add
Build-Deploy-Actions
Details
Build-Deploy-Actions
Details
This commit is contained in:
songw
parent
5bd1c47b1c
commit
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name: Build
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run-name: ${{ github.actor }} is upgrade release 🚀
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on: [push]
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env:
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REPOSITORY: ${{ github.repository }}
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COMMIT_ID: ${{ github.sha }}
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jobs:
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Build-Deploy-Actions:
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runs-on: ubuntu-latest
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steps:
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- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
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- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
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- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
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- name: Check out repository code
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uses: actions/checkout@v3
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-
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name: Setup Git LFS
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run: |
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git lfs install
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git lfs fetch
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git lfs checkout
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- name: List files in the repository
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run: |
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ls ${{ github.workspace }}
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-
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name: Docker Image Info
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id: image-info
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run: |
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echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
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echo "::set-output name=image_tag::${COMMIT_ID:0:10}"
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-
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name: Login to Docker Hub
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uses: docker/login-action@v2
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with:
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registry: artifacts.iflytek.com
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v2
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-
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name: Build and push
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run: |
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docker version
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docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
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docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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- run: echo "🍏 This job's status is ${{ job.status }}."
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FROM python:3.10.6
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WORKDIR /app
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COPY . /app
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RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
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RUN pip install -r requirements.txt
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CMD ["python", "app.py"]
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#!/usr/bin/env python
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from __future__ import annotations
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import os
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import random
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import tempfile
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import gradio as gr
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import imageio
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import numpy as np
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import torch
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
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from gradio.themes.utils import sizes
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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css = "footer {visibility: hidden}"
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MAX_NUM_FRAMES = int(os.getenv('MAX_NUM_FRAMES', '200'))
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DEFAULT_NUM_FRAMES = min(MAX_NUM_FRAMES,
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int(os.getenv('DEFAULT_NUM_FRAMES', '16')))
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pipe = DiffusionPipeline.from_pretrained('damo-vilab/text-to-video-ms-1.7b',
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torch_dtype=torch.float16,
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variant='fp16')
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.enable_model_cpu_offload()
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pipe.enable_vae_slicing()
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def to_video(frames: list[np.ndarray], fps: int) -> str:
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out_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False)
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writer = imageio.get_writer(out_file.name, format='FFMPEG', fps=fps)
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for frame in frames:
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writer.append_data(frame)
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writer.close()
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return out_file.name
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def generate(prompt: str, seed: int, num_frames: int,
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num_inference_steps: int) -> str:
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if seed == -1:
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seed = random.randint(0, 1000000)
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generator = torch.Generator().manual_seed(seed)
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frames = pipe(prompt,
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num_inference_steps=num_inference_steps,
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num_frames=num_frames,
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generator=generator).frames
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return to_video(frames, 8)
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examples = [
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['An astronaut riding a horse.', 0, 16, 25],
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['A panda eating bamboo on a rock.', 0, 16, 25],
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['Spiderman is surfing.', 0, 16, 25],
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]
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with gr.Blocks(theme=theme, css=css) as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>通过文本合成视频</font></div>
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""")
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with gr.Group():
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with gr.Box():
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with gr.Row(elem_id='prompt-container').style(equal_height=True):
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prompt = gr.Text(
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label='Prompt',
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show_label=False,
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max_lines=1,
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placeholder='输入提示',
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elem_id='prompt-text-input').style(container=False)
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run_button = gr.Button('生成视频').style(
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full_width=False)
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result = gr.Video(label='Result', show_label=False, elem_id='gallery')
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with gr.Accordion('高级选项', open=False):
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seed = gr.Slider(
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label='Seed',
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minimum=-1,
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maximum=1000000,
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step=1,
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value=-1,
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info='If set to -1, a different seed will be used each time.')
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num_frames = gr.Slider(
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label='Number of frames',
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minimum=16,
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maximum=MAX_NUM_FRAMES,
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step=1,
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value=16,
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info=
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'Note that the content of the video also changes when you change the number of frames.'
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)
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num_inference_steps = gr.Slider(label='Number of inference steps',
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minimum=10,
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maximum=50,
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step=1,
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value=25)
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inputs = [
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prompt,
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seed,
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num_frames,
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num_inference_steps,
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]
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gr.Examples(examples=examples,
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inputs=inputs,
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outputs=result,
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fn=generate,
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cache_examples=os.getenv('SYSTEM') == 'spaces',
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label="示例")
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prompt.submit(fn=generate, inputs=inputs, outputs=result)
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run_button.click(fn=generate, inputs=inputs, outputs=result)
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demo.queue(api_open=False, max_size=15).launch(server_name="0.0.0.0")
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@ -0,0 +1,8 @@
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accelerate==0.17.1
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git+https://ghproxy.com/https://github.com/huggingface/diffusers@9dc8444
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gradio==3.23.0
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huggingface-hub==0.13.3
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imageio[ffmpeg]==2.26.1
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torch==2.0.0
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torchvision==0.15.1
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transformers==4.27.2
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