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Running
on
Zero
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() | |