Update app2.py

app2.py

@@ -30,8 +30,6 @@ import cv2
 import sys
 sys.path.append("../")
 from models.depth_normal_pipeline_clip import DepthNormalEstimationPipeline
-#from models.depth_normal_pipeline_clip_cfg import DepthNormalEstimationPipeline
-#from models.depth_normal_pipeline_clip_cfg_1 import DepthNormalEstimationPipeline as DepthNormalEstimationPipelineCFG
 from utils.seed_all import seed_all
 import matplotlib.pyplot as plt
 from utils.de_normalized import align_scale_shift
@@ -46,7 +44,6 @@ import torchvision.transforms.functional as TF
 from torchvision.transforms import InterpolationMode
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
-#pipe = DepthNormalEstimationPipeline.from_pretrained(CHECKPOINT)
 
 stable_diffusion_repo_path = '.'
 vae = AutoencoderKL.from_pretrained(stable_diffusion_repo_path, subfolder='vae')
@@ -56,7 +53,6 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(sd_image_variation
 feature_extractor = CLIPImageProcessor.from_pretrained(sd_image_variations_diffusers_path, subfolder="feature_extractor")
 
 unet = UNet2DConditionModel.from_pretrained('./wocfg/unet_ema')
-unet_cfg = UNet2DConditionModel.from_pretrained('./cfg/unet_ema')
 
 pipe = DepthNormalEstimationPipeline(vae=vae,
                             image_encoder=image_encoder,
@@ -64,13 +60,6 @@ pipe = DepthNormalEstimationPipeline(vae=vae,
                             unet=unet,
                             scheduler=scheduler)
 
-# pipe_cfg = DepthNormalEstimationPipelineCFG(vae=vae,
-#                             image_encoder=image_encoder,
-#                             feature_extractor=feature_extractor,
-#                             unet=unet_cfg,
-#                             scheduler=scheduler)
-
-    
 try:
     import xformers
     pipe.enable_xformers_memory_efficient_attention()
@@ -78,8 +67,6 @@ except:
     pass  # run without xformers
 
 pipe = pipe.to(device)
-#pipe_cfg = pipe_cfg.to(device)
-#run_demo_server(pipe)
 
 @spaces.GPU
 def depth_normal(img,
@@ -93,21 +80,6 @@ def depth_normal(img,
     seed = int(seed)
     torch.manual_seed(seed)
 
-    #img = img.resize((processing_res, processing_res), Image.Resampling.LANCZOS)
-
-    # if guidance_scale > 0:
-    #     pipe_out = pipe_cfg(
-    #         img,
-    #         denoising_steps=denoising_steps,
-    #         ensemble_size=ensemble_size,
-    #         processing_res=processing_res,
-    #         batch_size=0,
-    #         guidance_scale=guidance_scale,
-    #         domain=domain,
-    #         show_progress_bar=True,
-    #     )
-                    
-    # else:
     pipe_out = pipe(
         img,
         denoising_steps=denoising_steps,
@@ -178,13 +150,6 @@ def run_demo():
                          label="Data Type (Must Select One matches your image)",
                          value="indoor",
                      )
-                     #    guidance_scale = gr.Slider(
-                     #     label="Classifier Free Guidance Scale, 0 Recommended for no guidance",
-                     #     minimum=0,
-                     #     maximum=5,
-                     #     step=1,
-                     #     value=0,
-                     # )
                         denoising_steps = gr.Slider(
                          label="Number of denoising steps (More steps, better quality)",
                          minimum=1,