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Merge branch 'comfyanonymous:master' into master

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patientx 2024-10-15 09:55:21 +03:00 committed by GitHub
commit f143a803d6
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2 changed files with 2 additions and 5 deletions
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5
comfy/k_diffusion/sampling.py
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@ -1080,7 +1080,6 @@ def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disabl
d = to_d(x, sigma_hat, temp[0])
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
dt = sigmas[i + 1] - sigma_hat
# Euler method
x = denoised + d * sigmas[i + 1]
return x
@ -1107,7 +1106,6 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d = to_d(x, sigmas[i], temp[0])
# Euler method
dt = sigma_down - sigmas[i]
x = denoised + d * sigma_down
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
@ -1138,7 +1136,6 @@ def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback
if sigma_down == 0:
# Euler method
d = to_d(x, sigmas[i], temp[0])
dt = sigma_down - sigmas[i]
x = denoised + d * sigma_down
else:
# DPM-Solver++(2S)
@ -1186,4 +1183,4 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
x = denoised + denoised_mix + torch.exp(-h) * x
old_uncond_denoised = uncond_denoised
return x
return x

2
comfy/sd1_clip.py
View File

@ -405,7 +405,7 @@ class SDTokenizer:
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None, tokenizer_data={}):
if tokenizer_path is None:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, clean_up_tokenization_spaces=True) # Fix Transformers FutureWarning by explicitly setting clean_up_tokenization_spaces to True
self.max_length = max_length
self.min_length = min_length