Spaces:
Sleeping
Sleeping
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
|