Update app.py

app.py

@@ -16,7 +16,31 @@ model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
 pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
 flow_shift = 1.0 #5.0  1.0 for image, 5.0 for 720P, 3.0 for 480P
-pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
+# pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
+import torch
+from diffusers import DDIMScheduler  # Changed to DDIMScheduler
+from diffusers import WanPipeline, AutoencoderKLWan
+from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
+from transformers import T5EncoderModel, T5Tokenizer
+from PIL import Image
+import numpy as np
+import gradio as gr
+import spaces
+
+model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+flow_shift = 1.0  # 1.0 for image, 5.0 for 720P, 3.0 for 480P
+
+# Configure DDIMScheduler with a beta schedule
+pipe.scheduler = DDIMScheduler.from_config(
+    pipe.scheduler.config,
+    beta_start=0.00085,  # Starting beta value
+    beta_end=0.012,      # Ending beta value
+    beta_schedule="linear",  # Linear beta schedule (other options: "scaled_linear", "squaredcos_cap_v2")
+    num_train_timesteps=1000,  # Number of timesteps
+    flow_shift=flow_shift
+)
 
 
 @spaces.GPU()