first

.gitattributes

@@ -32,3 +32,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+__pycache__
+venv

app.py

@@ -0,0 +1,161 @@
+import torch
+import gradio as gr
+from PIL import Image
+from diffusers import (
+    StableDiffusionControlNetImg2ImgPipeline,
+    ControlNetModel,
+    DDIMScheduler,
+)
+from diffusers.utils import load_image
+from PIL import Image
+
+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,
+)
+
+pipe.enable_xformers_memory_efficient_attention()
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+pipe.enable_model_cpu_offload()
+
+
+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
+
+
+def inference(
+    init_image: Image.Image,
+    qrcode_image: Image.Image,
+    prompt: str,
+    negative_prompt: str,
+    guidance_scale: float = 10.0,
+    controlnet_conditioning_scale: float = 2.0,
+    strength: float = 0.8,
+    seed: int = -1,
+    num_inference_steps: int = 50,
+):
+    init_image = resize_for_condition_image(init_image, 768)
+    qrcode_image = resize_for_condition_image(qrcode_image, 768)
+
+    generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
+
+    out = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        image=init_image,  # type: ignore
+        control_image=qrcode_image,  # type: ignore
+        width=768,  # type: ignore
+        height=768,  # type: ignore
+        guidance_scale=guidance_scale,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,  # type: ignore
+        generator=generator,
+        strength=strength,
+        num_inference_steps=num_inference_steps,
+    )  # type: ignore
+    return out.images[0]
+
+
+with gr.Blocks() as blocks:
+    gr.Markdown(
+        """# AI QR Code Generator
+                
+                model by: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15
+                """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            init_image = gr.Image(label="Init Image", type="pil")
+            qr_code_image = gr.Image(label="QR Code Image", type="pil")
+            prompt = gr.Textbox(label="Prompt")
+            negative_prompt = gr.Textbox(
+                label="Negative Prompt",
+                value="ugly, disfigured, low quality, blurry, nsfw",
+            )
+            with gr.Accordion(label="Params"):
+                guidance_scale = gr.Slider(
+                    minimum=0.0,
+                    maximum=50.0,
+                    step=0.1,
+                    value=10.0,
+                    label="Guidance Scale",
+                )
+                controlnet_conditioning_scale = gr.Slider(
+                    minimum=0.0,
+                    maximum=5.0,
+                    step=0.1,
+                    value=2.0,
+                    label="Controlnet Conditioning Scale",
+                )
+                strength = gr.Slider(
+                    minimum=0.0, maximum=1.0, step=0.1, value=0.8, label="Strength"
+                )
+                seed = gr.Slider(
+                    minimum=-1,
+                    maximum=9999999999,
+                    step=1,
+                    value=2313123,
+                    label="Seed",
+                    randomize=True,
+                )
+            run_btn = gr.Button("Run")
+        with gr.Column():
+            result_image = gr.Image(label="Result Image")
+    run_btn.click(
+        inference,
+        inputs=[
+            init_image,
+            qr_code_image,
+            prompt,
+            negative_prompt,
+            guidance_scale,
+            controlnet_conditioning_scale,
+            strength,
+            seed,
+        ],
+        outputs=[result_image],
+    )
+
+    gr.Examples(
+        examples=[
+            [
+                "./examples/init.jpeg",
+                "./examples/qrcode.png",
+                "crisp QR code prominently displayed on a billboard amidst the bustling skyline of New York City, with iconic landmarks subtly featured in the background.",
+                "ugly, disfigured, low quality, blurry, nsfw",
+                10.0,
+                2.0,
+                0.8,
+                2313123,
+            ]
+        ],
+        fn=inference,
+        inputs=[
+            init_image,
+            qr_code_image,
+            prompt,
+            negative_prompt,
+            guidance_scale,
+            controlnet_conditioning_scale,
+            strength,
+            seed,
+        ],
+        outputs=[result_image],
+    )
+
+blocks.queue()
+blocks.launch()

requirements.txt

@@ -0,0 +1,7 @@
+diffusers
+transformers
+accelerate
+torch
+xformers
+gradio
+Pillow