Update app.py

app.py

@@ -29,6 +29,13 @@ device = "cuda"
 dtype = torch.float16
 result=[]
 step = 2
+repo = "ByteDance/AnimateDiff-Lightning"
+ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
+base = "emilianJR/epiCRealism"
+#base = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
+#adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3")
+#vae = AutoencoderKL.from_single_file("https://huggingface.co/stabilityai/sd-vae-ft-mse-original/vae-ft-mse-840000-ema-pruned.safetensors")
+#unet = UNet2DConditionModel.from_config("emilianJR/epiCRealism",subfolder="unet").to(device, dtype).load_state_dict(load_file(hf_hub_download("emilianJR/epiCRealism", "unet/diffusion_pytorch_model.safetensors"), device=device), strict=False)
 
 css="""
 input, input::placeholder {
@@ -122,12 +129,9 @@ def generate_random_string(length):
 
 @spaces.GPU(duration=65)
 def Piper(name,positive_prompt,negative,motion):
-    global step
-    global fps
-    global time
     global last_motion
-    global base
-    global device
+
+    pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
 
     if last_motion != motion:
         pipe.unload_lora_weights()
@@ -135,7 +139,7 @@ def Piper(name,positive_prompt,negative,motion):
             pipe.load_lora_weights(motion, adapter_name="motion")
             pipe.set_adapters(["motion"], [0.7])
         last_motion = motion
-
+    
     return pipe(
         positive_prompt,
         negative_prompt=negative,
@@ -180,18 +184,8 @@ def run(m,p1,p2,*result):
 
         return out
 
-#adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3")
-#vae = AutoencoderKL.from_single_file("https://huggingface.co/stabilityai/sd-vae-ft-mse-original/vae-ft-mse-840000-ema-pruned.safetensors")
-#unet = UNet2DConditionModel.from_config("emilianJR/epiCRealism",subfolder="unet").to(device, dtype).load_state_dict(load_file(hf_hub_download("emilianJR/epiCRealism", "unet/diffusion_pytorch_model.safetensors"), device=device), strict=False)
-    
-repo = "ByteDance/AnimateDiff-Lightning"
-ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
-base = "emilianJR/epiCRealism"
-#base = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
-
 pipe = AnimateDiffPipeline.from_pretrained(base, torch_dtype=dtype).to(device)
 pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
-pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
 pipe.enable_free_init(method="butterworth", use_fast_sampling=False)
 
 mp.set_start_method("spawn", force=True)