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import torch
import gradio as gr
from PIL import Image
import qrcode
from pathlib import Path
from diffusers import (
StableDiffusionPipeline,
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
DDIMScheduler,
DPMSolverMultistepScheduler,
)
from PIL import Image
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
)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16,
)
pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
sd_pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16
)
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to("cuda")
sd_pipe.enable_xformers_memory_efficient_attention()
sd_pipe.enable_model_cpu_offload()
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
def inference(
init_image: Image.Image,
qrcode_image: Image.Image,
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,
num_inference_steps: int = 30,
):
print(init_image, qrcode_image, qr_code_content, prompt, negative_prompt)
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")
generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
if init_image is None:
print("Generating random image from prompt using Stable Diffusion")
# generate image from prompt
out = sd_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
generator=generator,
num_inference_steps=25,
num_images_per_prompt=1,
) # type: ignore
init_image = out.images[0]
else:
print("Using provided init image")
init_image = resize_for_condition_image(init_image, 768)
if 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)
out = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=init_image,
control_image=qrcode_image, # type: ignore
width=768, # type: ignore
height=768, # type: ignore
guidance_scale=float(guidance_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] # type: ignore
with gr.Blocks() as blocks:
gr.Markdown(
"""
# AI QR Code Generator
model: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15
<a href="https://huggingface.co/spaces/huggingface-projects/AI-QR-code-generator?duplicate=true" style="display: inline-block;margin-top: .5em;margin-right: .25em;" target="_blank">
<img style="margin-bottom: 0em;display: inline;margin-top: -.25em;" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a> for no queue on your own hardware.</p>
"""
)
with gr.Row():
with gr.Column():
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"):
gr.Markdown(
"**Note: The QR Code Image functionality is highly dependent on the params below.**"
)
guidance_scale = gr.Slider(
minimum=0.0,
maximum=50.0,
step=0.01,
value=10.0,
label="Guidance Scale",
)
controlnet_conditioning_scale = gr.Slider(
minimum=0.0,
maximum=5.0,
step=0.01,
value=2.0,
label="Controlnet Conditioning Scale",
)
strength = gr.Slider(
minimum=0.0, maximum=1.0, step=0.01, value=0.8, label="Strength"
)
seed = gr.Slider(
minimum=-1,
maximum=9999999999,
step=1,
value=2313123,
label="Seed",
randomize=True,
)
run_btn = gr.Button("Run")
with gr.Column():
result_image = gr.Image(label="Result Image")
run_btn.click(
inference,
inputs=[
init_image,
qr_code_image,
qr_code_content,
prompt,
negative_prompt,
guidance_scale,
controlnet_conditioning_scale,
strength,
seed,
],
outputs=[result_image],
)
gr.Examples(
examples=[
[
"./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/spaces/huggingface-projects/AI-QR-code-generator",
"beautiful sunset in San Francisco with Golden Gate bridge in the background",
"ugly, disfigured, low quality, blurry, nsfw",
10.0,
2.7,
0.8,
7878952477,
],
[
None,
None,
"https://huggingface.co",
"A flying cat over a jungle",
"ugly, disfigured, low quality, blurry, nsfw",
10.0,
2.7,
0.8,
23123124123,
],
],
fn=inference,
inputs=[
init_image,
qr_code_image,
qr_code_content,
prompt,
negative_prompt,
guidance_scale,
controlnet_conditioning_scale,
strength,
seed,
],
outputs=[result_image],
cache_examples=True,
)
blocks.queue()
blocks.launch()