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# Or save the image
#output_image.save("output_image.jpg")
from os import device_encoding
from diffusers import StableDiffusionInpaintPipeline
from PIL import Image
import torch
import numpy as np
import torch
import gc
from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipelineLegacy, DDIMScheduler, AutoencoderKL
from PIL import Image
#import pose_estimation as pe
import requests
from rembg import remove
from transformers import BlipProcessor, BlipForConditionalGeneration
import sys
import os
import subprocess
sys.path.append(
os.path.join(os.path.dirname(__file__), "huggingface-cloth-segmentation"))
from process import load_seg_model, get_palette, generate_mask
device = 'cpu'
def initialize_and_load_models():
checkpoint_path = 'model/cloth_segm.pth'
net = load_seg_model(checkpoint_path, device=device)
return net
net = initialize_and_load_models()
palette = get_palette(4)
def run(img):
cloth_seg = generate_mask(img, net=net, palette=palette, device=device)
return cloth_seg
def image_caption(image_path, img_type):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
processor = BlipProcessor.from_pretrained("noamrot/FuseCap")
model = BlipForConditionalGeneration.from_pretrained("noamrot/FuseCap").to(device)
raw_image = Image.open(image_path).convert('RGB')
if img_type == "dress":
raw_image = remove(raw_image)
print("bg removed")
raw_image.show
#raw_image = img_np_no_bg
text = "a picture of "
inputs = processor(raw_image, text, return_tensors="pt").to(device)
out = model.generate(**inputs, num_beams = 3)
print(processor.decode(out[0], skip_special_tokens=True))
caption = processor.decode(out[0], skip_special_tokens=True)
return caption
def gen_vton(image_input, dress_input):
# Load the pre-trained model
pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
#revision="fp16", # Or "full" to disable
torch_dtype=torch.float32, # Or torch.float32
)
image_path = image_input
#submodule_path = os.path.join(os.path.dirname(__file__), "huggingface-cloth-segmentation/process.py")
img_open = Image.open(image_path)
#
run(img_open)
gen_mask_1 = "./huggingface-cloth-segmentation/output/alpha/1.png"
gen_mask_2 = "./huggingface-cloth-segmentation/output/alpha/2.png"
gen_mask_3 = "./huggingface-cloth-segmentation/output/alpha/3.png"
print("mask_generated")
if gen_mask_1:
mask_path = gen_mask_1
elif gen_mask_2:
mask_path = gen_mask_2
else:
mask_path = gen_mask_3
dress_path = dress_input
image = Image.open(image_path)
mask = Image.open(mask_path) # Convert mask to grayscale
#image = Image.open("/content/drive/MyDrive/train1/train/image/000025.jpg")
#mask = Image.open("/content/drive/MyDrive/train1/train/image/000014.jpg")# Convert mask to grayscale
#image = download_image(img_url).resize((512, 512))
#mask = download_image(mask_url).resize((512, 512))
#image = Image.open(image_path)
#mask_image = Image.open(mask_path)
image = image.resize((512, 512))
mask = mask.resize((512, 512))
# Define your prompt (text input)
user_caption = image_caption(image_path, "user")
dress_caption = image_caption(dress_path, "dress")
print(user_caption)
print(dress_caption)
prompt = " a human wearing a {dress_caption} "
neg_prompt = "{user_caption}"
# Note: `image` and `mask_image` should be PIL images.
# The mask structure is white for inpainting and black for keeping as is.
# Replace `image` and `mask_image` with your actual images.
guidance_scale=7.5
denoising_strength=0.9
num_samples = 2
generator = torch.Generator(device="cpu") # Explicitly create a CPU generator
images = pipe(
prompt=prompt,
negative_prompt=neg_prompt,
image=image,
mask_image=mask,
guidance_scale=guidance_scale,
denoising_strength=denoising_strength,
generator=generator,
num_images_per_prompt=num_samples,
).images
#Image_1 = pipe(prompt=prompt, image=image, mask_image=mask).images[0]
#images[0] # Display the image
#img = Image.open(images[0])
#img.show()
#img = Image.open(images[1])
#img.show()
#images[2].show
# Or save the image
images[0].save("./processed_images/output_image.jpg")
images[1].save("./processed_images/output_image_1.jpg")
#images[2].save("output_image_2.jpg")
#images[3].save("output_image_3.jpg")
#images[3].save("output_image_4.jpg")
#if app == "__main__":
#gen_vton()
#user_image = "C:/Users/Admin/Downloads/woman.jpg"
#dress_image = "C:/Users/Admin/Downloads/dress1.jpg"
#gen_vton(user_image, dress_image)
def predict(dict, prompt):
image = dict['image'].convert("RGB").resize((512, 512))
mask_image = dict['mask'].convert("RGB").resize((512, 512))
#images = pipe(prompt=prompt, image=image, mask_image=mask_image).images
return(images[0])
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