|
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 = 30, |
|
): |
|
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, |
|
control_image=qrcode_image, |
|
width=768, |
|
height=768, |
|
guidance_scale=float(guidance_scale), |
|
controlnet_conditioning_scale=float(controlnet_conditioning_scale), |
|
generator=generator, |
|
strength=float(strength), |
|
num_inference_steps=num_inference_steps, |
|
) |
|
return out.images[0] |
|
|
|
|
|
with gr.Blocks() as blocks: |
|
gr.Markdown( |
|
"""# AI QR Code Generator |
|
|
|
model: 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() |
|
|
