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repair --medvram for SD2.x too after SDXL update

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AUTOMATIC1111 2023-07-24 11:57:59 +03:00
parent 1f26815dd3
commit 2c11e9009e
2 changed files with 5 additions and 4 deletions
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7
modules/lowvram.py
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@ -90,8 +90,12 @@ def setup_for_low_vram(sd_model, use_medvram):
sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu) sd_model.conditioner.register_forward_pre_hook(send_me_to_gpu)
elif is_sd2: elif is_sd2:
sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu) sd_model.cond_stage_model.model.register_forward_pre_hook(send_me_to_gpu)
sd_model.cond_stage_model.model.token_embedding.register_forward_pre_hook(send_me_to_gpu)
parents[sd_model.cond_stage_model.model] = sd_model.cond_stage_model
parents[sd_model.cond_stage_model.model.token_embedding] = sd_model.cond_stage_model
else: else:
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu) sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu) sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.encode = first_stage_model_encode_wrap sd_model.first_stage_model.encode = first_stage_model_encode_wrap
@ -101,9 +105,6 @@ def setup_for_low_vram(sd_model, use_medvram):
if sd_model.embedder: if sd_model.embedder:
sd_model.embedder.register_forward_pre_hook(send_me_to_gpu) sd_model.embedder.register_forward_pre_hook(send_me_to_gpu)
if hasattr(sd_model, 'cond_stage_model'):
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if use_medvram: if use_medvram:
sd_model.model.register_forward_pre_hook(send_me_to_gpu) sd_model.model.register_forward_pre_hook(send_me_to_gpu)
else: else:

2
modules/sd_hijack_open_clip.py
View File

@ -32,7 +32,7 @@ class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWit
def encode_embedding_init_text(self, init_text, nvpt): def encode_embedding_init_text(self, init_text, nvpt):
ids = tokenizer.encode(init_text) ids = tokenizer.encode(init_text)
ids = torch.asarray([ids], device=devices.device, dtype=torch.int) ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0) embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
return embedded return embedded