m

controllers/gra_06_video/video.py

@@ -1,11 +1,9 @@
 #!/usr/bin/env python
 
 from __future__ import annotations
-
 import os
 import random
 import tempfile
-
 import gradio as gr
 import imageio
 import numpy as np
@@ -15,21 +13,26 @@ from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
 DESCRIPTION = '# [ModelScope Text to Video Synthesis](https://modelscope.cn/models/damo/text-to-video-synthesis/summary)'
 DESCRIPTION += '\n<p>For Colab usage, you can view <a href="https://colab.research.google.com/drive/1uW1ZqswkQ9Z9bp5Nbo5z59cAn7I0hE6R?usp=sharing" style="text-decoration: underline;" target="_blank">this webpage</a>.（the latest update on 2023.03.21）</p>'
 DESCRIPTION += '\n<p>This model can only be used for non-commercial purposes. To learn more about the model, take a look at the <a href="https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis" style="text-decoration: underline;" target="_blank">model card</a>.</p>'
+
 if (SPACE_ID := os.getenv('SPACE_ID')) is not None:
     DESCRIPTION += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>'
 
 MAX_NUM_FRAMES = int(os.getenv('MAX_NUM_FRAMES', '200'))
-DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES,
-                         int(os.getenv('DEFAULT_NUM_FRAMES', '16')))
+DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES, int(os.getenv('DEFAULT_NUM_FRAMES', '16')))
+
+# ✅ CUDA 対応修正
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+pipe = DiffusionPipeline.from_pretrained(
+    'damo-vilab/text-to-video-ms-1.7b',
+    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+    variant='fp16' if torch.cuda.is_available() else None
+).to(device)
 
-pipe = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b',
-                                         torch_dtype=torch.float16,
-                                         variant='fp16')
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 pipe.enable_model_cpu_offload()
 pipe.enable_vae_slicing()
 
-
 def to_video(frames: list[np.ndarray], fps: int) -> str:
     out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
     writer = imageio.get_writer(out_file.name, format='FFMPEG', fps=fps)
@@ -38,19 +41,18 @@ def to_video(frames: list[np.ndarray], fps: int) -> str:
     writer.close()
     return out_file.name
 
-
-def generate(prompt: str, seed: int, num_frames: int,
-             num_inference_steps: int) -> str:
+def generate(prompt: str, seed: int, num_frames: int, num_inference_steps: int) -> str:
     if seed == -1:
         seed = random.randint(0, 1000000)
-    generator = torch.Generator().manual_seed(seed)
-    frames = pipe(prompt,
-                  num_inference_steps=num_inference_steps,
-                  num_frames=num_frames,
-                  generator=generator).frames
+    generator = torch.Generator(device).manual_seed(seed)
+    frames = pipe(
+        prompt,
+        num_inference_steps=num_inference_steps,
+        num_frames=num_frames,
+        generator=generator
+    ).frames
     return to_video(frames, 8)
 
-
 examples = [
     ['An astronaut riding a horse.', 0, 16, 25],
     ['A panda eating bamboo on a rock.', 0, 16, 25],
@@ -60,16 +62,19 @@ examples = [
 with gr.Blocks() as gradio_interface:
     gr.Markdown(DESCRIPTION)
     with gr.Group():
-        with gr.Column():  # `gr.Box()` → `gr.Column()` に変更
-            with gr.Row(elem_id='prompt-container'):  # `.style()` を削除
-                prompt = gr.Text(
+        with gr.Column():  # ✅ `gr.Box()` → `gr.Column()` に変更
+            with gr.Row(elem_id='prompt-container'):
+                prompt = gr.Textbox(  # ✅ `gr.Text()` → `gr.Textbox()` に変更
                     label='Prompt',
                     show_label=False,
                     max_lines=1,
                     placeholder='Enter your prompt',
-                    elem_id='prompt-text-input')
-                run_button = gr.Button('Generate video')
+                    elem_id='prompt-text-input'
+                )
+                run_button = gr.Button('Generate video')  # ✅ `.style()` 削除
+
         result = gr.Video(label='Result', show_label=False, elem_id='gallery')
+
         with gr.Accordion('Advanced options', open=False):
             seed = gr.Slider(
                 label='Seed',
@@ -77,61 +82,59 @@ with gr.Blocks() as gradio_interface:
                 maximum=1000000,
                 step=1,
                 value=-1,
-                info='If set to -1, a different seed will be used each time.')
+                info='If set to -1, a different seed will be used each time.'
+            )
             num_frames = gr.Slider(
                 label='Number of frames',
                 minimum=16,
                 maximum=MAX_NUM_FRAMES,
                 step=1,
                 value=16,
-                info=
-                'Note that the content of the video also changes when you change the number of frames.'
+                info='Note that the content of the video also changes when you change the number of frames.'
+            )
+            num_inference_steps = gr.Slider(
+                label='Number of inference steps',
+                minimum=10,
+                maximum=50,
+                step=1,
+                value=25
             )
-            num_inference_steps = gr.Slider(label='Number of inference steps',
-                                            minimum=10,
-                                            maximum=50,
-                                            step=1,
-                                            value=25)
 
+    inputs = [prompt, seed, num_frames, num_inference_steps]
 
-    inputs = [
-        prompt,
-        seed,
-        num_frames,
-        num_inference_steps,
-    ]
-    gr.Examples(examples=examples,
-                inputs=inputs,
-                outputs=result,
-                fn=generate,
-                cache_examples=os.getenv('SYSTEM') == 'spaces')
+    gr.Examples(
+        examples=examples,
+        inputs=inputs,
+        outputs=result,
+        fn=generate,
+        cache_examples=os.getenv('SYSTEM') == 'spaces'
+    )
 
     prompt.submit(fn=generate, inputs=inputs, outputs=result)
     run_button.click(fn=generate, inputs=inputs, outputs=result)
 
+    with gr.Accordion(label='We are hiring (Based in Beijing / Hangzhou, China.)', open=False):
+        gr.HTML("""
+        <div class="acknowledgments">
+            <p>
+                If you're looking for an exciting challenge and the opportunity to work with cutting-edge technologies in AIGC and large-scale pretraining, then we are the place for you. We are looking for talented, motivated and creative individuals to join our team. If you are interested, please send your CV to us.
+            </p>
+            <p><b>EMAIL: [email protected]</b></p>
+        </div>
+        """)
 
-    with gr.Accordion(label='We are hiring(Based in Beijing / Hangzhou, China.)', open=False):
-        gr.HTML("""<div class="acknowledgments">
-                    <p>
-                        If you're looking for an exciting challenge and the opportunity to work with cutting-edge technologies in AIGC and large-scale pretraining, then we are the place for you. We are looking for talented, motivated and creative individuals to join our team. If you are interested, please send your CV to us.
-                    </p>
-                    <p>
-                        <b>EMAIL: [email protected]</b>.
-                    </p>
-                   </div>
-                """)
-    
     with gr.Accordion(label='Biases and content acknowledgment', open=False):
-        gr.HTML("""<div class="acknowledgments">
-                    <h4>Biases and content acknowledgment</h4>
-                    <p>
-                        Despite how impressive being able to turn text into video is, beware to the fact that this model may output content that reinforces or exacerbates societal biases. The training data includes LAION5B, ImageNet, Webvid and other public datasets. The model was not trained to realistically represent people or events, so using it to generate such content is beyond the model's capabilities.
-                    </p>
-                    <p>
-                        It is not intended to generate content that is demeaning or harmful to people or their environment, culture, religion, etc. Similarly, it is not allowed to generate pornographic, violent and bloody content generation. <b>The model is meant for research purposes</b>.
-                    </p>
-                    <p>
-                        To learn more about the model, head to its <a href="https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis" style="text-decoration: underline;" target="_blank">model card</a>.
-                    </p>
-                   </div>
-                """)
+        gr.HTML("""
+        <div class="acknowledgments">
+            <h4>Biases and content acknowledgment</h4>
+            <p>
+                Despite how impressive being able to turn text into video is, beware to the fact that this model may output content that reinforces or exacerbates societal biases. The training data includes LAION5B, ImageNet, Webvid and other public datasets. The model was not trained to realistically represent people or events, so using it to generate such content is beyond the model's capabilities.
+            </p>
+            <p>
+                It is not intended to generate content that is demeaning or harmful to people or their environment, culture, religion, etc. Similarly, it is not allowed to generate pornographic, violent and bloody content generation. <b>The model is meant for research purposes</b>.
+            </p>
+            <p>
+                To learn more about the model, head to its <a href="https://huggingface.co/damo-vilab/modelscope-damo-text-to-video-synthesis" target="_blank">model card</a>.
+            </p>
+        </div>
+        """)