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import gradio as gr
import torch
from PIL import Image, ImageDraw, ImageFont, ImageOps, ImageEnhance
from quanto import qfloat8, quantize, freeze
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from diffusers.utils import make_image_grid
atkbold = ImageFont.truetype("Atkinson-Hyperlegible-Bold-102.otf",50)
default_width = 1920
default_height = 1080
default_timesteps = 8
def mask_image_factory(mask_text="ASK FOR\nA SNACK", width=default_width, height=default_height):
img = Image.new("L", (width, height), (0,))
draw = ImageDraw.Draw(img)
draw.multiline_text(
xy=(0,0),
text=mask_text,
fill=(255,),
font=atkbold,
align="center",
spacing=0,
)
cropped = img.crop(img.getbbox())
# Calculate aspect ratios
image_aspect_ratio = width / height
cropped_aspect_ratio = cropped.size[0] / cropped.size[1]
# Determine which dimension of cropped.size is larger
if cropped_aspect_ratio > image_aspect_ratio:
# Calculate new dimensions for padding
new_width = int(cropped.size[1] * image_aspect_ratio)
new_height = cropped.size[1]
else:
new_width = cropped.size[0]
new_height = int(cropped.size[0] / image_aspect_ratio)
# Pad the image to the desired aspect ratio
padded = ImageOps.pad(cropped, (new_width, new_height))
resized = padded.resize((width, height), resample=Image.Resampling.LANCZOS)
return resized
preferred_device = "cuda" if torch.cuda.is_available() else ("mps" if torch.backends.mps.is_available() else "cpu")
# preferred_device = "cpu"
preferred_dtype = torch.float32
controlnet = ControlNetModel.from_pretrained(
"monster-labs/control_v1p_sd15_qrcode_monster",
# "monster-labs/control_v1p_sdxl_qrcode_monster",
subfolder="v2",
torch_dtype=preferred_dtype,
#torch_dtype=unet_preferred_dtype
).to(preferred_device)
#quantize(controlnet, weights=qfloat8)
#freeze(controlnet)
ctlpipe = StableDiffusionControlNetPipeline.from_pretrained(
"SimianLuo/LCM_Dreamshaper_v7",
controlnet=controlnet,
torch_dtype=preferred_dtype,
safety_checker=None,
).to(preferred_device)
#quantize(ctlpipe.unet, weights=qfloat8)
#freeze(ctlpipe.unet)
#quantize(ctlpipe.text_encoder, weights=qfloat8)
#freeze(ctlpipe.text_encoder)
def app(prompt, negative_prompt, mask_text, num_inference_steps, controlnet_conditioning_scale, width, height, seed, count):
all_images = [ctlpipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=mask_image_factory(mask_text=mask_text, width=width, height=height),
num_inference_steps=int(num_inference_steps),
guidance_scale=8.0,
controlnet_conditioning_scale=float(controlnet_conditioning_scale),
generator=torch.manual_seed(int(seed + i)),
height=height,
width=width,
).images[0] for i in range(count)]
if count == 1:
cols = 1
rows = 1
elif count == 2:
cols = 1
rows = 2
else:
cols = 2 if count % 2 == 0 else 1
rows = count // cols
return make_image_grid(all_images, cols=cols, rows=rows)
app("corgis running in the park", "ugly, wrong", "ASK FOR\nA SNACK", 1, 1.0, default_height, default_width, 42, 1)
iface = gr.Interface(
app,
[
gr.Textbox(label="Prompt", value="large happy dogs frolic on the seashore, tall cliffs, sun at the horizon, boulders, tide pools, golden hour"),
gr.Textbox(label="Negative Prompt", value="ugly, wrong"),
gr.Textbox(label="Mask Text", value="ASK FOR\nA SNACK"),
gr.Number(label="Number of Inference Steps", value=default_timesteps, minimum=1, maximum=50, step=1),
gr.Slider(label="ControlNet Conditioning Scale", value=0.6, minimum=-1.0, maximum=2.0, step=0.01),
gr.Number(label="Width", value=default_width, minimum=256, maximum=2048, precision=0),
gr.Number(label="Height", value=default_height, minimum=256, maximum=2048, precision=0),
gr.Number(label="Random Number Seed", value=42, minimum=0, maximum=2**32-1, precision=0),
gr.Radio(label="Number of Images to Generate with Subsequent Consecutive Seeds", choices=[1, 2, 4, 6, 10], value=2),
],
"image",
)
iface.launch()
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