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bug fixes

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This commit is contained in:
SayanoAI
2024-07-01 13:34:36 -04:00
parent b836b0125e
commit 285c6f4e57
2 changed files with 41 additions and 44 deletions
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custom_nodes/stt.py
+6 -17
View File
@@ -136,12 +136,6 @@ class AudioTranscriptionNode:
print(f"{audio_frames=}s")
return (transcription, audio_frames)
@classmethod
def IS_CHANGED(cls, *args, **kwargs):
print(f"{args=} {kwargs=}")
return get_hash(*args, *kwargs.items())
class BatchedTranscriptionEncoderNode:
def __init__(self):
@@ -189,7 +183,7 @@ class BatchedTranscriptionEncoderNode:
tokens = clip.tokenize(text)
cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
return text, duration, cond, pooled
return f'"{i*frame_interpolation}": "{text}"', duration, cond, pooled
@staticmethod
def get_spacy_model(language="en"):
@@ -260,12 +254,12 @@ class BatchedTranscriptionEncoderNode:
max_words=max_words)
batch_prompt_text.append(t)
batch_values_int.append(d)
cond.append(c)
pooled.append(pc)
cond.append(c.squeeze())
pooled.append(pc.squeeze())
num_chunks = len(total_chunks)
final_pooled_output = torch.cat(pooled, dim=0)
final_conditioning = torch.cat(cond, dim=0)
final_pooled_output = torch.nested.to_padded_tensor(torch.nested.nested_tensor(pooled, dtype=torch.float32),0)
final_conditioning = torch.nested.to_padded_tensor(torch.nested.nested_tensor(cond, dtype=torch.float32),0)
conditioning = [[final_conditioning,{"pooled_output": final_pooled_output}]]
del pooled, cond
@@ -274,9 +268,4 @@ class BatchedTranscriptionEncoderNode:
print(batch_values_int)
print(f"{num_chunks=}, {max_chunks=}, {num_frames=}")
return (conditioning, batch_prompt_text, batch_values_int, num_chunks, num_frames)
@classmethod
def IS_CHANGED(cls, *args, **kwargs):
print(f"{args=} {kwargs=}")
return get_hash(*args, *kwargs.items())
return (conditioning, batch_prompt_text, batch_values_int, num_chunks, num_frames)
custom_nodes/utils.py
+35 -27
View File
@@ -112,7 +112,6 @@ class MergeImageBatches:
return (images,)
class MergeLatentBatches:
@classmethod
def INPUT_TYPES(s):
@@ -142,6 +141,7 @@ class MergeLatentBatches:
gc_collect()
merged_latent["samples"] = self.merge_tensor([latent["samples"] for latent in latents])
merged_latent["noise_mask"] = self.merge_tensor([latent["noise_mask"] for latent in latents if "noise_mask" in latent])
# if len(merged_latent["noise_mask"])>0: merged_latent["noise_mask"] = torch.mean(merged_latent["noise_mask"],dim=0)
# merged_latent["batch_index"] = self.merge_tensor([latent["batch_index"] for latent in latents if "batch_index" in latent])
merged_latent["batch_index"] = range(len(merged_latent["samples"]))
@@ -195,7 +195,6 @@ class AudioBatchValueNode:
audio_zscore = (audio_rms-audio_rms.mean())/audio_rms.std()
output_range = output_max-output_min
print(f"{audio_rms.min()=} {audio_rms.max()=} {audio_rms.mean()=}")
if norm=="tanh":
x_norm = np.tanh(audio_zscore) #tanh=-1 to 1
if inverse: x_norm*=-1
@@ -209,10 +208,11 @@ class AudioBatchValueNode:
x_norm = (audio_zscore - x_min) / (audio_zscore.max() - x_min) #scale=0 to 1
if inverse: x_norm=1-x_norm
x_norm = x_norm * output_range + output_min
print(f"{x_norm.min()=} {x_norm.max()=} {x_norm.mean()=}")
batch_value = ",\n".join([f'{n}:({v})' for n,v in enumerate(x_norm)])
if print_output: pprint(batch_value)
# batch_value = ",\n".join([f'{n}:({v})' for n,v in enumerate(x_norm)])
if print_output:
print(f"{audio_rms.min()=} {audio_rms.max()=} {audio_rms.mean()=} {len(audio_rms)=}")
print(f"{x_norm.min()=} {x_norm.max()=} {x_norm.mean()=} {len(x_norm)=}")
return (list(x_norm),list(map(int,x_norm)),num_values)
class ImageRepeatInterleavedNode:
@@ -236,24 +236,25 @@ class ImageRepeatInterleavedNode:
CATEGORY = CATEGORY
def rebatch(self, images, repeats, fps=1):
repeats = np.array(repeats).flatten()*fps
print(f"{repeats=}")
repeats = np.array(repeats).flatten()
fps = np.array(fps)[0]
print(f"{repeats=} {fps=}")
all_images = []
for img in images:
for i in range(img.shape[0]):
all_images.append(img[i:i+1])
# expand repeat
if len(repeats)==1:
repeats = repeats.repeat(len(all_images),axis=0)
elif len(repeats)<len(all_images):
all_images*=repeats[0]
repeats = np.ones(len(all_images))
elif len(repeats)<len(all_images): # expand repeat
pad_width = len(all_images) - len(repeats)
repeats = np.pad(repeats, (0, pad_width), mode='constant', constant_values=1)
for i,img in enumerate(all_images):
if repeats[i]>1: all_images[i] = img.expand(repeats[i], *img.shape).flatten(0,1)
repeat = int(repeats[i])*fps
if repeat>1: all_images[i] = img.expand(repeat, *img.shape).flatten(0,1)
else: all_images[i] = img
print(f"{img.shape=} {all_images[i].shape=}")
print(f"{len(images)=} {len(all_images)=}")
gc_collect()
@@ -296,21 +297,22 @@ class LatentRepeatInterleavedNode:
offset = 0
num_latents = 0
for i in range(len(samples)):
samples[i]["batch_index"] = [i+offset for i in range(repeats[i])]
repeat = int(repeats[i])
samples[i]["batch_index"] = [i+offset for i in range(repeat)]
offset += repeats[i]
samples[i]["samples"] = LatentRepeatInterleavedNode.repeat(samples[i]["samples"],repeats[i])
samples[i]["samples"] = LatentRepeatInterleavedNode.repeat(samples[i]["samples"],repeat)
num_latents+=len(samples[i]["samples"])
if "noise_mask" in samples[i]:
samples[i]["noise_mask"] = LatentRepeatInterleavedNode.repeat(samples[i]["noise_mask"],repeats[i])
samples[i]["noise_mask"] = LatentRepeatInterleavedNode.repeat(samples[i]["noise_mask"],repeat)
return samples,num_latents
def merge(self, latents, repeats, fps=1):
repeats = np.array(repeats).flatten()*fps
print(f"{repeats=}")
repeats = np.array(repeats).flatten()
fps = np.array(fps)[0]
print(f"{repeats=} {fps=}")
# combine all latents
new_latents = []
@@ -322,19 +324,21 @@ class LatentRepeatInterleavedNode:
new_latent = {
"samples": latent["samples"][i],
}
if "noise_mask" in latent: new_latent["noise_mask"] = latent["noise_mask"][i]
if "noise_mask" in latent:
print(f"{latent['noise_mask'].shape=}")
new_latent["noise_mask"] = latent["noise_mask"]
new_latents.append(new_latent)
# expand repeat
if len(repeats)==1:
repeats = repeats.repeat(len(new_latents),axis=0)
elif len(repeats)<len(new_latents):
new_latents *= repeats[0]
repeats = np.ones(len(new_latents))
elif len(repeats)<len(new_latents): # expand repeat
pad_width = len(new_latents) - len(repeats)
repeats = np.pad(repeats, (0, pad_width), mode='constant', constant_values=1)
new_latents,num_latents = self.rebatch(new_latents,repeats.astype(int))
new_latents,num_latents = self.rebatch(new_latents,repeats.astype(np.int16)*fps)
gc_collect()
print(f"{num_latents=}")
print(f"{len(latents)=} {num_latents=}")
return (new_latents,num_latents)
@@ -404,11 +408,10 @@ class SimpleMathNode:
"round_up": ("BOOLEAN", {"default": False})
},
"required": {
"operation": (["","ADD","SUBTRACT","MULTIPLY","DIVIDE","MODULUS"], { "multiline": False, "default": "" }),
"operation": (["CONVERT","ADD","SUBTRACT","MULTIPLY","DIVIDE","MODULUS","MIN","MAX"], { "default": "CONVERT" }),
},
}
INPUT_IS_LIST = True
RETURN_TYPES = ("INT", "FLOAT", )
FUNCTION = "do_math"
CATEGORY = CATEGORY
@@ -421,8 +424,13 @@ class SimpleMathNode:
elif operation=="DIVIDE":
assert not any(b==0), f"cannot divide by 0 ({b=})!"
number=a/b
elif operation=="MODULUS": number=a-b
elif operation=="MODULUS": number=a%b
elif operation=="MIN": number=np.array(list(map(min,zip(a,b))))
elif operation=="MAX": number=np.array(list(map(max,zip(a,b))))
else: number=np.array(n1 or n2).flatten()
print(f"{a=} \n{operation=} \n{b=} \n{number=}")
num_to_int = math.ceil if round_up else math.floor
if len(number)>1: # handles list inputs
return (list(map(num_to_int,number)), list(map(float,number)),)