Duplicate from blanchon/qrcode-diffusion

Co-authored-by: Julien BLANCHON <blanchon@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+gradio_cached_examples/*
+cache/models*

Dockerfile

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+FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
+
+# Install dependencies
+COPY requirements.txt /tmp/requirements.txt
+RUN python -m pip install --upgrade pip && \
+    python -m pip install -r /tmp/requirements.txt
+
+# Copy source code
+WORKDIR /app
+RUN mkdir /app/cache
+COPY ./app.py /app/app.py
+
+# Run the application
+CMD ["python", "app.py"]

README.md

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+---
+title: QrCode Diffusion
+emoji: 📱
+colorFrom: red
+colorTo: yellow
+python_version: 3.10.11
+sdk: gradio
+sdk_version: 3.34.0
+app_file: app.py
+tags:
+- qrcode
+- stable-diffusion
+- controlnet
+pinned: true
+duplicated_from: blanchon/qrcode-diffusion
+---
+
+# QrCode Diffusion
+
+## Description
+
+This is a simple application that allows you to generate a QrCode and apply a stable diffusion algorithm to it. The diffusion algorithm used is the ControlNet algorithm.
+
+## How to use
+
+```python
+python app.py
+```
+
+And then go to the link that appears in the terminal.
+
+## References
+
+- ControlNet
+- Stable Diffusion
+
+## Credits
+
+The original idea is from [nhciao](https://www.reddit.com/user/nhciao/) ([Twitter](https://twitter.com/nhciao)) and [this post](https://www.reddit.com/r/StableDiffusion/comments/141hg9x/controlnet_for_qr_code/).
+
+## Other
+
+This is also fun <https://qrbtf.com/> and [open source ](https://github.com/ciaochaos/qrbtf).

app.py

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+from typing import Optional
+
+import gradio as gr
+import qrcode
+import torch
+from diffusers import (
+    ControlNetModel,
+    EulerAncestralDiscreteScheduler,
+    StableDiffusionControlNetPipeline,
+)
+from gradio.components import Image, Radio, Slider, Textbox, Number
+from PIL import Image as PilImage
+from typing_extensions import Literal
+
+
+def main():
+    device = (
+        'cuda' if torch.cuda.is_available() 
+        else 'mps' if torch.backends.mps.is_available() 
+        else 'cpu'
+    )
+
+    controlnet_tile = ControlNetModel.from_pretrained(
+        "lllyasviel/control_v11f1e_sd15_tile",
+        torch_dtype=torch.float16,
+        use_safetensors=False,
+        cache_dir="./cache"
+    ).to(device)
+
+    controlnet_brightness  = ControlNetModel.from_pretrained(
+        "ioclab/control_v1p_sd15_brightness",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+        cache_dir="./cache"
+    ).to(device)
+
+    def make_pipe(hf_repo: str, device: str) -> StableDiffusionControlNetPipeline:
+        pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            hf_repo,
+            controlnet=[controlnet_tile, controlnet_brightness],
+            torch_dtype=torch.float16,
+            cache_dir="./cache",
+        )
+        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+        # pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+        return pipe.to(device)
+
+    pipes = {
+        "DreamShaper": make_pipe("Lykon/DreamShaper", device),
+        # "DreamShaper": make_pipe("Lykon/DreamShaper", "cpu"),
+        # "Realistic Vision V1.4": make_pipe("SG161222/Realistic_Vision_V1.4", "cpu"),
+        # "OpenJourney": make_pipe("prompthero/openjourney", "cpu"),
+        # "Anything V3": make_pipe("Linaqruf/anything-v3.0", "cpu"),
+    }
+
+    def move_pipe(hf_repo: str):
+        for pipe_name, pipe in pipes.items():
+            if pipe_name != hf_repo:
+                pipe.to("cpu")
+        return pipes[hf_repo].to(device)
+
+    def predict(
+        model: Literal[
+            "DreamShaper",
+            # "Realistic Vision V1.4",
+            # "OpenJourney",
+            # "Anything V3"
+        ],
+        qrcode_data: str,
+        prompt: str,
+        negative_prompt: Optional[str] = None,
+        num_inference_steps: int = 100,
+        guidance_scale: int = 9,
+        controlnet_conditioning_tile: float = 0.25,
+        controlnet_conditioning_brightness: float = 0.45,
+        seed: int = 1331,
+    ) -> PilImage:
+        generator = torch.Generator(device).manual_seed(seed)
+        if model == "DreamShaper":
+            pipe = pipes["DreamShaper"]
+            # pipe = move_pipe("DreamShaper Vision V1.4")
+        # elif model == "Realistic Vision V1.4":
+        #     pipe = move_pipe("Realistic Vision V1.4")
+        # elif model == "OpenJourney":
+        #     pipe = move_pipe("OpenJourney")
+        # elif model == "Anything V3":
+        #     pipe = move_pipe("Anything V3")
+
+        
+        qr = qrcode.QRCode(
+            error_correction=qrcode.constants.ERROR_CORRECT_H,
+            box_size=11,
+            border=9,
+        )
+        qr.add_data(qrcode_data)
+        qr.make(fit=True)
+        qrcode_image = qr.make_image(
+            fill_color="black",
+            back_color="white"
+        ).convert("RGB")
+        qrcode_image = qrcode_image.resize((512, 512), PilImage.LANCZOS)
+
+        image = pipe(
+            prompt,
+            [qrcode_image, qrcode_image],
+            num_inference_steps=num_inference_steps,
+            generator=generator,
+            negative_prompt=negative_prompt,
+            guidance_scale=guidance_scale,
+            controlnet_conditioning_scale=[
+                controlnet_conditioning_tile,
+                controlnet_conditioning_brightness
+            ]
+        ).images[0]
+
+        return image
+
+
+    ui = gr.Interface(
+        fn=predict,
+        inputs=[
+            Radio(
+                value="DreamShaper",
+                label="Model",
+                choices=[
+                    "DreamShaper",
+                    # "Realistic Vision V1.4",
+                    # "OpenJourney",
+                    # "Anything V3"
+                ],
+            ),
+            Textbox(
+                value="https://twitter.com/JulienBlanchon",
+                label="QR Code Data",
+            ),
+            Textbox(
+                value="Japanese ramen with chopsticks, egg and steam, ultra detailed 8k",
+                label="Prompt",
+            ),
+            Textbox(
+                value="logo, watermark, signature, text, BadDream, UnrealisticDream",
+                label="Negative Prompt",
+                optional=True
+            ),
+            Slider(
+                value=100,
+                label="Number of Inference Steps",
+                minimum=10,
+                maximum=400,
+                step=1,
+            ),
+            Slider(
+                value=9,
+                label="Guidance Scale",
+                minimum=1,
+                maximum=20,
+                step=1,
+            ),
+            Slider(
+                value=0.25,
+                label="Controlnet Conditioning Tile",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+
+            ),
+            Slider(
+                value=0.45,
+                label="Controlnet Conditioning Brightness",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+            ),
+            Number(
+                value=1,
+                label="Seed",
+                precision=0,
+            ),
+
+        ],
+        outputs=Image(
+            label="Generated Image",
+            type="pil",
+        ),
+        examples=[
+            [
+                "DreamShaper",
+                "https://twitter.com/JulienBlanchon",
+                "Japanese ramen with chopsticks, egg and steam, ultra detailed 8k",
+                "logo, watermark, signature, text, BadDream, UnrealisticDream",
+                100,
+                9,
+                0.25,
+                0.45,
+                1,
+            ],
+            # [
+            #     "Anything V3",
+            #     "https://twitter.com/JulienBlanchon",
+            #     "Japanese ramen with chopsticks, egg and steam, ultra detailed 8k",
+            #     "logo, watermark, signature, text, BadDream, UnrealisticDream",
+            #     100,
+            #     9,
+            #     0.25,
+            #     0.60,
+            #     1,
+            # ],
+            [
+                "DreamShaper",
+                "https://twitter.com/JulienBlanchon",
+                "processor, chipset, electricity, black and white board",
+                "logo, watermark, signature, text, BadDream, UnrealisticDream",
+                300,
+                9,
+                0.50,
+                0.30,
+                1,
+            ],
+        ],
+        cache_examples=True,
+        title="Stable Diffusion QR Code Controlnet",
+        description="Generate QR Code with Stable Diffusion and Controlnet",
+        allow_flagging="never",
+        max_batch_size=1,
+    )
+
+    ui.queue(concurrency_count=10).launch()
+
+if __name__ == "__main__":
+    main()

docker-compose.yml

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+services:
+  gradio:
+    build: .
+    ports:
+      - "7860:7860"
+    volumes:
+      - ./cache:/app/cache
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [ gpu ]

requirements.txt

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+diffusers
+transformers
+accelerate
+safetensors
+gradio
+qrcode
+opencv-python-headless