generate qr codes and images on the fly

app.py

@@ -1,6 +1,10 @@
 import torch
 import gradio as gr
 from PIL import Image
+import qrcode
+from gradio_client import Client
+from pathlib import Path
+
 from diffusers import (
     StableDiffusionControlNetImg2ImgPipeline,
     ControlNetModel,
@@ -9,6 +13,16 @@ from diffusers import (
 from diffusers.utils import load_image
 from PIL import Image
 
+
+sd_client = Client("stabilityai/stable-diffusion")
+
+qrcode_generator = qrcode.QRCode(
+    version=1,
+    error_correction=qrcode.constants.ERROR_CORRECT_H,
+    box_size=10,
+    border=0,
+)
+
 controlnet = ControlNetModel.from_pretrained(
     "DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16
 )
@@ -40,6 +54,7 @@ def resize_for_condition_image(input_image: Image.Image, resolution: int):
 def inference(
     init_image: Image.Image,
     qrcode_image: Image.Image,
+    qr_code_content: str,
     prompt: str,
     negative_prompt: str,
     guidance_scale: float = 10.0,
@@ -48,9 +63,37 @@ def inference(
     seed: int = -1,
     num_inference_steps: int = 30,
 ):
-    init_image = resize_for_condition_image(init_image, 768)
-    qrcode_image = resize_for_condition_image(qrcode_image, 768)
+    if prompt is None or prompt == "":
+        raise gr.Error("Prompt is required")
+
+    if qrcode_image is None and qr_code_content is None:
+        raise gr.Error("QR Code Image or QR Code Content is required")
+
+    if init_image is None:
+        print("Generating random image from prompt using Stable Diffusion")
+        # generate image from prompt
+        img_dir = sd_client.predict(prompt, negative_prompt, 7, fn_index=1)
+        images = Path(img_dir).rglob("*.jpg")
+        init_image = Image.open(next(images))
+
+    if qr_code_content is not None or qr_code_content != "":
+        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)
 
+    init_image = resize_for_condition_image(init_image, 768)
     generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
 
     out = pipe(
@@ -58,15 +101,15 @@ def inference(
         negative_prompt=negative_prompt,
         image=init_image,
         control_image=qrcode_image,  # type: ignore
-        width=768,
-        height=768,
+        width=768,  # type: ignore
+        height=768,  # type: ignore
         guidance_scale=float(guidance_scale),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
+        controlnet_conditioning_scale=float(controlnet_conditioning_scale),  # type: ignore
         generator=generator,
         strength=float(strength),
         num_inference_steps=num_inference_steps,
     )
-    return out.images[0]
+    return out.images[0]  # type: ignore
 
 
 with gr.Blocks() as blocks:
@@ -79,13 +122,26 @@ with gr.Blocks() as blocks:
 
     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")
+            qr_code_content = gr.Textbox(
+                label="QR Code Content",
+                info="QR Code Content or URL",
+                value="",
+            )
+            prompt = gr.Textbox(
+                label="Prompt",
+                info="Prompt is required. If init image is not provided, then it will be generated from prompt using Stable Diffusion 2.1",
+            )
             negative_prompt = gr.Textbox(
                 label="Negative Prompt",
                 value="ugly, disfigured, low quality, blurry, nsfw",
             )
+            init_image = gr.Image(label="Init Image (Optional)", type="pil")
+
+            qr_code_image = gr.Image(
+                label="QR Code Image (Optional)",
+                type="pil",
+            )
+
             with gr.Accordion(label="Params"):
                 guidance_scale = gr.Slider(
                     minimum=0.0,
@@ -120,6 +176,7 @@ with gr.Blocks() as blocks:
         inputs=[
             init_image,
             qr_code_image,
+            qr_code_content,
             prompt,
             negative_prompt,
             guidance_scale,
@@ -135,18 +192,53 @@ with gr.Blocks() as blocks:
             [
                 "./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,
-            ]
+            ],
+            [
+                "./examples/init.jpeg",
+                None,
+                "https://huggingface.co",
+                "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,
+            ],
+            [
+                None,
+                None,
+                "https://huggingface.co",
+                "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,
+            ],
+            [
+                None,
+                None,
+                "https://huggingface.co",
+                "A flying cat over a jungle",
+                "ugly, disfigured, low quality, blurry, nsfw",
+                10.0,
+                2.0,
+                0.8,
+                2313123,
+            ],
         ],
         fn=inference,
         inputs=[
             init_image,
             qr_code_image,
+            qr_code_content,
             prompt,
             negative_prompt,
             guidance_scale,

requirements.txt

@@ -4,4 +4,5 @@ accelerate
 torch
 xformers
 gradio
-Pillow
+Pillow
+qrcode