Update app.py

app.py

@@ -28,7 +28,7 @@ time=2
 device = "cuda"
 dtype = torch.float16
 result=[]
-step = 2
+step = 8
 
 css="""
 input, input::placeholder {
@@ -120,7 +120,7 @@ def generate_random_string(length):
     characters = string.ascii_letters + string.digits
     return ''.join(random.choice(characters) for _ in range(length))
 
-@spaces.GPU(duration=75)
+@spaces.GPU(duration=85)
 def Piper(name,positive_prompt,negative,motion):
     global step
     global fps
@@ -142,7 +142,7 @@ def Piper(name,positive_prompt,negative,motion):
         height=1024,
         width=576,
         num_inference_steps=step,
-        guidance_scale=1,
+        guidance_scale=7,
         num_frames=(fps*time)
     )
 
@@ -156,7 +156,7 @@ def infer(pm):
         _do = ['beautiful', 'playful', 'photographed', 'realistic', 'dynamic poze', 'deep field', 'reasonable coloring', 'rough texture', 'best quality', 'focused']
         if p1 != "":
             _do.append(f'{p1}')
-        posi = " ".join(_do)+neg
+        posi = " ".join(_do)
 
         out = Piper(name,posi,neg,pm["m"])
         export_to_gif(out.frames[0],name,fps=fps)
@@ -180,7 +180,7 @@ def run(m,p1,p2,*result):
 
         return out
 
-adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-3")
+#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)
     
@@ -189,7 +189,7 @@ ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
 base = "emilianJR/epiCRealism"
 #base = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
     
-pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype, token=os.getenv("hf_token")).to(device)
+pipe = AnimateDiffPipeline.from_pretrained(base, torch_dtype=dtype, token=os.getenv("hf_token")).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)