diff --git a/panorama.py b/panorama.py
index 33d49ec..3bdab30 100644
--- a/panorama.py
+++ b/panorama.py
@@ -1,5 +1,8 @@
+from tqdm.auto import tqdm
+import copy
 from transformers import CLIPTextModel, CLIPTokenizer, logging
-from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler
+from diffusers import AutoencoderKL, UNet2DConditionModel, DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler
+
 
 # suppress partial model loading warning
 logging.set_verbosity_error()
@@ -52,7 +55,7 @@ def __init__(self, device, sd_version='2.0', hf_key=None):
             model_key = "runwayml/stable-diffusion-v1-5"
         else:
             raise ValueError(f'Stable-diffusion version {self.sd_version} not supported.')
-
+        # model_key = "../js-sd-svc/models/runway-sd-1.5"
         # Create model
         self.vae = AutoencoderKL.from_pretrained(model_key, subfolder="vae").to(self.device)
         self.tokenizer = CLIPTokenizer.from_pretrained(model_key, subfolder="tokenizer")
@@ -60,6 +63,8 @@ def __init__(self, device, sd_version='2.0', hf_key=None):
         self.unet = UNet2DConditionModel.from_pretrained(model_key, subfolder="unet").to(self.device)
 
         self.scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
+        print("now you can add what ever scheduler you want  from diffusers~~~")
+        self.scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_key, subfolder="scheduler")
 
         print(f'[INFO] loaded stable diffusion!')
 
@@ -90,7 +95,7 @@ def decode_latents(self, latents):
         return imgs
 
     @torch.no_grad()
-    def text2panorama(self, prompts, negative_prompts='', height=512, width=2048, num_inference_steps=50,
+    def text2panorama(self, prompts, negative_prompts='', height=512, width=2048, num_inference_steps=35,
                       guidance_scale=7.5):
 
         if isinstance(prompts, str):
@@ -110,32 +115,47 @@ def text2panorama(self, prompts, negative_prompts='', height=512, width=2048, nu
 
         self.scheduler.set_timesteps(num_inference_steps)
 
-        with torch.autocast('cuda'):
+        with torch.autocast('cuda'), tqdm(total=len(self.scheduler.timesteps)) as pbar:
             for i, t in enumerate(self.scheduler.timesteps):
                 count.zero_()
                 value.zero_()
-
-                for h_start, h_end, w_start, w_end in views:
+                #  -- core enhancement -----
+                noise_pred_all = torch.zeros(latent.shape, device=self.device)   #  full_latent as begning 
+                # for h_start, h_end, w_start, w_end in views:
+                for index in range(len(views)):
+                    h_start, h_end, w_start, w_end = views[index]
+                    
                     # TODO we can support batches, and pass multiple views at once to the unet
                     latent_view = latent[:, :, h_start:h_end, w_start:w_end]
 
                     # expand the latents if we are doing classifier-free guidance to avoid doing two forward passes.
                     latent_model_input = torch.cat([latent_view] * 2)
-
+                    latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
                     # predict the noise residual
                     noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeds)['sample']
-
+                    
                     # perform guidance
                     noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
                     noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
+                    
 
-                    # compute the denoising step with the reference model
-                    latents_view_denoised = self.scheduler.step(noise_pred, t, latent_view)['prev_sample']
-                    value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
                     count[:, :, h_start:h_end, w_start:w_end] += 1
 
+                    # put the noise to the pos it belongto 
+                    noise_pred_all[:, :, h_start:h_end, w_start:w_end] += noise_pred
+                    
+                    # # compute the denoising step with the reference model  -- no  need anymore~
+                    # latents_view_denoised = self.scheduler.step(noise_pred, t, latent_view)['prev_sample']
+                    # value[:, :, h_start:h_end, w_start:w_end] += latents_view_denoised
+                    # count[:, :, h_start:h_end, w_start:w_end] += 1
+                    
+                noise_pred = torch.where(count > 0, noise_pred_all / count, noise_pred_all)
+                latent = self.scheduler.step(noise_pred, t, latent)['prev_sample']
+                
+                pbar.update()
+
                 # take the MultiDiffusion step
-                latent = torch.where(count > 0, value / count, value)
+                # latent = torch.where(count > 0, value / count, value)
 
         # Img latents -> imgs
         imgs = self.decode_latents(latent)  # [1, 3, 512, 512]
@@ -152,7 +172,7 @@ def text2panorama(self, prompts, negative_prompts='', height=512, width=2048, nu
     parser.add_argument('--H', type=int, default=512)
     parser.add_argument('--W', type=int, default=4096)
     parser.add_argument('--seed', type=int, default=0)
-    parser.add_argument('--steps', type=int, default=50)
+    parser.add_argument('--steps', type=int, default=40)
     opt = parser.parse_args()
 
     seed_everything(opt.seed)