From 8f1e6149a834cff2f25d4495af52738218de5b92 Mon Sep 17 00:00:00 2001
From: DeepBeepMeep <deepbeepmeep@yahoo.com>
Date: Thu, 3 Apr 2025 01:31:32 +0200
Subject: [PATCH] fixed bugs

---
 gradio_server.py  | 21 +++++++++++----------
 rife/inference.py |  4 ++--
 2 files changed, 13 insertions(+), 12 deletions(-)

diff --git a/gradio_server.py b/gradio_server.py
index d883726..e85f67b 100644
--- a/gradio_server.py
+++ b/gradio_server.py
@@ -1773,7 +1773,6 @@ def generate_video(
             # yield f"Video generation was aborted. Total Generation Time: {end_time-start_time:.1f}s"
         else:
             sample = samples.cpu()
-            # video = rearrange(sample.cpu().numpy(), "c t h w -> t h w c")
 
             time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%Hh%Mm%Ss")
             if os.name == 'nt':
@@ -1782,14 +1781,14 @@ def generate_video(
                 file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(prompt[:100]).strip()}.mp4"
             video_path = os.path.join(save_path, file_name)
             # if False: # for testing
-            #     torch.save(sample, "ouput.pt")
+            #     torch.save(sample, "output.pt")
             # else:
-            #     sample =torch.load("ouput.pt")
+            #     sample =torch.load("output.pt")
             exp = 0
             fps = 16
 
             if len(temporal_upsampling) > 0 or len(spatial_upsampling) > 0:                
-                progress_args = [0, status + " - Upsampling"]
+                progress_args = [(num_inference_steps , num_inference_steps) , status + " - Upsampling"  ,  num_inference_steps]
                 progress(*progress_args )   
                 gen["progress_args"] = progress_args
 
@@ -1804,7 +1803,7 @@ def generate_video(
                 fps = fps * 2**exp
 
             if len(spatial_upsampling) > 0:
-                from wan.utils.utils import resize_lanczos
+                from wan.utils.utils import resize_lanczos # need multithreading or to do lanczos with cuda
                 if spatial_upsampling == "lanczos1.5":
                     scale = 1.5
                 else:
@@ -2712,10 +2711,12 @@ def generate_video_tab(image2video=False):
                 queue_df = gr.DataFrame(
                     headers=["Qty","Prompt", "Length","Steps","Start", "End", "", "", ""],
                     datatype=[ "str","markdown","str", "markdown", "markdown", "markdown",    "str", "str", "str"],
+                    column_widths= ["50","", "65","55", "60", "60", "30", "30", "35"],
                     interactive=False,
                     col_count=(9, "fixed"),
                     wrap=True,
                     value=[],
+                    line_breaks= True,
                     visible= False,
                     # every=1,  
                     elem_id="queue_df"
@@ -3266,15 +3267,16 @@ def create_demo():
             pointer-events: none;
             text-align: center;
             vertical-align: middle;
+            font-size:11px;
         }
-        #queue_df table {
+        #xqueue_df table {
             width: 100%;
             overflow: hidden !important;
         }
-        #queue_df::-webkit-scrollbar {
+        #xqueue_df::-webkit-scrollbar {
             display: none !important;
         }
-        #queue_df {
+        #xqueue_df {
             scrollbar-width: none !important;
             -ms-overflow-style: none !important;
         }
@@ -3292,9 +3294,8 @@ def create_demo():
             cursor: default !important;
             pointer-events: none;
         }
-        #queue_df th:nth-child(2),
+        #xqueue_df th:nth-child(2),
         #queue_df td:nth-child(2) {
-            width: auto;
             text-align: center;
             vertical-align: middle;
             white-space: normal;
diff --git a/rife/inference.py b/rife/inference.py
index 24a2bdd..8f775d3 100644
--- a/rife/inference.py
+++ b/rife/inference.py
@@ -73,7 +73,7 @@ def process_frames(model, device, frames, exp):
         ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
 
         break_flag = False
-        if ssim > 0.996:        
+        if ssim > 0.996 or pos > 100:        
             pos += 1
             frame = get_frame(frames, pos)
             if frame is None:
@@ -86,7 +86,7 @@ def process_frames(model, device, frames, exp):
             I1 = model.inference(I0, I1, scale)
             I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
             ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
-            frame = I1[0]
+            frame = I1[0][:, :h, :w]
         
         if ssim < 0.2:
             output = []