update

hf_demo.py

@@ -10,7 +10,9 @@ from PIL import Image
 
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained("rhfeiyang/art-free-diffusion-v1",).to(device)
+dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16
+pipe = DiffusionPipeline.from_pretrained("rhfeiyang/art-free-diffusion-v1",
+                                            dtype=dtype).to(device)
 
 from inference import get_lora_network, inference, get_validation_dataloader
 lora_map = {
@@ -33,7 +35,7 @@ lora_map = {
     "Henri Matisse": "henri-matisse_subset1",
     "Joan Miro": "joan-miro_subset2",
 }
-
+@spaces.GPU
 def demo_inference_gen(adapter_choice:str, prompt:str, samples:int=1,seed:int=0, steps=50, guidance_scale=7.5):
     adapter_path = lora_map[adapter_choice]
     if adapter_path not in [None, "None"]:
@@ -48,7 +50,7 @@ def demo_inference_gen(adapter_choice:str, prompt:str, samples:int=1,seed:int=0,
                             start_noise=-1, show=False, style_prompt="sks art", no_load=True,
                             from_scratch=True, device=device)[0][1.0]
     return pred_images
-
+@spaces.GPU
 def demo_inference_stylization(adapter_path:str, prompts:list, image:list, start_noise=800,seed:int=0):
     infer_loader = get_validation_dataloader(prompts, image)
     network = get_lora_network(pipe.unet, adapter_path,"all_up")["network"]