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fix pad token is not handled

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Kohya S
2023-07-09 18:13:49 +09:00
parent 0416f26a76
commit a380502c01
1 changed files with 5 additions and 3 deletions
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8
library/sdxl_lpw_stable_diffusion.py
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@@ -185,14 +185,14 @@ def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], m
return tokens, weights return tokens, weights
def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77): def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77):
r""" r"""
Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length.
""" """
max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) max_embeddings_multiples = (max_length - 2) // (chunk_length - 2)
weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length
for i in range(len(tokens)): for i in range(len(tokens)):
tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i])) tokens[i] = [bos] + tokens[i] + [eos] + [pad] * (max_length - 2 - len(tokens[i]))
if no_boseos_middle: if no_boseos_middle:
weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i]))
else: else:
@@ -363,6 +363,7 @@ def get_weighted_text_embeddings(
max_length, max_length,
bos, bos,
eos, eos,
pad,
no_boseos_middle=no_boseos_middle, no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length, chunk_length=pipe.tokenizer.model_max_length,
) )
@@ -374,6 +375,7 @@ def get_weighted_text_embeddings(
max_length, max_length,
bos, bos,
eos, eos,
pad,
no_boseos_middle=no_boseos_middle, no_boseos_middle=no_boseos_middle,
chunk_length=pipe.tokenizer.model_max_length, chunk_length=pipe.tokenizer.model_max_length,
) )
@@ -711,7 +713,7 @@ class SdxlStableDiffusionLongPromptWeightingPipeline:
# self.vae.set_use_memory_efficient_attention_xformers(False) # self.vae.set_use_memory_efficient_attention_xformers(False)
# image = self.vae.decode(latents.to("cpu")).sample # image = self.vae.decode(latents.to("cpu")).sample
image = self.vae.decode(latents).sample image = self.vae.decode(latents.to(self.vae.dtype)).sample
image = (image / 2 + 0.5).clamp(0, 1) image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3, 1).float().numpy() image = image.cpu().permute(0, 2, 3, 1).float().numpy()