init

app.py

@@ -6,15 +6,14 @@ from diffusers import AutoencoderTiny
 from torchvision.transforms.functional import to_pil_image, center_crop, resize, to_tensor
 
 device = 'cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu'
-d_type = torch.float32 if device == 'mps' else torch.float16
 
 model_id = "madebyollin/taesd"
-vae = AutoencoderTiny.from_pretrained(model_id, safetensors=True, torch_dtype=d_type).to(device)
+vae = AutoencoderTiny.from_pretrained(model_id, safetensors=True).to(device)
 
 
 @torch.no_grad()
 def decode(image):
-    t = to_tensor(image).unsqueeze(0).to(device, dtype=d_type)
+    t = to_tensor(image).unsqueeze(0).to(device)
     unscaled_t = vae.unscale_latents(t)
     reconstructed = vae.decoder(unscaled_t).clamp(0, 1)
     return to_pil_image(reconstructed[0])