add

.gitea/workflows/build.yaml

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+name: Build
+run-name: ${{ github.actor }} is upgrade release 🚀
+on: [push]
+env:
+  REPOSITORY: ${{ github.repository }}
+  COMMIT_ID: ${{ github.sha }}
+jobs:
+  Build-Deploy-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v3
+      -
+        name: Setup Git LFS
+        run: |
+          git lfs install
+          git lfs fetch
+          git lfs checkout                              
+      - name: List files in the repository
+        run: |
+                                                                                ls ${{ github.workspace }}
+      -
+        name: Docker Image Info
+        id: image-info
+        run: |
+          echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
+          echo "::set-output name=image_tag::${COMMIT_ID:0:10}"                              
+      -
+        name: Login to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          registry: artifacts.iflytek.com
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      -
+        name: Build and push
+        run: |
+          docker version
+          docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
+          docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
+          docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}                              
+      - run: echo "🍏 This job's status is ${{ job.status }}."

Dockerfile

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+FROM python:3.10.6
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
+RUN pip install -r requirements.txt
+
+CMD ["python", "app.py"]

app.py

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+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import os
+import random
+import tempfile
+
+import gradio as gr
+import imageio
+import numpy as np
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from gradio.themes.utils import sizes
+
+
+theme = gr.themes.Default(radius_size=sizes.radius_none).set(
+    block_label_text_color = '#4D63FF',
+    block_title_text_color = '#4D63FF',
+    button_primary_text_color = '#4D63FF',
+    button_primary_background_fill='#FFFFFF',
+    button_primary_border_color='#4D63FF',
+    button_primary_background_fill_hover='#EDEFFF',
+)
+
+css = "footer {visibility: hidden}"
+
+
+MAX_NUM_FRAMES = int(os.getenv('MAX_NUM_FRAMES', '200'))
+DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES,
+                         int(os.getenv('DEFAULT_NUM_FRAMES', '16')))
+
+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)
+    for frame in frames:
+        writer.append_data(frame)
+    writer.close()
+    return out_file.name
+
+
+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
+    return to_video(frames, 8)
+
+
+examples = [
+    ['An astronaut riding a horse.', 0, 16, 25],
+    ['A panda eating bamboo on a rock.', 0, 16, 25],
+    ['Spiderman is surfing.', 0, 16, 25],
+]
+
+with gr.Blocks(theme=theme, css=css) as demo:
+    gr.Markdown("""
+        <div align='center' ><font size='60'>通过文本合成视频</font></div>
+    """)
+    with gr.Group():
+        with gr.Box():
+            with gr.Row(elem_id='prompt-container').style(equal_height=True):
+                prompt = gr.Text(
+                    label='Prompt',
+                    show_label=False,
+                    max_lines=1,
+                    placeholder='输入提示',
+                    elem_id='prompt-text-input').style(container=False)
+                run_button = gr.Button('生成视频').style(
+                    full_width=False)
+        result = gr.Video(label='Result', show_label=False, elem_id='gallery')
+        with gr.Accordion('高级选项', open=False):
+            seed = gr.Slider(
+                label='Seed',
+                minimum=-1,
+                maximum=1000000,
+                step=1,
+                value=-1,
+                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.'
+            )
+            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,
+    ]
+    gr.Examples(examples=examples,
+                inputs=inputs,
+                outputs=result,
+                fn=generate,
+                cache_examples=os.getenv('SYSTEM') == 'spaces',
+                label="示例")
+
+    prompt.submit(fn=generate, inputs=inputs, outputs=result)
+    run_button.click(fn=generate, inputs=inputs, outputs=result)
+
+
+demo.queue(api_open=False, max_size=15).launch(server_name="0.0.0.0")

requirements.txt

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+accelerate==0.17.1
+git+https://ghproxy.com/https://github.com/huggingface/diffusers@9dc8444
+gradio==3.23.0
+huggingface-hub==0.13.3
+imageio[ffmpeg]==2.26.1
+torch==2.0.0
+torchvision==0.15.1
+transformers==4.27.2