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https://github.com/comfyanonymous/ComfyUI.git
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Merge branch 'comfyanonymous:master' into master
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commit
c4e18b7206
@ -207,6 +207,7 @@ def model_lora_keys_clip(model, key_map={}):
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text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
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clip_l_present = False
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clip_g_present = False
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for b in range(32): #TODO: clean up
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for c in LORA_CLIP_MAP:
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k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
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@ -230,6 +231,7 @@ def model_lora_keys_clip(model, key_map={}):
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k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
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if k in sdk:
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clip_g_present = True
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if clip_l_present:
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lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
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key_map[lora_key] = k
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@ -245,10 +247,18 @@ def model_lora_keys_clip(model, key_map={}):
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for k in sdk:
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if k.endswith(".weight"):
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if k.startswith("t5xxl.transformer."):#OneTrainer SD3 lora
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if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora
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l_key = k[len("t5xxl.transformer."):-len(".weight")]
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lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
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key_map[lora_key] = k
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t5_index = 1
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if clip_g_present:
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t5_index += 1
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if clip_l_present:
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t5_index += 1
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if t5_index == 2:
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key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux
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t5_index += 1
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key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k
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elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora
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l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")]
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lora_key = "lora_te1_{}".format(l_key.replace(".", "_"))
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@ -179,7 +179,13 @@ def merge_result_data(results, obj):
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# merge node execution results
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for i, is_list in zip(range(len(results[0])), output_is_list):
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if is_list:
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output.append([x for o in results for x in o[i]])
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value = []
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for o in results:
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if isinstance(o[i], ExecutionBlocker):
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value.append(o[i])
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else:
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value.extend(o[i])
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output.append(value)
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else:
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output.append([o[i] for o in results])
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return output
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@ -496,3 +496,29 @@ class TestExecution:
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assert len(images) == 1, "Should have 1 image"
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assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
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assert not result.did_run(test_node), "The execution should have been cached"
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# This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker
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# as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node,
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# only that one entry in the list is blocked.
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def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
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image3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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image_list = g.node("TestMakeListNode", value1=image1.out(0), value2=image2.out(0), value3=image3.out(0))
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int1 = g.node("StubInt", value=1)
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int2 = g.node("StubInt", value=2)
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int3 = g.node("StubInt", value=3)
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int_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0), value3=int3.out(0))
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compare = g.node("TestIntConditions", a=int_list.out(0), b=2, operation="==")
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blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False)
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list_output = g.node("TestMakeListNode", value1=blocker.out(0))
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output = g.node("PreviewImage", images=list_output.out(0))
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result = client.run(g)
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assert result.did_run(output), "The execution should have run"
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images = result.get_images(output)
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assert len(images) == 2, "Should have 2 images"
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assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black"
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assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black"
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