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import gradio as gr | |
from gradio.themes.base import Base | |
import spaces | |
from PIL import Image | |
from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline | |
from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref | |
from src.unet_hacked_tryon import UNet2DConditionModel | |
from transformers import ( | |
CLIPImageProcessor, | |
CLIPVisionModelWithProjection, | |
CLIPTextModel, | |
CLIPTextModelWithProjection, | |
) | |
from diffusers import DDPMScheduler,AutoencoderKL | |
from typing import List | |
import ast | |
import webbrowser | |
import torch | |
import os | |
from transformers import AutoTokenizer | |
import numpy as np | |
from utils_mask import get_mask_location | |
from torchvision import transforms | |
import apply_net | |
from preprocess.humanparsing.run_parsing import Parsing | |
from preprocess.openpose.run_openpose import OpenPose | |
from detectron2.data.detection_utils import convert_PIL_to_numpy,_apply_exif_orientation | |
from torchvision.transforms.functional import to_pil_image | |
import amazon_oxy | |
class Seafoam(Base): | |
pass | |
def pil_to_binary_mask(pil_image, threshold=0): | |
np_image = np.array(pil_image) | |
grayscale_image = Image.fromarray(np_image).convert("L") | |
binary_mask = np.array(grayscale_image) > threshold | |
mask = np.zeros(binary_mask.shape, dtype=np.uint8) | |
for i in range(binary_mask.shape[0]): | |
for j in range(binary_mask.shape[1]): | |
if binary_mask[i,j] == True : | |
mask[i,j] = 1 | |
mask = (mask*255).astype(np.uint8) | |
output_mask = Image.fromarray(mask) | |
return output_mask | |
def fetch_products(query): | |
result= amazon_oxy.scrape_amazon(query) | |
values = list(result.values()) | |
imgs=list(result.keys()) | |
pic_and_prices = [] | |
urls = [] | |
i = 0 | |
for price, url in values: | |
pic_and_prices.append((imgs[i], "$"+str(price))) | |
i+=1 | |
urls.append(url) | |
return [pic_and_prices, urls] | |
base_path = 'yisol/IDM-VTON' | |
example_path = os.path.join(os.path.dirname(__file__), 'example') | |
unet = UNet2DConditionModel.from_pretrained( | |
base_path, | |
subfolder="unet", | |
torch_dtype=torch.float16, | |
) | |
unet.requires_grad_(False) | |
tokenizer_one = AutoTokenizer.from_pretrained( | |
base_path, | |
subfolder="tokenizer", | |
revision=None, | |
use_fast=False, | |
) | |
tokenizer_two = AutoTokenizer.from_pretrained( | |
base_path, | |
subfolder="tokenizer_2", | |
revision=None, | |
use_fast=False, | |
) | |
noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler") | |
text_encoder_one = CLIPTextModel.from_pretrained( | |
base_path, | |
subfolder="text_encoder", | |
torch_dtype=torch.float16, | |
) | |
text_encoder_two = CLIPTextModelWithProjection.from_pretrained( | |
base_path, | |
subfolder="text_encoder_2", | |
torch_dtype=torch.float16, | |
) | |
image_encoder = CLIPVisionModelWithProjection.from_pretrained( | |
base_path, | |
subfolder="image_encoder", | |
torch_dtype=torch.float16, | |
) | |
vae = AutoencoderKL.from_pretrained(base_path, | |
subfolder="vae", | |
torch_dtype=torch.float16, | |
) | |
# "stabilityai/stable-diffusion-xl-base-1.0", | |
UNet_Encoder = UNet2DConditionModel_ref.from_pretrained( | |
base_path, | |
subfolder="unet_encoder", | |
torch_dtype=torch.float16, | |
) | |
parsing_model = Parsing(0) | |
openpose_model = OpenPose(0) | |
UNet_Encoder.requires_grad_(False) | |
image_encoder.requires_grad_(False) | |
vae.requires_grad_(False) | |
unet.requires_grad_(False) | |
text_encoder_one.requires_grad_(False) | |
text_encoder_two.requires_grad_(False) | |
tensor_transfrom = transforms.Compose( | |
[ | |
transforms.ToTensor(), | |
transforms.Normalize([0.5], [0.5]), | |
] | |
) | |
pipe = TryonPipeline.from_pretrained( | |
base_path, | |
unet=unet, | |
vae=vae, | |
feature_extractor= CLIPImageProcessor(), | |
text_encoder = text_encoder_one, | |
text_encoder_2 = text_encoder_two, | |
tokenizer = tokenizer_one, | |
tokenizer_2 = tokenizer_two, | |
scheduler = noise_scheduler, | |
image_encoder=image_encoder, | |
torch_dtype=torch.float16, | |
) | |
pipe.unet_encoder = UNet_Encoder | |
def start_tryon(dict,garm_img,garment_des,is_checked,is_checked_crop,denoise_steps,seed): | |
device = "cuda" | |
openpose_model.preprocessor.body_estimation.model.to(device) | |
pipe.to(device) | |
pipe.unet_encoder.to(device) | |
garm_img= garm_img.convert("RGB").resize((768,1024)) | |
human_img_orig = dict["background"].convert("RGB") | |
if is_checked_crop: | |
width, height = human_img_orig.size | |
target_width = int(min(width, height * (3 / 4))) | |
target_height = int(min(height, width * (4 / 3))) | |
left = (width - target_width) / 2 | |
top = (height - target_height) / 2 | |
right = (width + target_width) / 2 | |
bottom = (height + target_height) / 2 | |
cropped_img = human_img_orig.crop((left, top, right, bottom)) | |
crop_size = cropped_img.size | |
human_img = cropped_img.resize((768,1024)) | |
else: | |
human_img = human_img_orig.resize((768,1024)) | |
if is_checked: | |
keypoints = openpose_model(human_img.resize((384,512))) | |
model_parse, _ = parsing_model(human_img.resize((384,512))) | |
mask, mask_gray = get_mask_location('hd', "upper_body", model_parse, keypoints) | |
mask = mask.resize((768,1024)) | |
else: | |
mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024))) | |
# mask = transforms.ToTensor()(mask) | |
# mask = mask.unsqueeze(0) | |
mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img) | |
mask_gray = to_pil_image((mask_gray+1.0)/2.0) | |
human_img_arg = _apply_exif_orientation(human_img.resize((384,512))) | |
human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR") | |
args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda')) | |
# verbosity = getattr(args, "verbosity", None) | |
pose_img = args.func(args,human_img_arg) | |
pose_img = pose_img[:,:,::-1] | |
pose_img = Image.fromarray(pose_img).resize((768,1024)) | |
with torch.no_grad(): | |
# Extract the images | |
with torch.cuda.amp.autocast(): | |
with torch.no_grad(): | |
prompt = "model is wearing " + garment_des | |
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" | |
with torch.inference_mode(): | |
( | |
prompt_embeds, | |
negative_prompt_embeds, | |
pooled_prompt_embeds, | |
negative_pooled_prompt_embeds, | |
) = pipe.encode_prompt( | |
prompt, | |
num_images_per_prompt=1, | |
do_classifier_free_guidance=True, | |
negative_prompt=negative_prompt, | |
) | |
prompt = "a photo of " + garment_des | |
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" | |
if not isinstance(prompt, List): | |
prompt = [prompt] * 1 | |
if not isinstance(negative_prompt, List): | |
negative_prompt = [negative_prompt] * 1 | |
with torch.inference_mode(): | |
( | |
prompt_embeds_c, | |
_, | |
_, | |
_, | |
) = pipe.encode_prompt( | |
prompt, | |
num_images_per_prompt=1, | |
do_classifier_free_guidance=False, | |
negative_prompt=negative_prompt, | |
) | |
pose_img = tensor_transfrom(pose_img).unsqueeze(0).to(device,torch.float16) | |
garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device,torch.float16) | |
generator = torch.Generator(device).manual_seed(seed) if seed is not None else None | |
images = pipe( | |
prompt_embeds=prompt_embeds.to(device,torch.float16), | |
negative_prompt_embeds=negative_prompt_embeds.to(device,torch.float16), | |
pooled_prompt_embeds=pooled_prompt_embeds.to(device,torch.float16), | |
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device,torch.float16), | |
num_inference_steps=denoise_steps, | |
generator=generator, | |
strength = 1.0, | |
pose_img = pose_img.to(device,torch.float16), | |
text_embeds_cloth=prompt_embeds_c.to(device,torch.float16), | |
cloth = garm_tensor.to(device,torch.float16), | |
mask_image=mask, | |
image=human_img, | |
height=1024, | |
width=768, | |
ip_adapter_image = garm_img.resize((768,1024)), | |
guidance_scale=2.0, | |
)[0] | |
if is_checked_crop: | |
out_img = images[0].resize(crop_size) | |
human_img_orig.paste(out_img, (int(left), int(top))) | |
return human_img_orig | |
else: | |
return images[0] | |
# return images[0], mask_gray | |
# Function to handle image selection from the gallery | |
def select_image(images, urls, evt: gr.SelectData): | |
urls = ast.literal_eval(urls) | |
return images[evt.index][0], urls[evt.index] | |
garm_list = os.listdir(os.path.join(example_path,"cloth")) | |
garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list] | |
human_list = os.listdir(os.path.join(example_path,"human")) | |
human_list_path = [os.path.join(example_path,"human",human) for human in human_list] | |
human_ex_list = [] | |
for ex_human in human_list_path: | |
ex_dict= {} | |
ex_dict['background'] = ex_human | |
ex_dict['layers'] = None | |
ex_dict['composite'] = None | |
human_ex_list.append(ex_dict) | |
def open_link(link): | |
print(f"link is {link}") | |
return link | |
##default human | |
seafoam = Seafoam() | |
# Include the CSS file content in your Gradio interface | |
custom_css = """ | |
body, .gradio-container { | |
background-color: #0b0f19; | |
color: black; /* Black text */ | |
} | |
.gr-block { | |
background-color: #00a49c; /* Light green background */ | |
color: black; /* Black text */ | |
padding: 10px; /* Optional: Add some padding for spacing */ | |
border-radius: 5px; /* Optional: Rounded corners for blocks */ | |
} | |
""" | |
image_blocks = gr.Blocks(css=custom_css).queue() | |
with image_blocks as demo: | |
gr.HTML("<center><h1>Sheekify 🛍️👗👕🛒</h1></center>") | |
gr.HTML("<center><p>Upload an image of yourself or select from examples then describe your garment in the text box and wait for the magic. ✨</p></center>") | |
with gr.Row(): | |
with gr.Column(): | |
imgs = gr.ImageEditor(sources='upload', type="pil", label='Image', interactive=True) | |
with gr.Row(): | |
is_checked = gr.Checkbox(label="Yes", info="Use auto-generated mask",value=True) | |
with gr.Row(): | |
is_checked_crop = gr.Checkbox(label="Yes", info="Use auto-crop & resizing",value=False) | |
example = gr.Examples( | |
inputs=imgs, | |
examples_per_page=10, | |
examples=human_ex_list | |
) | |
with gr.Accordion(label="Advanced Settings", open=False): | |
with gr.Row(): | |
denoise_steps = gr.Number(label="Denoising Steps", minimum=5, maximum=40, value=10, step=1) | |
seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42) | |
with gr.Column(): | |
prompt = gr.Textbox(placeholder="Description of garment ex: Yellow Top", show_label=False, elem_id="prompt") | |
fetch_button = gr.Button("Find Products") | |
image_gallery = gr.Gallery(label="Available Products", show_label=True, elem_id="gallery" | |
, columns=[3], rows=[1], object_fit="contain", height="auto", allow_preview= False) | |
url_display = gr.Textbox(label="URLs of Images", interactive=False, visible=False) | |
with gr.Column(): | |
garm_img = gr.Image(label="Garment", sources='upload', type="pil") | |
try_button = gr.Button(value="Try-on") | |
#masked_img = gr.Image(label="Masked image output", elem_id="masked-img",show_share_button=False, visi) | |
image_out = gr.Image(label="Output", elem_id="output-img",show_share_button=False) | |
buy_link = gr.Textbox(label="URL of Selected Image", interactive=False, visible= False) | |
buy_button = gr.Button(value="Like it? Click to buy") | |
output = gr.HTML() | |
fetch_button.click(fn=fetch_products, inputs=prompt, outputs=[image_gallery, url_display]) | |
image_gallery.select(select_image, [image_gallery, url_display], [garm_img, buy_link]) | |
#try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked,is_checked_crop, denoise_steps, seed], outputs=[image_out,masked_img], api_name='tryon') | |
#buy_button.click(fn=None,inputs=buy_link,js=f"(buy_link) => {{ window.open(buy_link.substring(buy_link.indexOf('amazon.com')), '_blank');console.log(buy_link) }}") | |
try_button.click(fn=start_tryon, inputs=[imgs, garm_img, prompt, is_checked,is_checked_crop, denoise_steps, seed], outputs=[image_out], api_name='tryon') | |
buy_button.click(fn=None,inputs=buy_link, js=f'''(buy_link) => {{ | |
const clean_link = buy_link.includes('http') ? buy_link : 'https://' + buy_link; | |
window.open(clean_link, '_blank'); | |
console.log(clean_link); | |
}}''' | |
) | |
image_blocks.launch() |