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import os
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
from huggingface_hub import hf_hub_download
from pytorch_lightning import seed_everything
from utils.resize import resize_image, HWC3
from cldm.model import create_model, load_state_dict
from cldm.ddim_lle import DDIMSampler as DDIMSampler_LLE
from cldm.ddim_hlg import DDIMSampler as DDIMSampler_HLG
from automation_pose_mask.openpose import OpenposeDetector
from automation_pose_mask.auto_mask import MaskDetector
from PIL import Image
from rembg import remove
from utils.config import (
model_yaml,
category_dict,
attribute_dict
)
##########################################
# Download model files from HF Hub
##########################################
MODEL_REPO = "NguyenDinhHieu/EquiFashionModel"
openpose_body_model_path = hf_hub_download(MODEL_REPO, filename="body_pose_model.pth")
openpose_hand_model_path = hf_hub_download(MODEL_REPO, filename="hand_pose_model.pth")
sam_model_path = hf_hub_download(MODEL_REPO, filename="open_clip_pytorch_model.bin")
my_model_path = hf_hub_download(MODEL_REPO, filename="eqf_final.ckpt")
##########################################
# Initialize model on GPU once
##########################################
device = "cuda" if torch.cuda.is_available() else "cpu"
apply_openpose = OpenposeDetector(
body_model_path=openpose_body_model_path,
hand_model_path=openpose_hand_model_path
)
apply_mask = MaskDetector(sam_model_path=sam_model_path)
model = create_model(model_yaml).to(device)
model.load_state_dict(load_state_dict(my_model_path, location=device))
model.eval()
hlg_sampler = DDIMSampler_HLG(model)
lle_sampler = DDIMSampler_LLE(model)
##########################################
# Example images
##########################################
example_path = os.path.join(os.path.dirname(__file__), "preselected_images")
example_image_list = [os.path.join(example_path, x) for x in os.listdir(example_path)]
##########################################
# Utility functions
##########################################
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).astype(np.uint8) * 255
return Image.fromarray(binary_mask)
def add_white_background(image):
image = image.convert("RGBA")
white_bg = Image.new("RGBA", image.size, "WHITE")
white_bg.paste(image, (0, 0), image)
return white_bg.convert("RGB")
##########################################
# HLG PROCESS
##########################################
def hlg_process(hlg_prompt, input_image, category, a_prompt, n_prompt,
num_samples, image_resolution, detect_resolution, ddim_steps,
guess_mode, strength, scale, seed, eta):
with torch.no_grad():
input_image = HWC3(input_image)
detected_map, _ = apply_openpose(resize_image(input_image, detect_resolution))
detected_map = HWC3(detected_map)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(detected_map).float().to(device) / 255.0
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w')
if seed == -1:
seed = random.randint(0, 4294967294)
seed_everything(seed)
cond = {
"c_concat": [control],
"c_crossattn": [model.get_learned_conditioning([hlg_prompt + ', ' + a_prompt] * num_samples)]
}
un_cond = {
"c_concat": None if guess_mode else [control],
"c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]
}
shape = (4, H // 8, W // 8)
model.control_scales = ([strength] * 13)
samples, _ = hlg_sampler.sample(ddim_steps, num_samples, shape, cond,
verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond)
x_samples = model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c')
* 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
results = [Image.fromarray(x_samples[i]) for i in range(num_samples)]
return [add_white_background(remove(img)) for img in results]
##########################################
# LLE PROCESS
##########################################
def lle_process(lle_prompt, dict_img_mask, category, a_prompt, n_prompt,
num_samples, image_resolution, detect_resolution, ddim_steps,
guess_mode, strength, scale, seed, eta, attribute, selection_mode):
input_image = dict_img_mask["background"].convert("RGB")
input_image = HWC3(np.array(input_image))
detected_map, keypoints = apply_openpose(resize_image(input_image, detect_resolution))
detected_map = HWC3(detected_map)
if selection_mode == "Automatically recognize":
mask = apply_mask(resize_image(input_image, detect_resolution), keypoints,
category=category, attribute=attribute, sam_mode=True)
else:
mask = pil_to_binary_mask(dict_img_mask['layers'][0].convert("RGB"))
if mask is not None:
mask = torch.from_numpy(np.array(mask.convert("L"))).float().to(device) / 255.0
mask = mask.unsqueeze(0).unsqueeze(0)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
init_img = torch.from_numpy(img).float().to(device) / 127.0 - 1.0
init_img = einops.rearrange(init_img, 'h w c -> 1 c h w')
init_img = torch.stack([init_img] * num_samples)
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_NEAREST)
control = torch.from_numpy(detected_map).float().to(device) / 255.0
control = torch.stack([control]*num_samples)
control = einops.rearrange(control, 'b h w c -> b c h w')
if seed == -1:
seed = random.randint(0, 4294967294)
seed_everything(seed)
cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([lle_prompt + ', ' + a_prompt] * num_samples)]}
un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
shape = (4, H // 8, W // 8)
samples, _ = lle_sampler.sample(ddim_steps, num_samples, shape, cond,
verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond,
init_img=init_img, mask=mask,
english_attribute=attribute)
x_samples = model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c')
* 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
return [Image.fromarray(x_samples[i]) for i in range(num_samples)]
##########################################
# Send result to attribute editor
##########################################
def result2input(images):
return {"background": images[-1], "layers": None, "composite": None}
##########################################
# FULL UI
##########################################
def create_hfddm():
with gr.Blocks().queue() as app:
category = gr.Radio(list(category_dict.values()), value=list(category_dict.values())[0], label="Clothing Category")
with gr.Row():
with gr.Column():
with gr.Tab("Draft Design"):
hlg_prompt = gr.Textbox(label="High-level design prompt")
hlg_input_image = gr.Image(sources=("upload", "webcam"), type="numpy", value=example_image_list[0], label="Reference pose")
gr.Examples(inputs=hlg_input_image, examples=example_image_list)
hlg_run = gr.Button("Generate")
with gr.Tab("Attribute Editing"):
lle_prompt = gr.Textbox(label="Attribute prompt")
lle_input_image = gr.ImageEditor(sources='upload', type="pil", label="Edit regions", value=example_image_list[0])
gr.Examples(inputs=lle_input_image, examples=example_image_list)
selection_mode = gr.Radio(["Automatically recognize", "User interface"], label="Mask Selection", value="Automatically recognize")
current_tab = {}
lle_run = {}
for tab_elem in attribute_dict.values():
with gr.Tab(tab_elem):
current_tab[tab_elem] = gr.Label(value=tab_elem, visible=False)
lle_run[tab_elem] = gr.Button("Generate")
with gr.Column():
result_gallery = gr.Gallery(label="Result", show_label=False, elem_id="gallery", selected_index=0, interactive=False)
send2llg = gr.Button("Send to Attribute Editing")
with gr.Accordion("Advanced Options", open=False):
num_samples = gr.Slider(label="Images", minimum=1, maximum=4, value=2, step=1)
image_resolution = gr.Slider(label="Resolution", minimum=256, maximum=768, value=512, step=64)
strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
guess_mode = gr.Checkbox(label='Guess Mode', value=False)
detect_resolution = gr.Slider(label="Pose Detection Resolution", minimum=128, maximum=1024, value=512, step=1)
ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=10, step=1, visible=False)
scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
seed = gr.Slider(label="Seed", minimum=-1, maximum=4294967294, value=11, step=1)
eta = gr.Number(label="ETA (DDIM)", value=0.0)
a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed, masterpiece, 8k, white background')
n_prompt = gr.Textbox(label="Negative Prompt", value='worst quality, low quality, bad anatomy, watermark, signature, blurry')
hlg_run.click(fn=hlg_process, inputs=[hlg_prompt, hlg_input_image, category, a_prompt, n_prompt,
num_samples, image_resolution, detect_resolution, ddim_steps,
guess_mode, strength, scale, seed, eta], outputs=[result_gallery])
for tab_elem in attribute_dict.values():
lle_run[tab_elem].click(fn=lle_process, inputs=[lle_prompt, lle_input_image, category, a_prompt, n_prompt,
num_samples, image_resolution, detect_resolution,
ddim_steps, guess_mode, strength, scale, seed, eta,
current_tab[tab_elem], selection_mode], outputs=[result_gallery])
send2llg.click(fn=result2input, inputs=result_gallery, outputs=lle_input_image)
return app
hfddm_block = create_hfddm()
demo = gr.Blocks(title="AI Fashion Design", theme=gr.themes.Monochrome(secondary_hue="orange", neutral_hue="gray")).queue()
with demo:
gr.Markdown("# **AI Fashion Design** 👗")
with gr.Tab("Fashion Design"):
hfddm_block.render()
demo.launch()
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