multimodalart HF staff commited on
Commit
f9979b6
1 Parent(s): 77956e4

revert back to 3s

Browse files
Files changed (1) hide show
  1. app.py +9 -16
app.py CHANGED
@@ -64,14 +64,14 @@ def load_model():
64
  model = load_model()
65
 
66
  # Text-to-video generation function
67
- @spaces.GPU(duration=200)
68
  def generate_video(prompt, image=None, duration=5, guidance_scale=9, video_guidance_scale=5, progress=gr.Progress(track_tqdm=True)):
69
- multiplier = 3
70
- temp = int(duration * multiplier) + 1
71
  torch_dtype = torch.bfloat16 if MODEL_DTYPE == "bf16" else torch.float32
72
  if(image):
73
- cropped_image = center_crop(image, 1280, 768)
74
- resized_image = cropped_image.resize((1280, 768))
75
  with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
76
  frames = model.generate_i2v(
77
  prompt=prompt,
@@ -97,26 +97,23 @@ def generate_video(prompt, image=None, duration=5, guidance_scale=9, video_guida
97
  output_type="pil",
98
  save_memory=True,
99
  )
100
- return frames, gr.update()
101
-
102
- def compose_video(frames):
103
  output_path = f"{str(uuid.uuid4())}_output_video.mp4"
104
- export_to_video(frames, output_path, fps=24)
105
- return output_path
106
 
107
  # Gradio interface
108
  with gr.Blocks() as demo:
109
  gr.Markdown("# Pyramid Flow")
110
  gr.Markdown("Pyramid Flow is a training-efficient Autoregressive Video Generation model based on Flow Matching. It is trained only on open-source datasets within 20.7k A100 GPU hours")
111
  gr.Markdown("[[Paper](https://arxiv.org/pdf/2410.05954)], [[Model](https://huggingface.co/rain1011/pyramid-flow-sd3)], [[Code](https://github.com/jy0205/Pyramid-Flow)]")
112
- frames = gr.State()
113
  with gr.Row():
114
  with gr.Column():
115
  with gr.Accordion("Image to Video (optional)", open=False):
116
  i2v_image = gr.Image(type="pil", label="Input Image")
117
  t2v_prompt = gr.Textbox(label="Prompt")
118
  with gr.Accordion("Advanced settings", open=False):
119
- t2v_duration = gr.Slider(minimum=1, maximum=2 if is_canonical else 10, value=2 if is_canonical else 5, step=1, label="Duration (seconds)")
120
  t2v_guidance_scale = gr.Slider(minimum=1, maximum=15, value=9, step=0.1, label="Guidance Scale")
121
  t2v_video_guidance_scale = gr.Slider(minimum=1, maximum=15, value=5, step=0.1, label="Video Guidance Scale")
122
  t2v_generate_btn = gr.Button("Generate Video")
@@ -145,10 +142,6 @@ with gr.Blocks() as demo:
145
  t2v_generate_btn.click(
146
  generate_video,
147
  inputs=[t2v_prompt, i2v_image, t2v_duration, t2v_guidance_scale, t2v_video_guidance_scale],
148
- outputs=[frames, t2v_output]
149
- ).then(
150
- compose_video,
151
- inputs=[frames],
152
  outputs=t2v_output
153
  )
154
 
 
64
  model = load_model()
65
 
66
  # Text-to-video generation function
67
+ @spaces.GPU(duration=120)
68
  def generate_video(prompt, image=None, duration=5, guidance_scale=9, video_guidance_scale=5, progress=gr.Progress(track_tqdm=True)):
69
+ multiplier = 0.8 if is_canonical else 2.4
70
+ temp = int(duration * 0.8) # Convert seconds to temp value (assuming 24 FPS)
71
  torch_dtype = torch.bfloat16 if MODEL_DTYPE == "bf16" else torch.float32
72
  if(image):
73
+ cropped_image = center_crop(image, 1280, 720)
74
+ resized_image = cropped_image.resize((1280, 720))
75
  with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
76
  frames = model.generate_i2v(
77
  prompt=prompt,
 
97
  output_type="pil",
98
  save_memory=True,
99
  )
 
 
 
100
  output_path = f"{str(uuid.uuid4())}_output_video.mp4"
101
+ export_to_video(frames, output_path, fps=8 if is_canonical else 24)
102
+ return output_path
103
 
104
  # Gradio interface
105
  with gr.Blocks() as demo:
106
  gr.Markdown("# Pyramid Flow")
107
  gr.Markdown("Pyramid Flow is a training-efficient Autoregressive Video Generation model based on Flow Matching. It is trained only on open-source datasets within 20.7k A100 GPU hours")
108
  gr.Markdown("[[Paper](https://arxiv.org/pdf/2410.05954)], [[Model](https://huggingface.co/rain1011/pyramid-flow-sd3)], [[Code](https://github.com/jy0205/Pyramid-Flow)]")
109
+
110
  with gr.Row():
111
  with gr.Column():
112
  with gr.Accordion("Image to Video (optional)", open=False):
113
  i2v_image = gr.Image(type="pil", label="Input Image")
114
  t2v_prompt = gr.Textbox(label="Prompt")
115
  with gr.Accordion("Advanced settings", open=False):
116
+ t2v_duration = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Duration (seconds)", visible=not is_canonical)
117
  t2v_guidance_scale = gr.Slider(minimum=1, maximum=15, value=9, step=0.1, label="Guidance Scale")
118
  t2v_video_guidance_scale = gr.Slider(minimum=1, maximum=15, value=5, step=0.1, label="Video Guidance Scale")
119
  t2v_generate_btn = gr.Button("Generate Video")
 
142
  t2v_generate_btn.click(
143
  generate_video,
144
  inputs=[t2v_prompt, i2v_image, t2v_duration, t2v_guidance_scale, t2v_video_guidance_scale],
 
 
 
 
145
  outputs=t2v_output
146
  )
147