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prevent accidental creation of CLIP models in float32 type when user wants float16

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AUTOMATIC1111 2024-06-16 11:04:19 +03:00
parent 7ee2114cd9
commit b443fdcf76
2 changed files with 4 additions and 3 deletions
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6
modules/models/sd3/sd3_model.py
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@ -61,9 +61,9 @@ class SD3Cond(torch.nn.Module):
self.tokenizer = SD3Tokenizer() self.tokenizer = SD3Tokenizer()
with torch.no_grad(): with torch.no_grad():
self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=torch.float32) self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype)
self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=torch.float32, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG) self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=torch.float32) self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
self.weights_loaded = False self.weights_loaded = False

1
modules/sd_models.py
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@ -406,6 +406,7 @@ def set_model_fields(model):
if not hasattr(model, 'latent_channels'): if not hasattr(model, 'latent_channels'):
model.latent_channels = 4 model.latent_channels = 4
def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer): def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer):
sd_model_hash = checkpoint_info.calculate_shorthash() sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("calculate hash") timer.record("calculate hash")