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import pdb
from pathlib import Path
import sys
PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
sys.path.insert(0, str(PROJECT_ROOT))
import os
import torch
import numpy as np
from PIL import Image
import cv2
import random
import time
import pdb
from pipelines_ootd.pipeline_ootd import OotdPipeline
from pipelines_ootd.unet_garm_2d_condition import UNetGarm2DConditionModel
from pipelines_ootd.unet_vton_2d_condition import UNetVton2DConditionModel
from diffusers import UniPCMultistepScheduler
from diffusers import AutoencoderKL
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoProcessor, CLIPVisionModelWithProjection
from transformers import CLIPTextModel, CLIPTokenizer
VIT_PATH = "../checkpoints/clip-vit-large-patch14"
VAE_PATH = "../checkpoints/ootd"
UNET_PATH = "../checkpoints/ootd/ootd_dc/checkpoint-36000"
MODEL_PATH = "../checkpoints/ootd"
class OOTDiffusionDC:
def __init__(self, gpu_id):
self.gpu_id = 'cuda:' + str(gpu_id)
vae = AutoencoderKL.from_pretrained(
VAE_PATH,
subfolder="vae",
torch_dtype=torch.float16,
)
unet_garm = UNetGarm2DConditionModel.from_pretrained(
UNET_PATH,
subfolder="unet_garm",
torch_dtype=torch.float16,
use_safetensors=True,
)
unet_vton = UNetVton2DConditionModel.from_pretrained(
UNET_PATH,
subfolder="unet_vton",
torch_dtype=torch.float16,
use_safetensors=True,
)
self.pipe = OotdPipeline.from_pretrained(
MODEL_PATH,
unet_garm=unet_garm,
unet_vton=unet_vton,
vae=vae,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True,
safety_checker=None,
requires_safety_checker=False,
).to(self.gpu_id)
self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
self.auto_processor = AutoProcessor.from_pretrained(VIT_PATH)
self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(VIT_PATH).to(self.gpu_id)
self.tokenizer = CLIPTokenizer.from_pretrained(
MODEL_PATH,
subfolder="tokenizer",
)
self.text_encoder = CLIPTextModel.from_pretrained(
MODEL_PATH,
subfolder="text_encoder",
).to(self.gpu_id)
def tokenize_captions(self, captions, max_length):
inputs = self.tokenizer(
captions, max_length=max_length, padding="max_length", truncation=True, return_tensors="pt"
)
return inputs.input_ids
def __call__(self,
model_type='hd',
category='upperbody',
image_garm=None,
image_vton=None,
mask=None,
image_ori=None,
num_samples=1,
num_steps=20,
image_scale=1.0,
seed=-1,
):
if seed == -1:
random.seed(time.time())
seed = random.randint(0, 2147483647)
print('Initial seed: ' + str(seed))
generator = torch.manual_seed(seed)
with torch.no_grad():
prompt_image = self.auto_processor(images=image_garm, return_tensors="pt").to(self.gpu_id)
prompt_image = self.image_encoder(prompt_image.data['pixel_values']).image_embeds
prompt_image = prompt_image.unsqueeze(1)
if model_type == 'hd':
prompt_embeds = self.text_encoder(self.tokenize_captions([""], 2).to(self.gpu_id))[0]
prompt_embeds[:, 1:] = prompt_image[:]
elif model_type == 'dc':
prompt_embeds = self.text_encoder(self.tokenize_captions([category], 3).to(self.gpu_id))[0]
prompt_embeds = torch.cat([prompt_embeds, prompt_image], dim=1)
else:
raise ValueError("model_type must be \'hd\' or \'dc\'!")
images = self.pipe(prompt_embeds=prompt_embeds,
image_garm=image_garm,
image_vton=image_vton,
mask=mask,
image_ori=image_ori,
num_inference_steps=num_steps,
image_guidance_scale=image_scale,
num_images_per_prompt=num_samples,
generator=generator,
).images
return images
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