Delete main_code_script.py

main_code_script.py

@@ -1,200 +0,0 @@
-# Install necessary libraries (in your requirements.txt)
-# pillow opencv-python transformers mediapipe diffusers accelerate transformers
-# Example install command: pip install pillow opencv-python transformers mediapipe diffusers accelerate transformers
-from PIL import Image
-import cv2
-import mediapipe as mp
-import numpy as np
-from transformers import pipeline, CLIPImageProcessor, CLIPVisionModelWithProjection, CLIPTextModel, CLIPTextModelWithProjection, AutoTokenizer
-from diffusers import StableDiffusionXLInpaintPipeline, DDPMScheduler, AutoencoderKL
-import torch
-import os
-from torchvision import transforms
-from typing import List
-# from utils_mask import get_mask_location
-# 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
-import apply_net
-
-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 # [cite: 60, 61]
-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) # [cite: 61]
-tokenizer_one = AutoTokenizer.from_pretrained(
-    base_path,
-    subfolder="tokenizer",
-    revision=None,
-    use_fast=False,
-) # [cite: 61, 62]
-tokenizer_two = AutoTokenizer.from_pretrained(
-    base_path,
-    subfolder="tokenizer_2",
-    revision=None,
-    use_fast=False,
-) # [cite: 62]
-noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler") # [cite: 62]
-text_encoder_one = CLIPTextModel.from_pretrained(
-    base_path,
-    subfolder="text_encoder",
-    torch_dtype=torch.float16,
-) # [cite: 62]
-text_encoder_two = CLIPTextModelWithProjection.from_pretrained(
-    base_path,
-    subfolder="text_encoder_2",
-    torch_dtype=torch.float16,
-) # [cite: 62]
-image_encoder = CLIPVisionModelWithProjection.from_pretrained(
-    base_path,
-    subfolder="image_encoder",
-    torch_dtype=torch.float16,
-) # [cite: 62, 63]
-vae = AutoencoderKL.from_pretrained(base_path,
-                                    subfolder="vae",
-                                    torch_dtype=torch.float16,
-) # [cite: 63]
-# "stabilityai/stable-diffusion-xl-base-1.0",
-UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(
-    base_path,
-    subfolder="unet_encoder",
-    torch_dtype=torch.float16,
-) # [cite: 63]
-# parsing_model = Parsing(0)
-# openpose_model = OpenPose(0)
-UNet_Encoder.requires_grad_(False) # [cite: 63]
-image_encoder.requires_grad_(False) # [cite: 63]
-vae.requires_grad_(False) # [cite: 63]
-unet.requires_grad_(False) # [cite: 63]
-text_encoder_one.requires_grad_(False) # [cite: 63]
-text_encoder_two.requires_grad_(False) # [cite: 63]
-tensor_transfrom = transforms.Compose(
-    [
-        transforms.ToTensor(),
-        transforms.Normalize(,),
-    ]
-) # [cite: 63, 64]
-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,
-) # [cite: 64, 65]
-pipe.unet_encoder = UNet_Encoder # [cite: 65]
-@spaces.GPU
-def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed):
-    device = "cuda"
-    # openpose_model.preprocessor.body_estimation.model.to(device)
-    pipe.to(device)
-    pipe.unet_encoder.to(device)
-    garm_img = garm_img.convert("RGB").resize((768, 1024))
-    human_img_orig = dict["background"].convert("RGB")
-    if is_checked_crop:
-        width, height = human_img_orig.size
-        target_width = int(min(width, height * (3 / 4)))
-        target_height = int(min(height, width * (4 / 3)))
-        left = (width - target_width) / 2
-        top = (height - target_height) / 2
-        right = (width + target_width) / 2
-        bottom = (height + target_height) / 2
-        cropped_img = human_img_orig.crop((left, top, right, bottom))
-        crop_size = cropped_img.size
-        human_img = cropped_img.resize((384, 512))  # Reduced size for efficiency
-    else:
-        human_img = human_img_orig.resize((384, 512))  # Reduced size for efficiency
-    if is_checked:
-        # 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))
-        # Placeholder for mask generation (replace with your mask logic)
-        mask = Image.new('L', (768, 1024), color='white')  # Example: a white mask
-        mask_gray = Image.new('RGB', (768, 1024), color='gray') # Example: a gray image
-    else:
-        mask = pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
-    mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
-    mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)
-    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))
-    with torch.no_grad():
-        # Extract the images
-        with torch.cuda.amp.autocast():
-            with torch.no_grad():
-                prompt = "model is wearing " + garment_des
-                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 " + garment_des
-                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, # [cite: 78, 79, 80]
-                    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_