anydoor/run_inference_api_select.py

import cv2
import einops
import numpy as np
import torch
import random
from pytorch_lightning import seed_everything
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler
from cldm.hack import disable_verbosity, enable_sliced_attention
from datasets.data_utils import * 
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import albumentations as A
from omegaconf import OmegaConf
from PIL import Image

save_memory = True
disable_verbosity()
if save_memory:
    enable_sliced_attention()

config = OmegaConf.load('./configs/inference.yaml')
current_model_ckpt = config.pretrained_model
model_config = config.config_file

model = create_model(model_config).cpu()
model.load_state_dict(load_state_dict(current_model_ckpt, location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)

def load_model(new_model_ckpt):
    global model, ddim_sampler, current_model_ckpt
    if new_model_ckpt != current_model_ckpt:
        print(f"Loading new model: {new_model_ckpt}")
        model.load_state_dict(load_state_dict(f'/workspace/train-wefadoor-master/anydoor/lightning_logs/version_1/checkpoints/epoch=1-step=2499.ckpt', location='cuda'))
        # model.load_state_dict(load_state_dict(f'/workspace/300k_wefa_boys_slim/lightning_logs/version_0/checkpoints/{new_model_ckpt}', location='cuda'))
        current_model_ckpt = new_model_ckpt
        print("New model loaded successfully.")
    else:
        print("Same model is already loaded, skipping reload.")

def aug_data_mask(image, mask):
    transform = A.Compose([
        A.HorizontalFlip(p=0.5),
        A.RandomBrightnessContrast(p=0.5),
        ])
    transformed = transform(image=image.astype(np.uint8), mask = mask)
    transformed_image = transformed["image"]
    transformed_mask = transformed["mask"]
    return transformed_image, transformed_mask


def process_pairs(ref_image, ref_mask, tar_image, tar_mask):
    # ========= Reference ===========
    # ref expand 
    ref_box_yyxx = get_bbox_from_mask(ref_mask)

    # ref filter mask 
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)
    masked_ref_image = ref_image * ref_mask_3 + np.ones_like(ref_image) * 255 * (1-ref_mask_3)

    y1,y2,x1,x2 = ref_box_yyxx
    masked_ref_image = masked_ref_image[y1:y2,x1:x2,:]
    ref_mask = ref_mask[y1:y2,x1:x2]


    ratio = np.random.randint(12, 13) / 10
    masked_ref_image, ref_mask = expand_image_mask(masked_ref_image, ref_mask, ratio=ratio)
    ref_mask_3 = np.stack([ref_mask,ref_mask,ref_mask],-1)

    # to square and resize
    masked_ref_image = pad_to_square(masked_ref_image, pad_value = 255, random = False)
    masked_ref_image = cv2.resize(masked_ref_image, (224,224) ).astype(np.uint8)

    ref_mask_3 = pad_to_square(ref_mask_3 * 255, pad_value = 0, random = False)
    ref_mask_3 = cv2.resize(ref_mask_3, (224,224) ).astype(np.uint8)
    ref_mask = ref_mask_3[:,:,0]

    # ref aug 
    masked_ref_image_aug = masked_ref_image #aug_data(masked_ref_image) 

    # collage aug 
    masked_ref_image_compose, ref_mask_compose = masked_ref_image, ref_mask #aug_data_mask(masked_ref_image, ref_mask) 
    masked_ref_image_aug = masked_ref_image_compose.copy()
    ref_mask_3 = np.stack([ref_mask_compose,ref_mask_compose,ref_mask_compose],-1)
    ref_image_collage = sobel(masked_ref_image_compose, ref_mask_compose/255)

    # ========= Target ===========
    tar_box_yyxx = get_bbox_from_mask(tar_mask)
    tar_box_yyxx = expand_bbox(tar_mask, tar_box_yyxx, ratio=[1.1,1.2])

    # crop
    tar_box_yyxx_crop =  expand_bbox(tar_image, tar_box_yyxx, ratio=[1.5, 3])    #1.2 1.6
    tar_box_yyxx_crop = box2squre(tar_image, tar_box_yyxx_crop) # crop box
    y1,y2,x1,x2 = tar_box_yyxx_crop

    cropped_target_image = tar_image[y1:y2,x1:x2,:]
    tar_box_yyxx = box_in_box(tar_box_yyxx, tar_box_yyxx_crop)
    y1,y2,x1,x2 = tar_box_yyxx

    # collage
    ref_image_collage = cv2.resize(ref_image_collage, (x2-x1, y2-y1))
    ref_mask_compose = cv2.resize(ref_mask_compose.astype(np.uint8), (x2-x1, y2-y1))
    ref_mask_compose = (ref_mask_compose > 128).astype(np.uint8)

    collage = cropped_target_image.copy() 
    collage[y1:y2,x1:x2,:] = ref_image_collage

    collage_mask = cropped_target_image.copy() * 0.0
    collage_mask[y1:y2,x1:x2,:] = 1.0

    # the size before pad
    H1, W1 = collage.shape[0], collage.shape[1]
    cropped_target_image = pad_to_square(cropped_target_image, pad_value = 0, random = False).astype(np.uint8)
    collage = pad_to_square(collage, pad_value = 0, random = False).astype(np.uint8)
    collage_mask = pad_to_square(collage_mask, pad_value = -1, random = False).astype(np.uint8)

    # the size after pad
    H2, W2 = collage.shape[0], collage.shape[1]
    cropped_target_image = cv2.resize(cropped_target_image, (512,512)).astype(np.float32)
    collage = cv2.resize(collage, (512,512)).astype(np.float32)
    collage_mask  = (cv2.resize(collage_mask, (512,512)).astype(np.float32) > 0.5).astype(np.float32)

    masked_ref_image_aug = masked_ref_image_aug  / 255 
    cropped_target_image = cropped_target_image / 127.5 - 1.0
    collage = collage / 127.5 - 1.0 
    collage = np.concatenate([collage, collage_mask[:,:,:1]  ] , -1)

    item = dict(ref=masked_ref_image_aug.copy(), jpg=cropped_target_image.copy(), hint=collage.copy(), extra_sizes=np.array([H1, W1, H2, W2]), tar_box_yyxx_crop=np.array( tar_box_yyxx_crop ) ) 
    return item


def crop_back( pred, tar_image,  extra_sizes, tar_box_yyxx_crop):
    H1, W1, H2, W2 = extra_sizes
    y1,y2,x1,x2 = tar_box_yyxx_crop    
    pred = cv2.resize(pred, (W2, H2))
    m = 5 # maigin_pixel

    if W1 == H1:
        tar_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
        return tar_image

    if W1 < W2:
        pad1 = int((W2 - W1) / 2)
        pad2 = W2 - W1 - pad1
        pred = pred[:,pad1: -pad2, :]
    else:
        pad1 = int((H2 - H1) / 2)
        pad2 = H2 - H1 - pad1
        pred = pred[pad1: -pad2, :, :]

    gen_image = tar_image.copy()
    gen_image[y1+m :y2-m, x1+m:x2-m, :] =  pred[m:-m, m:-m]
    return gen_image


def inference_single_image(ref_image, ref_mask, tar_image, tar_mask, guidance_scale, seed, steps):
    item = process_pairs(ref_image, ref_mask, tar_image, tar_mask)
    ref = item['ref'] * 255
    tar = item['jpg'] * 127.5 + 127.5
    hint = item['hint'] * 127.5 + 127.5

    hint_image = hint[:,:,:-1]
    hint_mask = item['hint'][:,:,-1] * 255
    hint_mask = np.stack([hint_mask,hint_mask,hint_mask],-1)
    ref = cv2.resize(ref.astype(np.uint8), (512,512))

    seed = random.randint(0, 65535)
    if save_memory:
        model.low_vram_shift(is_diffusing=False)

    ref = item['ref']
    tar = item['jpg'] 
    hint = item['hint']
    num_samples = 1

    control = torch.from_numpy(hint.copy()).float().cuda() 
    control = torch.stack([control for _ in range(num_samples)], dim=0)
    control = einops.rearrange(control, 'b h w c -> b c h w').clone()


    clip_input = torch.from_numpy(ref.copy()).float().cuda() 
    clip_input = torch.stack([clip_input for _ in range(num_samples)], dim=0)
    clip_input = einops.rearrange(clip_input, 'b h w c -> b c h w').clone()

    guess_mode = False
    H,W = 512,512

    cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning( clip_input )]}
    un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([torch.zeros((1,3,224,224))] * num_samples)]}
    shape = (4, H // 8, W // 8)

    if save_memory:
        model.low_vram_shift(is_diffusing=True)

    # ====
    num_samples = 1 #gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
    image_resolution = 512  #gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
    strength = 1  #gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
    guess_mode = False #gr.Checkbox(label='Guess Mode', value=False)
    #detect_resolution = 512  #gr.Slider(label="Segmentation Resolution", minimum=128, maximum=1024, value=512, step=1)
    ddim_steps = steps #gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
    scale = guidance_scale  #gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
    seed = seed  #gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
    eta = 0.0 #gr.Number(label="eta (DDIM)", value=0.0)

    model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
    samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                    shape, cond, verbose=False, eta=eta,
                                                    unconditional_guidance_scale=scale,
                                                    unconditional_conditioning=un_cond)
    if save_memory:
        model.low_vram_shift(is_diffusing=False)

    x_samples = model.decode_first_stage(samples)
    x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy()#.clip(0, 255).astype(np.uint8)

    result = x_samples[0][:,:,::-1]
    result = np.clip(result,0,255)

    pred = x_samples[0]
    pred = np.clip(pred,0,255)[1:,:,:]
    sizes = item['extra_sizes']
    tar_box_yyxx_crop = item['tar_box_yyxx_crop'] 
    gen_image = crop_back(pred, tar_image, sizes, tar_box_yyxx_crop) 
    return gen_image


import cv2
import numpy as np
import base64
import json
import sys
from io import BytesIO
from PIL import Image

def base64_to_cv2_image(base64_str):
    img_str = base64.b64decode(base64_str)
    np_img = np.frombuffer(img_str, dtype=np.uint8)
    img = cv2.imdecode(np_img, cv2.IMREAD_COLOR)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    return img

def image_to_base64(img):
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
    _, buffer = cv2.imencode('.jpg', img)
    base64_str = base64.b64encode(buffer).decode("utf-8")
    return base64_str

def inference_single_image(ref_image, ref_mask, tar_image, tar_mask, guidance_scale, seed, steps):
    # Replace this with your image processing model function
    # Placeholder operation (e.g., blending images for demonstration)
    np.random.seed(seed)
    output_img = cv2.addWeighted(ref_image, 0.5, tar_image, 0.5, 0)
    return output_img

def process_images(data):
    model_name = data.get('model', 'default_model.ckpt')
    model_ckpt_map = {
        'boys': 'boys.ckpt',
        'men': 'men.ckpt',
        'women': 'women.ckpt',
        'girls': 'girls.ckpt'
    }
    current_model_ckpt = 'default_model.ckpt'
    new_model_ckpt = model_ckpt_map.get(model_name, current_model_ckpt)
    # load_model(new_model_ckpt)  # Load model if needed

    seed = int(data.get('seed', 42))
    steps = int(data.get('steps', 50))
    guidance_scale = float(data.get('guidance_scale', 1.0))

    ref_image = base64_to_cv2_image(data['ref_image'])
    tar_image = base64_to_cv2_image(data['tar_image'])

    ref_mask_img = base64_to_cv2_image(data['ref_mask'])
    ref_mask = cv2.cvtColor(ref_mask_img, cv2.COLOR_RGB2GRAY)
    ref_mask = (ref_mask > 128).astype(np.uint8)

    tar_mask_img = base64_to_cv2_image(data['tar_mask'])
    tar_mask = cv2.cvtColor(tar_mask_img, cv2.COLOR_RGB2GRAY)
    tar_mask = (tar_mask > 128).astype(np.uint8)

    gen_image = inference_single_image(ref_image, ref_mask, tar_image, tar_mask, guidance_scale, seed, steps)
    gen_image_base64 = image_to_base64(gen_image)
    return gen_image_base64

if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: python script.py '<json_data>'")
        sys.exit(1)

    # Read JSON data from command line argument
    json_data = sys.argv[1]
    try:
        data = json.loads(json_data)
        result_image_base64 = process_images(data)
        print(result_image_base64)
    except Exception as e:
        print(f"Error processing images: {e}", file=sys.stderr)