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import torch
import gradio as gr
from PIL import Image
from diffusers import (
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
DDIMScheduler,
)
from diffusers.utils import load_image
from PIL import Image
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()
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,
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 = 50,
):
init_image = resize_for_condition_image(init_image, 768)
qrcode_image = resize_for_condition_image(qrcode_image, 768)
generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
out = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=init_image, # type: ignore
control_image=qrcode_image, # type: ignore
width=768, # type: ignore
height=768, # type: ignore
guidance_scale=guidance_scale,
controlnet_conditioning_scale=controlnet_conditioning_scale, # type: ignore
generator=generator,
strength=strength,
num_inference_steps=num_inference_steps,
) # type: ignore
return out.images[0]
with gr.Blocks() as blocks:
gr.Markdown(
"""# AI QR Code Generator
model by: https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15
"""
)
with gr.Row():
with gr.Column():
init_image = gr.Image(label="Init Image", type="pil")
qr_code_image = gr.Image(label="QR Code Image", type="pil")
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(
label="Negative Prompt",
value="ugly, disfigured, low quality, blurry, nsfw",
)
with gr.Accordion(label="Params"):
guidance_scale = gr.Slider(
minimum=0.0,
maximum=50.0,
step=0.1,
value=10.0,
label="Guidance Scale",
)
controlnet_conditioning_scale = gr.Slider(
minimum=0.0,
maximum=5.0,
step=0.1,
value=2.0,
label="Controlnet Conditioning Scale",
)
strength = gr.Slider(
minimum=0.0, maximum=1.0, step=0.1, 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,
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,
]
],
fn=inference,
inputs=[
init_image,
qr_code_image,
prompt,
negative_prompt,
guidance_scale,
controlnet_conditioning_scale,
strength,
seed,
],
outputs=[result_image],
)
blocks.queue()
blocks.launch()
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