initial commit

.vscode/settings.json

@@ -1,6 +0,0 @@
-{
-	"[python]": {
-		"editor.defaultFormatter": "ms-python.black-formatter"
-	},
-	"python.formatting.provider": "none"
-}

handler.py

@@ -1,128 +0,0 @@
-import torch
-from PIL import Image
-from diffusers import (
-    StableDiffusionControlNetImg2ImgPipeline,
-    ControlNetModel,
-    DDIMScheduler,
-)
-from diffusers.utils import load_image
-import openai
-from io import BytesIO
-import base64
-import qrcode
-import random
-
-qrcode_data = "https://www.vertxdesigns.com/"
-prompt = "masterpiece, best quality, mecha, no humans, black armor, blue eyes, science fiction, fire, laser canon beam, war, conflict, destroyed city background"
-negative_prompt = "UnrealisticDream, FastNegativeEmbedding"
-
-
-qr = qrcode.QRCode(
-    version=1,
-    error_correction=qrcode.constants.ERROR_CORRECT_H,
-    box_size=10,
-    border=4,
-)
-qr.add_data(qrcode_data)
-qr.make(fit=True)
-img = qr.make_image(fill_color="black", back_color="white")
-
-# Resize image
-basewidth = 768
-wpercent = basewidth / float(img.size[0])
-hsize = int((float(img.size[1]) * float(wpercent)))
-qrcode_image = img.resize((basewidth, hsize), Image.LANCZOS)
-
-# Display the image
-qrcode_image
-# img.save('qrcode.png')
-
-
-# Initialize the control net model and pipeline.
-controlnet = ControlNetModel.from_pretrained(
-    "DionTimmer/controlnet_qrcode-control_v11p_sd21", torch_dtype=torch.float16
-)
-
-pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-2-1",
-    controlnet=controlnet,
-    safety_checker=None,
-    torch_dtype=torch.float16,
-)
-
-# Enable memory efficient attention.
-pipe.enable_xformers_memory_efficient_attention()
-
-# Set the scheduler for the pipeline.
-pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
-
-# Enable CPU offload for the model.
-pipe.enable_model_cpu_offload()
-
-
-# Resizes input_image to a specified resolution while maintaining the aspect ratio.
-def resize_for_condition_image(input_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 get_random_seed():
-    return random.randint(1, 1e8)  # random integer between 1 and 1,000,000.
-
-
-# Generate and store your seed.
-seed = get_random_seed()
-
-# Set the seed for the random number generator.
-generator = torch.manual_seed(seed)
-
-# Print the seed.
-print(seed)
-
-
-openai.api_key = "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"
-response = openai.Image.create(prompt=prompt, n=1, size="1024x1024")
-image_url = response.data[0].url
-print(image_url)
-
-
-init_image = load_image(image_url)
-
-# Set the control image to the qrcode image.
-control_image = qrcode_image
-
-# Resize the initial image
-init_image = resize_for_condition_image(init_image, 768)
-
-# Run the image generation process using the pipeline.
-image = pipe(
-    prompt=prompt,
-    negative_prompt=negative_prompt,
-    image=init_image,  # The initial image, set as a QR code image
-    control_image=control_image,  # QR code image
-    width=768,
-    height=768,
-    guidance_scale=7.5,  # The influence of the 'prompt' 0-50
-    controlnet_conditioning_scale=1.6,  # The influence of the qr code 1-5
-    generator=generator,  # Random seed for the generation process
-    strength=0.99,  # Noise added to the QR code 0-1
-    num_inference_steps=150,  # The number of steps in the image generation process
-)
-
-
-image.images[0]
-
-
-pil_image = image.images[0]
-buffered = BytesIO()
-pil_image.save(buffered, format="PNG")
-image_base64 = base64.b64encode(buffered.getvalue()).decode()
-print(f"First 10 characters: {image_base64[:10]}")
-print(f"Length of string: {len(image_base64):,}")