DeepBeepMeep commited on
Commit
d6835bd
·
1 Parent(s): d290612

fixed bugs

Browse files
Files changed (2) hide show
  1. gradio_server.py +11 -10
  2. rife/inference.py +2 -2
gradio_server.py CHANGED
@@ -1773,7 +1773,6 @@ def generate_video(
1773
  # yield f"Video generation was aborted. Total Generation Time: {end_time-start_time:.1f}s"
1774
  else:
1775
  sample = samples.cpu()
1776
- # video = rearrange(sample.cpu().numpy(), "c t h w -> t h w c")
1777
 
1778
  time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%Hh%Mm%Ss")
1779
  if os.name == 'nt':
@@ -1782,14 +1781,14 @@ def generate_video(
1782
  file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(prompt[:100]).strip()}.mp4"
1783
  video_path = os.path.join(save_path, file_name)
1784
  # if False: # for testing
1785
- # torch.save(sample, "ouput.pt")
1786
  # else:
1787
- # sample =torch.load("ouput.pt")
1788
  exp = 0
1789
  fps = 16
1790
 
1791
  if len(temporal_upsampling) > 0 or len(spatial_upsampling) > 0:
1792
- progress_args = [0, status + " - Upsampling"]
1793
  progress(*progress_args )
1794
  gen["progress_args"] = progress_args
1795
 
@@ -1804,7 +1803,7 @@ def generate_video(
1804
  fps = fps * 2**exp
1805
 
1806
  if len(spatial_upsampling) > 0:
1807
- from wan.utils.utils import resize_lanczos
1808
  if spatial_upsampling == "lanczos1.5":
1809
  scale = 1.5
1810
  else:
@@ -2712,10 +2711,12 @@ def generate_video_tab(image2video=False):
2712
  queue_df = gr.DataFrame(
2713
  headers=["Qty","Prompt", "Length","Steps","Start", "End", "", "", ""],
2714
  datatype=[ "str","markdown","str", "markdown", "markdown", "markdown", "str", "str", "str"],
 
2715
  interactive=False,
2716
  col_count=(9, "fixed"),
2717
  wrap=True,
2718
  value=[],
 
2719
  visible= False,
2720
  # every=1,
2721
  elem_id="queue_df"
@@ -3266,15 +3267,16 @@ def create_demo():
3266
  pointer-events: none;
3267
  text-align: center;
3268
  vertical-align: middle;
 
3269
  }
3270
- #queue_df table {
3271
  width: 100%;
3272
  overflow: hidden !important;
3273
  }
3274
- #queue_df::-webkit-scrollbar {
3275
  display: none !important;
3276
  }
3277
- #queue_df {
3278
  scrollbar-width: none !important;
3279
  -ms-overflow-style: none !important;
3280
  }
@@ -3292,9 +3294,8 @@ def create_demo():
3292
  cursor: default !important;
3293
  pointer-events: none;
3294
  }
3295
- #queue_df th:nth-child(2),
3296
  #queue_df td:nth-child(2) {
3297
- width: auto;
3298
  text-align: center;
3299
  vertical-align: middle;
3300
  white-space: normal;
 
1773
  # yield f"Video generation was aborted. Total Generation Time: {end_time-start_time:.1f}s"
1774
  else:
1775
  sample = samples.cpu()
 
1776
 
1777
  time_flag = datetime.fromtimestamp(time.time()).strftime("%Y-%m-%d-%Hh%Mm%Ss")
1778
  if os.name == 'nt':
 
1781
  file_name = f"{time_flag}_seed{seed}_{sanitize_file_name(prompt[:100]).strip()}.mp4"
1782
  video_path = os.path.join(save_path, file_name)
1783
  # if False: # for testing
1784
+ # torch.save(sample, "output.pt")
1785
  # else:
1786
+ # sample =torch.load("output.pt")
1787
  exp = 0
1788
  fps = 16
1789
 
1790
  if len(temporal_upsampling) > 0 or len(spatial_upsampling) > 0:
1791
+ progress_args = [(num_inference_steps , num_inference_steps) , status + " - Upsampling" , num_inference_steps]
1792
  progress(*progress_args )
1793
  gen["progress_args"] = progress_args
1794
 
 
1803
  fps = fps * 2**exp
1804
 
1805
  if len(spatial_upsampling) > 0:
1806
+ from wan.utils.utils import resize_lanczos # need multithreading or to do lanczos with cuda
1807
  if spatial_upsampling == "lanczos1.5":
1808
  scale = 1.5
1809
  else:
 
2711
  queue_df = gr.DataFrame(
2712
  headers=["Qty","Prompt", "Length","Steps","Start", "End", "", "", ""],
2713
  datatype=[ "str","markdown","str", "markdown", "markdown", "markdown", "str", "str", "str"],
2714
+ column_widths= ["50","", "65","55", "60", "60", "30", "30", "35"],
2715
  interactive=False,
2716
  col_count=(9, "fixed"),
2717
  wrap=True,
2718
  value=[],
2719
+ line_breaks= True,
2720
  visible= False,
2721
  # every=1,
2722
  elem_id="queue_df"
 
3267
  pointer-events: none;
3268
  text-align: center;
3269
  vertical-align: middle;
3270
+ font-size:11px;
3271
  }
3272
+ #xqueue_df table {
3273
  width: 100%;
3274
  overflow: hidden !important;
3275
  }
3276
+ #xqueue_df::-webkit-scrollbar {
3277
  display: none !important;
3278
  }
3279
+ #xqueue_df {
3280
  scrollbar-width: none !important;
3281
  -ms-overflow-style: none !important;
3282
  }
 
3294
  cursor: default !important;
3295
  pointer-events: none;
3296
  }
3297
+ #xqueue_df th:nth-child(2),
3298
  #queue_df td:nth-child(2) {
 
3299
  text-align: center;
3300
  vertical-align: middle;
3301
  white-space: normal;
rife/inference.py CHANGED
@@ -73,7 +73,7 @@ def process_frames(model, device, frames, exp):
73
  ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
74
 
75
  break_flag = False
76
- if ssim > 0.996:
77
  pos += 1
78
  frame = get_frame(frames, pos)
79
  if frame is None:
@@ -86,7 +86,7 @@ def process_frames(model, device, frames, exp):
86
  I1 = model.inference(I0, I1, scale)
87
  I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
88
  ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
89
- frame = I1[0]
90
 
91
  if ssim < 0.2:
92
  output = []
 
73
  ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
74
 
75
  break_flag = False
76
+ if ssim > 0.996 or pos > 100:
77
  pos += 1
78
  frame = get_frame(frames, pos)
79
  if frame is None:
 
86
  I1 = model.inference(I0, I1, scale)
87
  I1_small = F.interpolate(I1, (32, 32), mode='bilinear', align_corners=False)
88
  ssim = ssim_matlab(I0_small[:, :3], I1_small[:, :3])
89
+ frame = I1[0][:, :h, :w]
90
 
91
  if ssim < 0.2:
92
  output = []