File size: 10,832 Bytes
02267af 938e515 1a13129 d568c8b 938e515 d568c8b 938e515 d568c8b 938e515 d568c8b 938e515 6204db3 d568c8b 938e515 d568c8b 595105e 938e515 d568c8b 938e515 d568c8b 938e515 d568c8b 938e515 d568c8b 938e515 d568c8b 6204db3 d568c8b 6204db3 d568c8b 938e515 d568c8b ab2e314 d568c8b 938e515 d568c8b 938e515 d568c8b 595105e d568c8b 595105e 938e515 d568c8b 938e515 d568c8b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 |
import spaces
import gradio as gr
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 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
from PIL import Image, ImageDraw, ImageFont
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
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
progress=gr.Progress()
@spaces.GPU
def infer(person,garment,denoise_steps,seed):
progress(0,desc="Starting")
device = "cuda"
openpose_model.preprocessor.body_estimation.model.to(device)
pipe.to(device)
pipe.unet_encoder.to(device)
personRGB = person.convert("RGB")
crop_size = personRGB.size
human_img = personRGB.resize((768,1024))
garm_img= garment.convert("RGB").resize((768,1024))
progress(0.1,desc="Mask generating")
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))
mask_gray = (1-transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
mask_gray = to_pil_image((mask_gray+1.0)/2.0)
progress(0.3,desc="DensePose processing")
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))
progress(0.5,desc="Image generating")
def callback(pipe, step, timestep, callback_kwargs):
progress_value = 0.5 + ((step+1.0)/denoise_steps)*(0.5/1.0)
progress(progress_value, desc=f"Image generating, {step + 1}/{denoise_steps} steps")
return callback_kwargs
with torch.no_grad():
# Extract the images
with torch.cuda.amp.autocast():
with torch.no_grad():
prompt = "model is wearing clothing"
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 clothing"
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,
callback_on_step_end=callback
)[0]
out_img = images[0].resize(crop_size)
progress(1,desc="Complete")
return out_img
title = "## IDM-VTON"
description = "Virtual Try-on with your image and garment image. Check out the [source codes](https://github.com/yisol/IDM-VTON) and the [model](https://huggingface.co/yisol/IDM-VTON)"
example_path = os.path.join(os.path.dirname(__file__), 'example')
person_list = os.listdir(os.path.join(example_path,"human"))
person_images = [os.path.join(example_path,"human",person) for person in person_list]
garment_list = os.listdir(os.path.join(example_path,"cloth"))
garment_images = [os.path.join(example_path,"cloth",garment) for garment in garment_list]
with gr.Blocks().queue() as demo:
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column():
gr.Markdown("#### Person Image")
person_image = gr.Image(
sources=["upload"],
type="pil",
label="Person Image",
width=512,
height=512,
)
gr.Examples(
inputs=person_image,
examples_per_page=20,
examples=person_images,
)
with gr.Column():
gr.Markdown("#### Garment Image")
garment_image = gr.Image(
sources=["upload"],
type="pil",
label="Garment Image",
width=512,
height=512,
)
gr.Examples(
inputs=garment_image,
examples_per_page=20,
examples=garment_images,
)
with gr.Column():
gr.Markdown("#### Generated Image")
gen_image = gr.Image(
label="Generated Image",
width=512,
height=512,
)
with gr.Row():
gen_button = gr.Button("Generate")
with gr.Accordion("Advanced Options", open=False):
denoise_steps = gr.Number(label="Denoising Steps", minimum=20, maximum=40, value=30, step=1)
seed = gr.Number(label="Seed", minimum=-1, maximum=2147483647, step=1, value=42)
gen_button.click(
fn=infer,
inputs=[person_image, garment_image, denoise_steps, seed],
outputs=[gen_image]
)
demo.launch() |