Update app.py

app.py

@@ -9,25 +9,15 @@ import gradio as gr
 import numpy as np
 from lxml.html import fromstring
 from pathos.threading import ThreadPool as Pool
-from diffusers import DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
-#from diffusers.pipelines.flux import FluxPipeline
-from diffusers.utils import export_to_gif
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
+#from diffusers import DiffusionPipeline, AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
+from diffusers.pipelines.flux import FluxPipeline
+#from diffusers.utils import export_to_gif
+#from huggingface_hub import hf_hub_download
+#from safetensors.torch import load_file
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
-
-step = 2
-repo = "ByteDance/AnimateDiff-Lightning"
-ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
-base = "emilianJR/epiCRealism"
-
-adapter = MotionAdapter().to(device, dtype)
-adapter.load_state_dict(load_file(hf_hub_download(repo ,ckpt), device=device))
-pipe = AnimateDiffPipeline.from_pretrained(base, motion_adapter=adapter, torch_dtype=dtype).to(device)
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
-
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype).to(device)
 pipe.enable_vae_slicing()
 pipe.enable_vae_tiling()
 
@@ -89,14 +79,14 @@ def generate_random_string(length):
     characters = string.ascii_letters + string.digits
     return ''.join(random.choice(characters) for _ in range(length))
 
-@spaces.GPU(duration=45)
+@spaces.GPU(duration=35)
 def Piper(_do):
     return pipe(
         _do,
         height=256,
         width=768,
-        num_frames=16,
-        num_inference_steps=step,
+        num_inference_steps=50,
+        max_sequence_length=512,
         guidance_scale=7
     )