Merge branch 'comfyanonymous:master' into master

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patientx 2024-09-08 22:20:50 +03:00 committed by GitHub
commit c4e18b7206
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3 changed files with 46 additions and 4 deletions

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@ -207,6 +207,7 @@ def model_lora_keys_clip(model, key_map={}):
text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}"
clip_l_present = False
clip_g_present = False
for b in range(32): #TODO: clean up
for c in LORA_CLIP_MAP:
k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
@ -230,6 +231,7 @@ def model_lora_keys_clip(model, key_map={}):
k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c)
if k in sdk:
clip_g_present = True
if clip_l_present:
lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base
key_map[lora_key] = k
@ -245,10 +247,18 @@ def model_lora_keys_clip(model, key_map={}):
for k in sdk:
if k.endswith(".weight"):
if k.startswith("t5xxl.transformer."):#OneTrainer SD3 lora
if k.startswith("t5xxl.transformer."):#OneTrainer SD3 and Flux lora
l_key = k[len("t5xxl.transformer."):-len(".weight")]
lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
key_map[lora_key] = k
t5_index = 1
if clip_g_present:
t5_index += 1
if clip_l_present:
t5_index += 1
if t5_index == 2:
key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k #OneTrainer Flux
t5_index += 1
key_map["lora_te{}_{}".format(t5_index, l_key.replace(".", "_"))] = k
elif k.startswith("hydit_clip.transformer.bert."): #HunyuanDiT Lora
l_key = k[len("hydit_clip.transformer.bert."):-len(".weight")]
lora_key = "lora_te1_{}".format(l_key.replace(".", "_"))

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@ -179,7 +179,13 @@ def merge_result_data(results, obj):
# merge node execution results
for i, is_list in zip(range(len(results[0])), output_is_list):
if is_list:
output.append([x for o in results for x in o[i]])
value = []
for o in results:
if isinstance(o[i], ExecutionBlocker):
value.append(o[i])
else:
value.extend(o[i])
output.append(value)
else:
output.append([o[i] for o in results])
return output

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@ -496,3 +496,29 @@ class TestExecution:
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"
# This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker
# as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node,
# only that one entry in the list is blocked.
def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder):
g = builder
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
image3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
image_list = g.node("TestMakeListNode", value1=image1.out(0), value2=image2.out(0), value3=image3.out(0))
int1 = g.node("StubInt", value=1)
int2 = g.node("StubInt", value=2)
int3 = g.node("StubInt", value=3)
int_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0), value3=int3.out(0))
compare = g.node("TestIntConditions", a=int_list.out(0), b=2, operation="==")
blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False)
list_output = g.node("TestMakeListNode", value1=blocker.out(0))
output = g.node("PreviewImage", images=list_output.out(0))
result = client.run(g)
assert result.did_run(output), "The execution should have run"
images = result.get_images(output)
assert len(images) == 2, "Should have 2 images"
assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black"
assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black"