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import torch
from PIL import Image
import qrcode
from pathlib import Path
from multiprocessing import cpu_count
import requests
import io
import os
from PIL import Image
from diffusers import (
StableDiffusionPipeline,
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
DDIMScheduler,
DPMSolverMultistepScheduler,
DEISMultistepScheduler,
HeunDiscreteScheduler,
EulerDiscreteScheduler,
)
SAMPLER_MAP = {
"DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
"DPM++ Karras": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True),
"Heun": lambda config: HeunDiscreteScheduler.from_config(config),
"Euler": lambda config: EulerDiscreteScheduler.from_config(config),
"DDIM": lambda config: DDIMScheduler.from_config(config),
"DEIS": lambda config: DEISMultistepScheduler.from_config(config),
}
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
class EndpointHandler():
def __init__(self, path=""):
qrcode_generator = qrcode.QRCode(
version=1,
error_correction=qrcode.ERROR_CORRECT_H,
box_size=10,
border=4,
)
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,
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
def __call__inference(self,
qr_code_content: str,
prompt: str,
negative_prompt: str,
guidance_scale: float = 10.0,
controlnet_conditioning_scale: float = 2.0,
strength: float = 0.8,
seed: int = -1,
init_image: Image.Image | None = None,
qrcode_image: Image.Image | None = None,
use_qr_code_as_init_image = True,
sampler = "DPM++ Karras SDE",
):
if prompt is None or prompt == "":
raise gr.Error("Prompt is required")
if qrcode_image is None and qr_code_content == "":
raise gr.Error("QR Code Image or QR Code Content is required")
pipe.scheduler = SAMPLER_MAP[sampler](pipe.scheduler.config)
generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
if qr_code_content != "" or qrcode_image.size == (1, 1):
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")
if init_image is None:
if use_qr_code_as_init_image:
init_image = qrcode_image.convert("RGB")
resolution = controlnet.config.resolution
qrcode_image = resize_for_condition_image(qrcode_image, resolution)
if init_image is not None:
init_image = init_image.convert("RGB")
init_image = resize_for_condition_image(init_image, resolution)
init_image = torch.nn.functional.interpolate(
torch.nn.functional.to_tensor(init_image).unsqueeze(0),
size=(resolution, resolution),
mode="bilinear",
align_corners=False,
)[0].unsqueeze(0)
else:
init_image = torch.zeros(
(1, 3, resolution, resolution), device=pipe.device
).to(dtype=torch.float32)
with torch.no_grad():
result_image = pipe(
qr_code_condition=qrcode_image,
prompt=prompt,
negative_prompt=negative_prompt,
init_image=init_image,
strength=strength,
guidance_scale=guidance_scale,
controlnet_conditioning_scale=controlnet_conditioning_scale,
disable_progress_bar=True,
seed=generator,
)
result_image = (
result_image.clamp(-1, 1).squeeze().permute(1, 2, 0).numpy() * 255
)
result_image = Image.fromarray(result_image.astype("uint8"))
return result_image
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