prevent accidental creation of CLIP models in float32 type when user …

…wants float16
AUTOMATIC1111 · Jun 16, 2024 · b443fdc · b443fdc
1 parent 7ee2114
commit b443fdc
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Showing 2 changed files with 4 additions and 3 deletions.
diff --git a/modules/models/sd3/sd3_model.py b/modules/models/sd3/sd3_model.py
@@ -61,9 +61,9 @@ def __init__(self, *args, **kwargs):
         self.tokenizer = SD3Tokenizer()
 
         with torch.no_grad():
-            self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=torch.float32)
-            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.t5xxl = T5XXLModel(T5_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=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
+            self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
 
         self.weights_loaded = False
 

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