast:
         free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True

execution.py

@@ -47,7 +47,8 @@ def get(self, node_id):
             self.is_changed[node_id] = node["is_changed"]
             return self.is_changed[node_id]
 
-        input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, self.outputs_cache)
+        # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
+        input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None)
         try:
             is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED")
             node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]

tests/inference/test_execution.py

@@ -459,3 +459,22 @@ def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder):
         assert len(images1) == 1, "Should have 1 image"
         assert len(images2) == 1, "Should have 1 image"
 
+
+    # This tests that only constant outputs are used in the call to `IS_CHANGED`
+    def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1)
+        test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5)
+
+        output = g.node("PreviewImage", images=test_node.out(0))
+
+        result = client.run(g)
+        images = result.get_images(output)
+        assert len(images) == 1, "Should have 1 image"
+        assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
+
+        result = client.run(g)
+        images = result.get_images(output)
+        assert len(images) == 1, "Should have 1 image"
+        assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
+        assert not result.did_run(test_node), "The execution should have been cached"

tests/inference/testing_nodes/testing-pack/specific_tests.py

@@ -95,6 +95,31 @@ def IS_CHANGED(cls, should_change=False, *args, **kwargs):
         else:
             return False
 
+class TestIsChangedWithConstants:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_is_changed"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_is_changed(self, image, value):
+        return (image * value,)
+    
+    @classmethod
+    def IS_CHANGED(cls, image, value):
+        if image is None:
+            return value
+        else:
+            return image.mean().item() * value
+
 class TestCustomValidation1:
     @classmethod
     def INPUT_TYPES(cls):
@@ -312,6 +337,7 @@ def mixed_expansion_returns(self, input1):
     "TestLazyMixImages": TestLazyMixImages,
     "TestVariadicAverage": TestVariadicAverage,
     "TestCustomIsChanged": TestCustomIsChanged,
+    "TestIsChangedWithConstants": TestIsChangedWithConstants,
     "TestCustomValidation1": TestCustomValidation1,
     "TestCustomValidation2": TestCustomValidation2,
     "TestCustomValidation3": TestCustomValidation3,
@@ -325,6 +351,7 @@ def mixed_expansion_returns(self, input1):
     "TestLazyMixImages": "Lazy Mix Images",
     "TestVariadicAverage": "Variadic Average",
     "TestCustomIsChanged": "Custom IsChanged",
+    "TestIsChangedWithConstants": "IsChanged With Constants",
     "TestCustomValidation1": "Custom Validation 1",
     "TestCustomValidation2": "Custom Validation 2",
     "TestCustomValidation3": "Custom Validation 3",

tests/inference/testing_nodes/testing-pack/stubs.py

@@ -28,6 +28,28 @@ def stub_image(self, content, height, width, batch_size):
         elif content == "NOISE":
             return (torch.rand(batch_size, height, width, 3),)
 
+class StubConstantImage:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}),
+                "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stub_constant_image"
+
+    CATEGORY = "Testing/Stub Nodes"
+
+    def stub_constant_image(self, value, height, width, batch_size):
+        return (torch.ones(batch_size, height, width, 3) * value,)
+
 class StubMask:
     def __init__(self):
         pass
@@ -93,12 +115,14 @@ def stub_float(self, value):
 
 TEST_STUB_NODE_CLASS_MAPPINGS = {
     "StubImage": StubImage,
+    "StubConstantImage": StubConstantImage,
     "StubMask": StubMask,
     "StubInt": StubInt,
     "StubFloat": StubFloat,
 }
 TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS = {
     "StubImage": "Stub Image",
+    "StubConstantImage": "Stub Constant Image",
     "StubMask": "Stub Mask",
     "StubInt": "Stub Int",
     "StubFloat": "Stub Float",