README.md CHANGED
@@ -4,7 +4,7 @@ emoji: 🦀
4
  colorFrom: indigo
5
  colorTo: red
6
  sdk: gradio
7
- sdk_version: 5.49.1
8
  app_file: app.py
9
  pinned: false
10
  license: mit
 
4
  colorFrom: indigo
5
  colorTo: red
6
  sdk: gradio
7
+ sdk_version: 4.0.0
8
  app_file: app.py
9
  pinned: false
10
  license: mit
opensora/serve/gradio_web_server.py CHANGED
@@ -5,39 +5,6 @@ import sys
5
  import os
6
  import random
7
 
8
- import spaces
9
-
10
- # diffusers 0.24 imports `HfFolder` and `cached_download` from huggingface_hub
11
- # at module load. Both were removed in hub 0.26 / 1.x. Stub them so the import
12
- # succeeds; the demo path doesn't actually exercise these functions.
13
- import huggingface_hub as _hf_hub
14
- if not hasattr(_hf_hub, "cached_download"):
15
- def _cached_download_removed(*a, **k):
16
- raise NotImplementedError(
17
- "huggingface_hub.cached_download was removed; bump diffusers or pin hub<0.26."
18
- )
19
- _hf_hub.cached_download = _cached_download_removed
20
- if not hasattr(_hf_hub, "HfFolder"):
21
- class _HfFolderStub:
22
- @staticmethod
23
- def get_token():
24
- import os
25
- return os.environ.get("HF_TOKEN")
26
- _hf_hub.HfFolder = _HfFolderStub
27
-
28
- # Force xformers to use the Cutlass kernel — the auto-picked FA3 path is
29
- # Hopper-only and crashes on Blackwell (sm_120) with "CUDA error ... invalid argument".
30
- try:
31
- import xformers.ops as _xops
32
- _orig_mea = _xops.memory_efficient_attention
33
- _cutlass_ops = (_xops.fmha.cutlass.FwOp, _xops.fmha.cutlass.BwOp)
34
- def _mea(*a, **kw):
35
- kw.setdefault("op", _cutlass_ops)
36
- return _orig_mea(*a, **kw)
37
- _xops.memory_efficient_attention = _mea
38
- except Exception:
39
- pass
40
-
41
  import imageio
42
  import torch
43
  from diffusers import PNDMScheduler
@@ -57,6 +24,8 @@ from opensora.models.diffusion.latte.modeling_latte import LatteT2V
57
  from opensora.sample.pipeline_videogen import VideoGenPipeline
58
  from opensora.serve.gradio_utils import block_css, title_markdown, randomize_seed_fn, set_env, examples, DESCRIPTION
59
 
 
 
60
  @spaces.GPU(duration=300)
61
  def generate_img(prompt, sample_steps, scale, seed=0, randomize_seed=False, force_images=False):
62
  seed = int(randomize_seed_fn(seed, randomize_seed))
 
5
  import os
6
  import random
7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8
  import imageio
9
  import torch
10
  from diffusers import PNDMScheduler
 
24
  from opensora.sample.pipeline_videogen import VideoGenPipeline
25
  from opensora.serve.gradio_utils import block_css, title_markdown, randomize_seed_fn, set_env, examples, DESCRIPTION
26
 
27
+ import spaces
28
+
29
  @spaces.GPU(duration=300)
30
  def generate_img(prompt, sample_steps, scale, seed=0, randomize_seed=False, force_images=False):
31
  seed = int(randomize_seed_fn(seed, randomize_seed))
requirements.txt CHANGED
@@ -1,12 +1,12 @@
1
- torch==2.8.0
2
- torchvision==0.23.0
3
  transformers==4.39.1
4
  accelerate==0.28.0
5
  albumentations==1.4.0
6
- av
7
  decord==0.6.0
8
  einops==0.7.0
9
- fastapi
10
  gdown==5.1.0
11
  h5py==3.10.0
12
  idna==3.6
@@ -33,7 +33,7 @@ torchdiffeq==0.2.3
33
  torchmetrics==1.3.2
34
  tqdm==4.66.2
35
  urllib3==2.2.1
36
- uvicorn
37
  diffusers==0.24.0
38
  scikit-video==1.1.11
39
  imageio-ffmpeg==0.4.9
@@ -43,3 +43,5 @@ ftfy==6.1.3
43
  moviepy==1.0.3
44
  wandb==0.16.3
45
  tensorboard==2.14.0
 
 
 
1
+ torch==2.1.2
2
+ torchvision==0.16.2
3
  transformers==4.39.1
4
  accelerate==0.28.0
5
  albumentations==1.4.0
6
+ av==11.0.0
7
  decord==0.6.0
8
  einops==0.7.0
9
+ fastapi==0.110.0
10
  gdown==5.1.0
11
  h5py==3.10.0
12
  idna==3.6
 
33
  torchmetrics==1.3.2
34
  tqdm==4.66.2
35
  urllib3==2.2.1
36
+ uvicorn==0.27.1
37
  diffusers==0.24.0
38
  scikit-video==1.1.11
39
  imageio-ffmpeg==0.4.9
 
43
  moviepy==1.0.3
44
  wandb==0.16.3
45
  tensorboard==2.14.0
46
+ pydantic==2.6.4
47
+ deepspeed==0.12.6