QRCode generate

app.py

@@ -1,8 +1,11 @@
 import gradio as gr
 import torch
 from diffusers import StableDiffusionPipeline
+from PIL import Image
+import qrcode
+from qrcode.constants import ERROR_CORRECT_H
 
-# Load Stable Diffusion (v1.5)
+# -------- Stable Diffusion setup --------
 device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 dtype = torch.float16 if device != "cpu" else torch.float32
 
@@ -11,22 +14,48 @@ pipe = StableDiffusionPipeline.from_pretrained(
     torch_dtype=dtype
 ).to(device)
 
-def generate(prompt, steps, guidance, seed):
-    generator = torch.Generator(device=device).manual_seed(int(seed)) if seed != 0 else None
-    with torch.autocast(device):
-        image = pipe(prompt, num_inference_steps=steps, guidance_scale=guidance, generator=generator).images[0]
-    return image
-
-demo = gr.Interface(
-    fn=generate,
-    inputs=[
-        gr.Textbox(label="Prompt", value="A fantasy castle at sunset"),
-        gr.Slider(10, 50, value=30, label="Steps"),
-        gr.Slider(1, 12, value=7.5, label="Guidance Scale"),
-        gr.Number(value=0, label="Seed (0 = random)")
-    ],
-    outputs=gr.Image()
-)
+def sd_generate(prompt, steps, guidance, seed):
+    gen = torch.Generator(device=device).manual_seed(int(seed)) if int(seed) != 0 else None
+    def run():
+        return pipe(prompt, num_inference_steps=int(steps), guidance_scale=float(guidance), generator=gen).images[0]
+    # autocast only where supported
+    if device in ("cuda", "mps"):
+        with torch.autocast(device):
+            return run()
+    else:
+        return run()
+
+# -------- QR code helper --------
+def make_qr(url: str = "http://www.mybirdfire.com", size: int = 512, border: int = 2) -> Image.Image:
+    qr = qrcode.QRCode(
+        version=None,
+        error_correction=ERROR_CORRECT_H,  # high EC to survive stylization later
+        box_size=10,
+        border=border
+    )
+    qr.add_data(url.strip())
+    qr.make(fit=True)
+    img = qr.make_image(fill_color="black", back_color="white").convert("RGB")
+    return img.resize((size, size), resample=Image.NEAREST)
+
+# -------- UI --------
+with gr.Blocks() as demo:
+    gr.Markdown("## Stable Diffusion + QR (step by step)")
+
+    with gr.Tab("Stable Diffusion (prompt → image)"):
+        prompt = gr.Textbox(label="Prompt", value="A fantasy castle at sunset")
+        steps  = gr.Slider(10, 50, value=30, label="Steps", step=1)
+        cfg    = gr.Slider(1, 12, value=7.5, label="Guidance Scale", step=0.1)
+        seed   = gr.Number(value=0, label="Seed (0 = random)", precision=0)
+        out_sd = gr.Image(label="Generated Image")
+        gr.Button("Generate").click(sd_generate, [prompt, steps, cfg, seed], out_sd)
+
+    with gr.Tab("QR Maker (mybirdfire)"):
+        url   = gr.Textbox(label="URL/Text", value="http://www.mybirdfire.com")
+        size  = gr.Slider(256, 1024, value=512, step=64, label="Size (px)")
+        quiet = gr.Slider(0, 8, value=2, step=1, label="Border (quiet zone)")
+        out_qr = gr.Image(label="QR Code", type="pil")
+        gr.Button("Generate QR").click(make_qr, [url, size, quiet], out_qr)
 
 if __name__ == "__main__":
     demo.launch()