Spaces:
Sleeping
Sleeping
File size: 6,578 Bytes
475e066 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
import torch
from torch import autocast
from diffusers import StableDiffusionInpaintPipeline
import gradio as gr
import traceback
import base64
from io import BytesIO
import os
import PIL
import json
import requests
import logging
import time
import warnings
warnings.filterwarnings("ignore")
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger('looks.studio')
# Model paths
SEGFORMER_MODEL = "mattmdjaga/segformer_b2_clothes"
STABLE_DIFFUSION_MODEL = "stabilityai/stable-diffusion-2-inpainting"
# Global variables for models
parser = None
model = None
inpainter = None
def get_device():
if torch.cuda.is_available():
device = "cuda"
logger.info("Using GPU")
else:
device = "cpu"
logger.info("Using CPU")
return device
def init():
global parser
global model
global inpainter
start_time = time.time()
logger.info("Starting application initialization")
try:
device = get_device()
# Initialize Segformer parser
logger.info("Initializing Segformer parser...")
from parser.segformer_parser import SegformerParser
parser = SegformerParser(SEGFORMER_MODEL)
# Initialize Stable Diffusion model
logger.info("Initializing Stable Diffusion model...")
model = StableDiffusionInpaintPipeline.from_pretrained(
STABLE_DIFFUSION_MODEL,
safety_checker=None,
revision="fp16" if device == "cuda" else None,
torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device)
# Initialize inpainter
logger.info("Initializing inpainter...")
inpainter = ClothingInpainter(model=model, parser=parser)
logger.info(f"Application initialized in {time.time() - start_time:.2f} seconds")
except Exception as e:
logger.error(f"Error initializing application: {str(e)}")
raise e
class ClothingInpainter:
def __init__(self, model_path=None, model=None, parser=None):
self.device = get_device()
if model_path is None and model is None:
raise ValueError('No model provided!')
if model_path is not None:
self.pipe = StableDiffusionInpaintPipeline.from_pretrained(
model_path,
safety_checker=None,
revision="fp16" if self.device == "cuda" else None,
torch_dtype=torch.float16 if self.device == "cuda" else torch.float32
).to(self.device)
else:
self.pipe = model
self.parser = parser
def make_square(self, im, min_size=256, fill_color=(0, 0, 0, 0)):
x, y = im.size
size = max(min_size, x, y)
new_im = PIL.Image.new('RGBA', (size, size), fill_color)
new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))
return new_im.convert('RGB')
def unmake_square(self, init_im, op_im, min_size=256, rs_size=512):
x, y = init_im.size
size = max(min_size, x, y)
factor = rs_size/size
return op_im.crop((int((size-x) * factor / 2), int((size-y) * factor / 2),\
int((size+x) * factor / 2), int((size+y) * factor / 2)))
def inpaint(self, prompt, init_image, parser=None) -> dict:
image = self.make_square(init_image).resize((512,512))
if self.parser is not None:
mask = self.parser.get_image_mask(image)
mask = mask.resize((512,512))
elif parser is not None:
mask = parser.get_image_mask(image)
mask = mask.resize((512,512))
else:
raise ValueError('Image Parser is Missing')
logger.info(f'[generated required mask(s) at {time.time()}]')
# Run the model
guidance_scale=7.5
num_samples = 3
with autocast("cuda"), torch.inference_mode():
images = self.pipe(
num_inference_steps = 50,
prompt=prompt['pos'],
image=image,
mask_image=mask,
guidance_scale=guidance_scale,
num_images_per_prompt=num_samples,
).images
images_output = []
for img in images:
ch = PIL.Image.composite(img,image, mask.convert('L'))
fin_img = self.unmake_square(init_image, ch)
images_output.append(fin_img)
return images_output
def process_image(prompt, image):
start_time = time.time()
logger.info(f"Processing new request - Prompt: {prompt}, Image size: {image.size if image else 'None'}")
try:
if image is None:
logger.error("No image provided")
raise gr.Error("Please upload an image")
if not prompt:
logger.error("No prompt provided")
raise gr.Error("Please enter a prompt")
prompt_dict = {'pos': prompt}
logger.info("Starting inpainting process")
images = inpainter.inpaint(prompt_dict, image)
if not images:
logger.error("Inpainting failed to produce results")
raise gr.Error("Failed to generate images. Please try again.")
logger.info(f"Request processed in {time.time() - start_time:.2f} seconds")
return images
except Exception as e:
logger.error(f"Error processing image: {str(e)}")
raise gr.Error(f"Error processing image: {str(e)}")
# Initialize the model
init()
# Create Gradio interface
with gr.Blocks(title="Looks.Studio - AI Clothing Inpainting") as demo:
gr.Markdown("# Looks.Studio - AI Clothing Inpainting")
gr.Markdown("Upload an image and describe the clothing you want to generate")
with gr.Row():
with gr.Column():
input_image = gr.Image(
type="pil",
label="Upload Image",
height=512
)
prompt = gr.Textbox(label="Describe the clothing you want to generate")
generate_btn = gr.Button("Generate")
with gr.Column():
gallery = gr.Gallery(
label="Generated Images",
show_label=False,
columns=2,
height=512
)
generate_btn.click(
fn=process_image,
inputs=[prompt, input_image],
outputs=gallery
)
if __name__ == "__main__":
demo.launch(share=True) |