Update app.py

app.py

@@ -17,14 +17,17 @@ import spaces
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
+
+dtype = torch.bfloat16
 model_path = "MeissonFlow/Meissonic"
-model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer")
-vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae")
-# text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder")
-text_encoder = CLIPTextModelWithProjection.from_pretrained(  #more stable sampling for some cases
+model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
+vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype)
+# text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder", torch_dtype=dtype)
+text_encoder = CLIPTextModelWithProjection.from_pretrained(   #using original text enc for stable sampling
             "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
-        )
-tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
+        torch_dtype=dtype
+    )
+tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", torch_dtype=dtype)
 scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler")
 pipe = Pipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
 pipe.to(device)