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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+import torch
+import gradio as gr
+from PIL import Image
+import qrcode
+from pathlib import Path
+from multiprocessing import cpu_count
+import requests
+import io
+import os
+from PIL import Image
+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}"
+
+from diffusers import (
+    StableDiffusionPipeline,
+    StableDiffusionControlNetImg2ImgPipeline,
+    ControlNetModel,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    DEISMultistepScheduler,
+    HeunDiscreteScheduler,
+    EulerDiscreteScheduler,
+)
+
+qrcode_generator = qrcode.QRCode(
+    version=1,
+    error_correction=qrcode.ERROR_CORRECT_H,
+    box_size=10,
+    border=4,
+)
+
+controlnet = ControlNetModel.from_pretrained(
+    "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
+)
+
+pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    controlnet=controlnet,
+    safety_checker=None,
+    torch_dtype=torch.float16,
+).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+
+
+def resize_for_condition_image(input_image: Image.Image, resolution: int):
+    input_image = input_image.convert("RGB")
+    W, H = input_image.size
+    k = float(resolution) / min(H, W)
+    H *= k
+    W *= k
+    H = int(round(H / 64.0)) * 64
+    W = int(round(W / 64.0)) * 64
+    img = input_image.resize((W, H), resample=Image.LANCZOS)
+    return img
+
+
+SAMPLER_MAP = {
+    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
+    "DPM++ Karras": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True),
+    "Heun": lambda config: HeunDiscreteScheduler.from_config(config),
+    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
+    "DDIM": lambda config: DDIMScheduler.from_config(config),
+    "DEIS": lambda config: DEISMultistepScheduler.from_config(config),
+}
+
+
+def inference(
+    qr_code_content: str,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: float = 10.0,
+    controlnet_conditioning_scale: float = 2.0,
+    strength: float = 0.8,
+    seed: int = -1,
+    init_image: Image.Image | None = None,
+    qrcode_image: Image.Image | None = None,
+    use_qr_code_as_init_image = True,
+    sampler = "DPM++ Karras SDE",
+):
+    if prompt is None or prompt == "":
+        raise gr.Error("Prompt is required")
+
+    if qrcode_image is None and qr_code_content == "":
+        raise gr.Error("QR Code Image or QR Code Content is required")
+
+    pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
+
+    generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
+
+    if qr_code_content != "" or qrcode_image.size == (1, 1):
+        print("Generating QR Code from content")
+        qr = qrcode.QRCode(
+            version=1,
+            error_correction=qrcode.constants.ERROR_CORRECT_H,
+            box_size=10,
+            border=4,
+        )
+        qr.add_data(qr_code_content)
+        qr.make(fit=True)
+
+        qrcode_image = qr.make_image(fill_color="black", back_color="white")
+        qrcode_image = resize_for_condition_image(qrcode_image, 768)
+    else:
+        print("Using QR Code Image")
+        qrcode_image = resize_for_condition_image(qrcode_image, 768)
+
+    # hack due to gradio examples
+    init_image = qrcode_image
+
+    out = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=qrcode_image,
+        control_image=qrcode_image,  # type: ignore
+        width=768,  # type: ignore
+        height=768,  # type: ignore
+        guidance_scale=float(guidance_scale),
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
+        generator=generator,
+        strength=float(strength),
+        num_inference_steps=40,
+    )
+    return out.images[0]  # type: ignore
+
+
+with gr.Blocks(theme=theme, css=css) as blocks:
+    gr.Markdown("""
+        <div align='center' ><font size='60'>艺术二维码生成</font></div>
+    """)
+    with gr.Row():
+        with gr.Column():
+            qr_code_content = gr.Textbox(
+                label="QR Code Content",
+                info="QR Code Content or URL",
+                value="",
+            )
+            with gr.Accordion(label="QR Code Image (Optional)", open=False):
+                qr_code_image = gr.Image(
+                    label="QR Code Image (Optional). Leave blank to automatically generate QR code",
+                    type="pil",
+                )
+
+            prompt = gr.Textbox(
+                label="Prompt",
+                info="Prompt that guides the generation towards",
+            )
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                value="ugly, disfigured, low quality, blurry, nsfw",
+            )
+            use_qr_code_as_init_image = gr.Checkbox(label="Use QR code as init image", value=True, interactive=False, info="Whether init image should be QR code. Unclick to pass init image or generate init image with Stable Diffusion 2.1")
+
+            with gr.Accordion(label="Init Images (Optional)", open=False, visible=False) as init_image_acc:
+                init_image = gr.Image(label="Init Image (Optional). Leave blank to generate image with SD 2.1", type="pil")
+
+
+            with gr.Accordion(
+                label="Params: The generated QR Code functionality is largely influenced by the parameters detailed below",
+                open=True,
+            ):
+                controlnet_conditioning_scale = gr.Slider(
+                    minimum=0.0,
+                    maximum=5.0,
+                    step=0.01,
+                    value=1.1,
+                    label="Controlnet Conditioning Scale",
+                )
+                strength = gr.Slider(
+                    minimum=0.0, maximum=1.0, step=0.01, value=0.9, label="Strength"
+                )
+                guidance_scale = gr.Slider(
+                    minimum=0.0,
+                    maximum=50.0,
+                    step=0.25,
+                    value=7.5,
+                    label="Guidance Scale",
+                )
+                sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="DPM++ Karras SDE", label="Sampler")
+                seed = gr.Slider(
+                    minimum=-1,
+                    maximum=9999999999,
+                    step=1,
+                    value=2313123,
+                    label="Seed",
+                    randomize=True,
+                )
+            with gr.Row():
+                run_btn = gr.Button("Run")
+        with gr.Column():
+            result_image = gr.Image(label="Result Image")
+    run_btn.click(
+        inference,
+        inputs=[
+            qr_code_content,
+            prompt,
+            negative_prompt,
+            guidance_scale,
+            controlnet_conditioning_scale,
+            strength,
+            seed,
+            init_image,
+            qr_code_image,
+            use_qr_code_as_init_image,
+            sampler,
+        ],
+        outputs=[result_image],
+    )
+
+    gr.Examples(
+        examples=[
+            [
+                "https://huggingface.co/",
+                "A sky view of a colorful lakes and rivers flowing through the desert",
+                "ugly, disfigured, low quality, blurry, nsfw",
+                7.5,
+                1.3,
+                0.9,
+                5392011833,
+                None,
+                None,
+                True,
+                "DPM++ Karras SDE",
+            ],
+            [
+                "https://huggingface.co/",
+                "Bright sunshine coming through the cracks of a wet, cave wall of big rocks",
+                "ugly, disfigured, low quality, blurry, nsfw",
+                7.5,
+                1.11,
+                0.9,
+                2523992465,
+                None,
+                None,
+                True,
+                "DPM++ Karras SDE",
+            ],
+            [
+                "https://huggingface.co/",
+                "Sky view of highly aesthetic, ancient greek thermal baths  in beautiful nature",
+                "ugly, disfigured, low quality, blurry, nsfw",
+                7.5,
+                1.5,
+                0.9,
+                2523992465,
+                None,
+                None,
+                True,
+                "DPM++ Karras SDE",
+            ],
+        ],
+        fn=inference,
+        inputs=[
+            qr_code_content,
+            prompt,
+            negative_prompt,
+            guidance_scale,
+            controlnet_conditioning_scale,
+            strength,
+            seed,
+            init_image,
+            qr_code_image,
+            use_qr_code_as_init_image,
+            sampler,
+        ],
+        outputs=[result_image],
+        cache_examples=True,
+    )
+
+blocks.queue(concurrency_count=1, max_size=20)
+blocks.launch(share=bool(os.environ.get("SHARE", False)), server_name="0.0.0.0")

gradio_cached_examples/25/log.csv

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+Result Image,flag,username,timestamp
+/home/weisong/ailab/qr-code-ai-art-generator/gradio_cached_examples/25/Result Image/tmpy3ycqi9s.png,,,2023-07-10 16:20:11.226037
+/home/weisong/ailab/qr-code-ai-art-generator/gradio_cached_examples/25/Result Image/tmpbhvk3bzo.png,,,2023-07-10 16:20:18.825394
+/home/weisong/ailab/qr-code-ai-art-generator/gradio_cached_examples/25/Result Image/tmpe7hrzx_p.png,,,2023-07-10 16:20:26.469537

gradio_cached_examples/26/log.csv

@@ -0,0 +1,4 @@
+Result Image,flag,username,timestamp
+/home/weisong/ailab/qr-code-ai-art-generator/gradio_cached_examples/26/Result Image/tmp30ja3fxu.png,,,2023-06-21 16:06:53.802426
+/home/weisong/ailab/qr-code-ai-art-generator/gradio_cached_examples/26/Result Image/tmpp0r32l4s.png,,,2023-06-21 16:07:01.586941
+/home/weisong/ailab/qr-code-ai-art-generator/gradio_cached_examples/26/Result Image/tmphhhet_c5.png,,,2023-06-21 16:07:09.475027

requirements.txt

@@ -0,0 +1,8 @@
+git+https://ghproxy.com/https://github.com/huggingface/diffusers
+transformers
+accelerate
+torch
+xformers
+gradio
+Pillow
+qrcode