Update app.py

app.py

@@ -30,15 +30,15 @@ height=896
 device = "cuda"
 dtype = torch.float16
 result=[]
-step = 4
-accu=2
+step = 30
+accu=7.5
 repo = "ByteDance/AnimateDiff-Lightning"
 ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
-base = "emilianJR/epiCRealism"
-#base = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
-#vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device, dtype=dtype)
+#base = "emilianJR/epiCRealism"
+base = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device, dtype=dtype)
 #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)
-#adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=dtype, device=device)
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3", torch_dtype=dtype, device=device)
 
 css="""
 input, input::placeholder {
@@ -141,6 +141,7 @@ def Piper(image,positive,negative,motion):
         pipe.unload_lora_weights()
         if motion != "":
             pipe.load_lora_weights(motion, adapter_name="motion")
+            pipe.fuse_lora()
             pipe.set_adapters(["motion"], [0.7])
         last_motion = motion
 
@@ -193,7 +194,7 @@ def run(i,m,p1,p2,*result):
 
         return out
 
-pipe = AnimateDiffPipeline.from_pretrained(base, torch_dtype=dtype).to(device)
+pipe = AnimateDiffPipeline.from_pretrained(base, vae=vae, motion_adapter=adapter, torch_dtype=dtype).to(device)
 pipe.scheduler = DDIMScheduler(
         clip_sample=False,
         beta_start=0.00085,