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from PIL import Image |
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from diffusers import ( |
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StableDiffusionControlNetImg2ImgPipeline, |
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ControlNetModel, |
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DDIMScheduler, |
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) |
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from diffusers.utils import load_image |
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import torch |
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import openai |
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from io import BytesIO |
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import base64 |
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import qrcode |
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class QRImageHandler: |
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def __init__( |
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self, |
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controlnet_path="DionTimmer/controlnet_qrcode-control_v11p_sd21", |
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pipeline_path="stabilityai/stable-diffusion-2-1", |
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): |
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self.controlnet = ControlNetModel.from_pretrained( |
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controlnet_path, torch_dtype=torch.float16 |
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) |
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self.pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( |
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pipeline_path, |
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controlnet=self.controlnet, |
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safety_checker=None, |
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torch_dtype=torch.float16, |
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) |
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self.pipe.enable_xformers_memory_efficient_attention() |
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self.pipe.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config) |
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self.pipe.enable_model_cpu_offload() |
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@staticmethod |
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def resize_for_condition_image(input_image: Image, resolution: int): |
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input_image = input_image.convert("RGB") |
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W, H = input_image.size |
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k = float(resolution) / min(H, W) |
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H *= k |
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W *= k |
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H = int(round(H / 64.0)) * 64 |
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W = int(round(W / 64.0)) * 64 |
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img = input_image.resize((W, H), resample=Image.LANCZOS) |
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return img |
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def __call__( |
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self, |
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prompt, |
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negative_prompt, |
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qrcode_data, |
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guidance_scale, |
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controlnet_conditioning_scale, |
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strength, |
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generator_seed, |
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width, |
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height, |
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num_inference_steps, |
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): |
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openai.api_key = "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" |
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qr = qrcode.QRCode( |
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version=1, |
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error_correction=qrcode.constants.ERROR_CORRECT_H, |
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box_size=10, |
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border=4, |
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) |
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qr.add_data(qrcode_data) |
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qr.make(fit=True) |
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img = qr.make_image(fill_color="black", back_color="white") |
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basewidth = 768 |
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wpercent = basewidth / float(img.size[0]) |
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hsize = int((float(img.size[1]) * float(wpercent))) |
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qrcode_image = img.resize((basewidth, hsize), Image.LANCZOS) |
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response = openai.Image.create(prompt=prompt, n=1, size="1024x1024") |
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image_url = response.data[0].url |
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init_image = load_image(image_url) |
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control_image = qrcode_image |
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init_image = self.resize_for_condition_image(init_image, 768) |
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generator = torch.manual_seed(generator_seed) |
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image = self.pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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image=init_image, |
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control_image=control_image, |
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width=width, |
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height=height, |
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guidance_scale=guidance_scale, |
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controlnet_conditioning_scale=controlnet_conditioning_scale, |
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generator=generator, |
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strength=strength, |
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num_inference_steps=num_inference_steps, |
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) |
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pil_image = image.images[0] |
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buffered = BytesIO() |
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pil_image.save(buffered, format="PNG") |
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image_base64 = base64.b64encode(buffered.getvalue()).decode() |
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return image_base64 |
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