1 files changed, 5 insertions, 5 deletions

diff --git a/infer.py b/infer.py
index 70851fd..5bd4abc 100644
--- a/infer.py
+++ b/infer.py
@@ -22,7 +22,7 @@ torch.backends.cuda.matmul.allow_tf32 = True
 default_args = {
     "model": None,
     "scheduler": "euler_a",
-    "precision": "fp16",
+    "precision": "fp32",
     "embeddings_dir": "embeddings",
     "output_dir": "output/inference",
     "config": None,
@@ -205,10 +205,10 @@ def load_embeddings(tokenizer, text_encoder, embeddings_dir):
 def create_pipeline(model, scheduler, embeddings_dir, dtype):
     print("Loading Stable Diffusion pipeline...")
 
-    tokenizer = CLIPTokenizer.from_pretrained(model + '/tokenizer', torch_dtype=dtype)
-    text_encoder = CLIPTextModel.from_pretrained(model + '/text_encoder', torch_dtype=dtype)
-    vae = AutoencoderKL.from_pretrained(model + '/vae', torch_dtype=dtype)
-    unet = UNet2DConditionModel.from_pretrained(model + '/unet', torch_dtype=dtype)
+    tokenizer = CLIPTokenizer.from_pretrained(model, subfolder='/tokenizer', torch_dtype=dtype)
+    text_encoder = CLIPTextModel.from_pretrained(model, subfolder='/text_encoder', torch_dtype=dtype)
+    vae = AutoencoderKL.from_pretrained(model, subfolder='/vae', torch_dtype=dtype)
+    unet = UNet2DConditionModel.from_pretrained(model, subfolder='/unet', torch_dtype=dtype)
 
     load_embeddings(tokenizer, text_encoder, embeddings_dir)