File size: 4,533 Bytes
3d49622
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
7ea6f18
d957d40
 
 
3d49622
 
 
 
 
 
 
 
 
 
 
 
 
 
d957d40
3d49622
 
 
 
 
d957d40
3d49622
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
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 = "openai/clip-vit-large-patch14"
VAE_PATH = "levihsu/ootd"
UNET_PATH = "levihsu/ootd"
MODEL_PATH = "levihsu/ootd"

class OOTDiffusion:

    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="ootd_hd/checkpoint-36000/unet_garm",
            torch_dtype=torch.float16,
            use_safetensors=True,
        )
        unet_vton = UNetVton2DConditionModel.from_pretrained(
            UNET_PATH,
            subfolder="ootd_hd/checkpoint-36000/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