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diff --git a/anydoor/run_inference copy.py b/anydoor/run_inference copy.py deleted file mode 100644 index 4acf5dc788057d0b5382044b1be2793ae1802c5c..0000000000000000000000000000000000000000 --- a/anydoor/run_inference copy.py +++ /dev/null @@ -1,294 +0,0 @@ -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 = False -disable_verbosity() -if save_memory: - enable_sliced_attention() - - -config = OmegaConf.load('./configs/inference.yaml') -model_ckpt = config.pretrained_model -model_config = config.config_file - -model = create_model(model_config ).cpu() -model.load_state_dict(load_state_dict(model_ckpt, location='cuda')) -model = model.cuda() -ddim_sampler = DDIMSampler(model) - - - -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 = 5.0): - 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 = 50 #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 = -1 #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 - - -if __name__ == '__main__': - ''' - # ==== Example for inferring a single image === - reference_image_path = './examples/TestDreamBooth/FG/01.png' - bg_image_path = './examples/TestDreamBooth/BG/000000309203_GT.png' - bg_mask_path = './examples/TestDreamBooth/BG/000000309203_mask.png' - save_path = './examples/TestDreamBooth/GEN/gen_res.png' - - # reference image + reference mask - # You could use the demo of SAM to extract RGB-A image with masks - # https://segment-anything.com/demo - image = cv2.imread( reference_image_path, cv2.IMREAD_UNCHANGED) - mask = (image[:,:,-1] > 128).astype(np.uint8) - image = image[:,:,:-1] - image = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB) - ref_image = image - ref_mask = mask - - # background image - back_image = cv2.imread(bg_image_path).astype(np.uint8) - back_image = cv2.cvtColor(back_image, cv2.COLOR_BGR2RGB) - - # background mask - tar_mask = cv2.imread(bg_mask_path)[:,:,0] > 128 - tar_mask = tar_mask.astype(np.uint8) - - gen_image = inference_single_image(ref_image, ref_mask, back_image.copy(), tar_mask) - h,w = back_image.shape[0], back_image.shape[0] - ref_image = cv2.resize(ref_image, (w,h)) - vis_image = cv2.hconcat([ref_image, back_image, gen_image]) - - cv2.imwrite(save_path, vis_image [:,:,::-1]) - ''' - #''' - # ==== Example for inferring VITON-HD Test dataset === - - from omegaconf import OmegaConf - import os - DConf = OmegaConf.load('./configs/datasets.yaml') - save_dir = '../VITONGEN' - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - test_dir = DConf.Test.VitonHDTest.image_dir - image_names = os.listdir(test_dir) - - for image_name in image_names: - ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth/','/cloth-mask/') - tar_mask_path = ref_image_path.replace('/cloth/', '/image-parse-v3/').replace('.jpg','.png') - - ref_image = cv2.imread(ref_image_path) - ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) - - gt_image = cv2.imread(tar_image_path) - gt_image = cv2.cvtColor(gt_image, cv2.COLOR_BGR2RGB) - - ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] - - tar_mask = Image.open(tar_mask_path ).convert('P') - tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 5 - - gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = os.path.join(save_dir, image_name) - - vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) - cv2.imwrite(gen_path, vis_image[:,:,::-1]) - #''' - - - diff --git a/anydoor/run_inference.py b/anydoor/run_inference.py index 4acf5dc788057d0b5382044b1be2793ae1802c5c..ddaaa9640a41e5b727c36de764f12dd4901a20ba 100644 --- a/anydoor/run_inference.py +++ b/anydoor/run_inference.py @@ -258,14 +258,14 @@ if __name__ == '__main__': from omegaconf import OmegaConf import os DConf = OmegaConf.load('./configs/datasets.yaml') - save_dir = '../VITONGEN' + save_dir = '../INFERRED_TRAINED' if not os.path.exists(save_dir): os.mkdir(save_dir) test_dir = DConf.Test.VitonHDTest.image_dir image_names = os.listdir(test_dir) - for image_name in image_names: + for image_name in image_names[:10]: ref_image_path = os.path.join(test_dir, image_name) tar_image_path = ref_image_path.replace('/cloth/', '/image/') ref_mask_path = ref_image_path.replace('/cloth/','/cloth-mask/') diff --git a/anydoor/run_inference_train copy.py b/anydoor/run_inference_train copy.py deleted file mode 100644 index d6777d2d466c7e216365ab37eddee69d98d07024..0000000000000000000000000000000000000000 --- a/anydoor/run_inference_train copy.py +++ /dev/null @@ -1,319 +0,0 @@ -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 = False -disable_verbosity() -if save_memory: - enable_sliced_attention() - - -config = OmegaConf.load('./configs/inference.yaml') -model_ckpt = config.pretrained_model -model_config = config.config_file - -model = create_model(model_config ).cpu() -model.load_state_dict(load_state_dict(model_ckpt, location='cuda')) -model = model.cuda() -ddim_sampler = DDIMSampler(model) - - - -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 = 5.0): - 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 = 50 #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 = -1 #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 - - -if __name__ == '__main__': - ''' - # ==== Example for inferring a single image === - reference_image_path = './examples/TestDreamBooth/FG/01.png' - bg_image_path = './examples/TestDreamBooth/BG/000000309203_GT.png' - bg_mask_path = './examples/TestDreamBooth/BG/000000309203_mask.png' - save_path = './examples/TestDreamBooth/GEN/gen_res.png' - - # reference image + reference mask - # You could use the demo of SAM to extract RGB-A image with masks - # https://segment-anything.com/demo - image = cv2.imread( reference_image_path, cv2.IMREAD_UNCHANGED) - mask = (image[:,:,-1] > 128).astype(np.uint8) - image = image[:,:,:-1] - image = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB) - ref_image = image - ref_mask = mask - - # background image - back_image = cv2.imread(bg_image_path).astype(np.uint8) - back_image = cv2.cvtColor(back_image, cv2.COLOR_BGR2RGB) - - # background mask - tar_mask = cv2.imread(bg_mask_path)[:,:,0] > 128 - tar_mask = tar_mask.astype(np.uint8) - - gen_image = inference_single_image(ref_image, ref_mask, back_image.copy(), tar_mask) - h,w = back_image.shape[0], back_image.shape[0] - ref_image = cv2.resize(ref_image, (w,h)) - vis_image = cv2.hconcat([ref_image, back_image, gen_image]) - - cv2.imwrite(save_path, vis_image [:,:,::-1]) - ''' - #''' - # ==== Example for inferring VITON-HD Test dataset === - - from omegaconf import OmegaConf - import os - DConf = OmegaConf.load('./configs/datasets.yaml') - save_dir = '/work/wefa-door/INFERRED_TRAIN' - - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - test_dir = '/work/wefa-door/cloth_train_inf' - image_names = os.listdir(test_dir) - - for image_name in image_names: - ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_train_inf/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_train_inf/','/cloth-mask/') - tar_mask_path = ref_image_path.replace('/cloth_train_inf/', '/image-parse-v3/').replace('.jpg','.png') - - ref_image = cv2.imread(ref_image_path) - ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) - - gt_image = cv2.imread(tar_image_path) - gt_image = cv2.cvtColor(gt_image, cv2.COLOR_BGR2RGB) - - ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] - - tar_mask = Image.open(tar_mask_path ).convert('P') - tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 9 - - gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = os.path.join(save_dir, image_name) - - vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) - cv2.imwrite(gen_path, vis_image[:,:,::-1]) - #''' - - for image_name in image_names: - ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_train_inf/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_train_inf/','/cloth-mask/') - tar_mask_path = ref_image_path.replace('/cloth_train_inf/', '/image-parse-v3/').replace('.jpg','.png') - - ref_image = cv2.imread(ref_image_path) - ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) - - gt_image = cv2.imread(tar_image_path) - gt_image = cv2.cvtColor(gt_image, cv2.COLOR_BGR2RGB) - - ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] - - tar_mask = Image.open(tar_mask_path ).convert('P') - tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 5 - - gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = ogen_path = os.path.join(save_dir, '_t_' + image_name) - - vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) - cv2.imwrite(gen_path, vis_image[:,:,::-1]) - #''' - - diff --git a/anydoor/run_inference_train.py b/anydoor/run_inference_train.py index d6777d2d466c7e216365ab37eddee69d98d07024..18c45875284592f835e115f5ae64b94e30fe7f88 100644 --- a/anydoor/run_inference_train.py +++ b/anydoor/run_inference_train.py @@ -258,19 +258,19 @@ if __name__ == '__main__': from omegaconf import OmegaConf import os DConf = OmegaConf.load('./configs/datasets.yaml') - save_dir = '/work/wefa-door/INFERRED_TRAIN' + save_dir = '../INFERRED_TRAINED_SHUFFLED' if not os.path.exists(save_dir): os.mkdir(save_dir) - test_dir = '/work/wefa-door/cloth_train_inf' + test_dir = '/work/wefa-door-master/wefa-door/cloth_s' image_names = os.listdir(test_dir) - - for image_name in image_names: + # tops + for image_name in image_names[:10]: ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_train_inf/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_train_inf/','/cloth-mask/') - tar_mask_path = ref_image_path.replace('/cloth_train_inf/', '/image-parse-v3/').replace('.jpg','.png') + tar_image_path = ref_image_path.replace('/cloth_s/', '/image/') + ref_mask_path = ref_image_path.replace('/cloth_s/','/cloth-mask_s/') + tar_mask_path = ref_image_path.replace('/cloth_s/', '/image-parse-v3/').replace('.jpg','.png') ref_image = cv2.imread(ref_image_path) ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) @@ -282,20 +282,19 @@ if __name__ == '__main__': tar_mask = Image.open(tar_mask_path ).convert('P') tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 9 + tar_mask = tar_mask == 5 gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = os.path.join(save_dir, image_name) + gen_path = ogen_path = os.path.join(save_dir, '_t_' + image_name) vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) cv2.imwrite(gen_path, vis_image[:,:,::-1]) - #''' - - for image_name in image_names: + # bottoms + for image_name in image_names[:10]: ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_train_inf/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_train_inf/','/cloth-mask/') - tar_mask_path = ref_image_path.replace('/cloth_train_inf/', '/image-parse-v3/').replace('.jpg','.png') + tar_image_path = ref_image_path.replace('/cloth_s/', '/image/') + ref_mask_path = ref_image_path.replace('/cloth_s/','/cloth-mask_s/') + tar_mask_path = ref_image_path.replace('/cloth_s/', '/image-parse-v3/').replace('.jpg','.png') ref_image = cv2.imread(ref_image_path) ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) @@ -307,13 +306,13 @@ if __name__ == '__main__': tar_mask = Image.open(tar_mask_path ).convert('P') tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 5 + tar_mask = tar_mask == 9 gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = ogen_path = os.path.join(save_dir, '_t_' + image_name) + gen_path = os.path.join(save_dir, image_name) vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) cv2.imwrite(gen_path, vis_image[:,:,::-1]) - #''' + diff --git a/anydoor/run_inference_train2.py b/anydoor/run_inference_train2.py deleted file mode 100644 index bd89383fc58223c17f692c5236bc66470f04972b..0000000000000000000000000000000000000000 --- a/anydoor/run_inference_train2.py +++ /dev/null @@ -1,318 +0,0 @@ -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 = False -disable_verbosity() -if save_memory: - enable_sliced_attention() - - -config = OmegaConf.load('./configs/inference.yaml') -model_ckpt = config.pretrained_model -model_config = config.config_file - -model = create_model(model_config ).cpu() -model.load_state_dict(load_state_dict(model_ckpt, location='cuda')) -model = model.cuda() -ddim_sampler = DDIMSampler(model) - - - -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 = 5.0): - 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 = 50 #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 = -1 #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 - - -if __name__ == '__main__': - ''' - # ==== Example for inferring a single image === - reference_image_path = './examples/TestDreamBooth/FG/01.png' - bg_image_path = './examples/TestDreamBooth/BG/000000309203_GT.png' - bg_mask_path = './examples/TestDreamBooth/BG/000000309203_mask.png' - save_path = './examples/TestDreamBooth/GEN/gen_res.png' - - # reference image + reference mask - # You could use the demo of SAM to extract RGB-A image with masks - # https://segment-anything.com/demo - image = cv2.imread( reference_image_path, cv2.IMREAD_UNCHANGED) - mask = (image[:,:,-1] > 128).astype(np.uint8) - image = image[:,:,:-1] - image = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB) - ref_image = image - ref_mask = mask - - # background image - back_image = cv2.imread(bg_image_path).astype(np.uint8) - back_image = cv2.cvtColor(back_image, cv2.COLOR_BGR2RGB) - - # background mask - tar_mask = cv2.imread(bg_mask_path)[:,:,0] > 128 - tar_mask = tar_mask.astype(np.uint8) - - gen_image = inference_single_image(ref_image, ref_mask, back_image.copy(), tar_mask) - h,w = back_image.shape[0], back_image.shape[0] - ref_image = cv2.resize(ref_image, (w,h)) - vis_image = cv2.hconcat([ref_image, back_image, gen_image]) - - cv2.imwrite(save_path, vis_image [:,:,::-1]) - ''' - #''' - # ==== Example for inferring VITON-HD Test dataset === - - from omegaconf import OmegaConf - import os - DConf = OmegaConf.load('./configs/datasets.yaml') - save_dir = '../INFERRED_TRAIN_SHUFFLED1' - - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - test_dir = '/work/wefa-door-master/wefa-door/cloth_s' - image_names = os.listdir(test_dir) - # tops - for image_name in image_names: - ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_s/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_s/','/cloth-mask_s/') - tar_mask_path = ref_image_path.replace('/cloth_s/', '/image-parse-v3/').replace('.jpg','.png') - - ref_image = cv2.imread(ref_image_path) - ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) - - gt_image = cv2.imread(tar_image_path) - gt_image = cv2.cvtColor(gt_image, cv2.COLOR_BGR2RGB) - - ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] - - tar_mask = Image.open(tar_mask_path ).convert('P') - tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 5 - - gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = ogen_path = os.path.join(save_dir, '_t_' + image_name) - - vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) - cv2.imwrite(gen_path, vis_image[:,:,::-1]) - # bottoms - for image_name in image_names: - ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_s/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_s/','/cloth-mask_s/') - tar_mask_path = ref_image_path.replace('/cloth_s/', '/image-parse-v3/').replace('.jpg','.png') - - ref_image = cv2.imread(ref_image_path) - ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) - - gt_image = cv2.imread(tar_image_path) - gt_image = cv2.cvtColor(gt_image, cv2.COLOR_BGR2RGB) - - ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] - - tar_mask = Image.open(tar_mask_path ).convert('P') - tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 9 - - gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = os.path.join(save_dir, image_name) - - vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) - cv2.imwrite(gen_path, vis_image[:,:,::-1]) - - - diff --git a/anydoor/run_inference_train_shuffled.py b/anydoor/run_inference_train_shuffled.py deleted file mode 100644 index 9bc901e8e53333307446b101876c7822f637316d..0000000000000000000000000000000000000000 --- a/anydoor/run_inference_train_shuffled.py +++ /dev/null @@ -1,318 +0,0 @@ -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 = False -disable_verbosity() -if save_memory: - enable_sliced_attention() - - -config = OmegaConf.load('./configs/inference.yaml') -model_ckpt = config.pretrained_model -model_config = config.config_file - -model = create_model(model_config ).cpu() -model.load_state_dict(load_state_dict(model_ckpt, location='cuda')) -model = model.cuda() -ddim_sampler = DDIMSampler(model) - - - -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 = 5.0): - 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 = 50 #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 = -1 #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 - - -if __name__ == '__main__': - ''' - # ==== Example for inferring a single image === - reference_image_path = './examples/TestDreamBooth/FG/01.png' - bg_image_path = './examples/TestDreamBooth/BG/000000309203_GT.png' - bg_mask_path = './examples/TestDreamBooth/BG/000000309203_mask.png' - save_path = './examples/TestDreamBooth/GEN/gen_res.png' - - # reference image + reference mask - # You could use the demo of SAM to extract RGB-A image with masks - # https://segment-anything.com/demo - image = cv2.imread( reference_image_path, cv2.IMREAD_UNCHANGED) - mask = (image[:,:,-1] > 128).astype(np.uint8) - image = image[:,:,:-1] - image = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2RGB) - ref_image = image - ref_mask = mask - - # background image - back_image = cv2.imread(bg_image_path).astype(np.uint8) - back_image = cv2.cvtColor(back_image, cv2.COLOR_BGR2RGB) - - # background mask - tar_mask = cv2.imread(bg_mask_path)[:,:,0] > 128 - tar_mask = tar_mask.astype(np.uint8) - - gen_image = inference_single_image(ref_image, ref_mask, back_image.copy(), tar_mask) - h,w = back_image.shape[0], back_image.shape[0] - ref_image = cv2.resize(ref_image, (w,h)) - vis_image = cv2.hconcat([ref_image, back_image, gen_image]) - - cv2.imwrite(save_path, vis_image [:,:,::-1]) - ''' - #''' - # ==== Example for inferring VITON-HD Test dataset === - - from omegaconf import OmegaConf - import os - DConf = OmegaConf.load('./configs/datasets.yaml') - save_dir = '/work/wefa-door/INFERRED_TRAIN_SHUFFLED' - - if not os.path.exists(save_dir): - os.mkdir(save_dir) - - test_dir = '/work/wefa-door/cloth_s' - image_names = os.listdir(test_dir) - # tops - for image_name in image_names: - ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_s/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_s/','/cloth-mask_s/') - tar_mask_path = ref_image_path.replace('/cloth_s/', '/image-parse-v3/').replace('.jpg','.png') - - ref_image = cv2.imread(ref_image_path) - ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) - - gt_image = cv2.imread(tar_image_path) - gt_image = cv2.cvtColor(gt_image, cv2.COLOR_BGR2RGB) - - ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] - - tar_mask = Image.open(tar_mask_path ).convert('P') - tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 5 - - gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = ogen_path = os.path.join(save_dir, '_t_' + image_name) - - vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) - cv2.imwrite(gen_path, vis_image[:,:,::-1]) - # bottoms - for image_name in image_names: - ref_image_path = os.path.join(test_dir, image_name) - tar_image_path = ref_image_path.replace('/cloth_s/', '/image/') - ref_mask_path = ref_image_path.replace('/cloth_s/','/cloth-mask_s/') - tar_mask_path = ref_image_path.replace('/cloth_s/', '/image-parse-v3/').replace('.jpg','.png') - - ref_image = cv2.imread(ref_image_path) - ref_image = cv2.cvtColor(ref_image, cv2.COLOR_BGR2RGB) - - gt_image = cv2.imread(tar_image_path) - gt_image = cv2.cvtColor(gt_image, cv2.COLOR_BGR2RGB) - - ref_mask = (cv2.imread(ref_mask_path) > 128).astype(np.uint8)[:,:,0] - - tar_mask = Image.open(tar_mask_path ).convert('P') - tar_mask= np.array(tar_mask) - tar_mask = tar_mask == 9 - - gen_image = inference_single_image(ref_image, ref_mask, gt_image.copy(), tar_mask) - gen_path = os.path.join(save_dir, image_name) - - vis_image = cv2.hconcat([ref_image, gt_image, gen_image]) - cv2.imwrite(gen_path, vis_image[:,:,::-1]) - - - diff --git a/anydoor/run_train_anydoor.py b/anydoor/run_train_anydoor.py index e71751396f1c12c37c65d430cca6f440bdc8f0ce..f2ef1b9c76f8c5af603432ff729beee3334576e3 100644 --- a/anydoor/run_train_anydoor.py +++ b/anydoor/run_train_anydoor.py @@ -32,7 +32,7 @@ if save_memory: enable_sliced_attention() # Configs -resume_path = '/work/wefa-door-master/adbase-step=37500.ckpt' +resume_path = '/work/wefa-door-master/adbase-step=41250.ckpt' batch_size = 16 logger_freq = 1000 learning_rate = 1e-5 diff --git a/anydoor/run_train_anydoor_infer_each_epoch.py b/anydoor/run_train_anydoor_infer_each_epoch.py deleted file mode 100644 index c8437b237a7e45f2a0152f1f0a919c69bf731ac6..0000000000000000000000000000000000000000 --- a/anydoor/run_train_anydoor_infer_each_epoch.py +++ /dev/null @@ -1,78 +0,0 @@ -import pytorch_lightning as pl -from torch.utils.data import DataLoader -from datasets.vitonhd import VitonHDDataset -from cldm.logger import ImageLogger -from cldm.model import create_model, load_state_dict -from cldm.hack import disable_verbosity, enable_sliced_attention -from omegaconf import OmegaConf -import subprocess -from pytorch_lightning.callbacks import Callback - -class InferenceCallback(Callback): - def on_epoch_end(self, trainer, pl_module): - print(f"Running inference script after epoch {trainer.current_epoch}") - # Define the command to run your Python script - command = ["python", "/work/AnyDoor/run_inference_train.py"] - # Execute the command - result = subprocess.run(command, capture_output=True, text=True) - - # Check if the script ran successfully - if result.returncode == 0: - print("Inference script executed successfully.") - # Optionally, print the script's output - print(result.stdout) - else: - print("Error running inference script:") - print(result.stderr) - -save_memory = False -disable_verbosity() -if save_memory: - enable_sliced_attention() - -# Configs -resume_path = '/work/anydoor.ckpt' -batch_size = 16 -logger_freq = 1000 -learning_rate = 1e-5 -sd_locked = False -only_mid_control = False -n_gpus = 4 -accumulate_grad_batches=1 - -# First use cpu to load models. Pytorch Lightning will automatically move it to GPUs. -model = create_model('./configs/anydoor.yaml').cpu() -model.load_state_dict(load_state_dict(resume_path, location='cpu')) -model.learning_rate = learning_rate -model.sd_locked = sd_locked -model.only_mid_control = only_mid_control - -# Datasets -DConf = OmegaConf.load('./configs/datasets.yaml') -# dataset1 = YoutubeVOSDataset(**DConf.Train.YoutubeVOS) -# dataset2 = SaliencyDataset(**DConf.Train.Saliency) -# dataset3 = VIPSegDataset(**DConf.Train.VIPSeg) -# dataset4 = YoutubeVISDataset(**DConf.Train.YoutubeVIS) -# dataset5 = MVImageNetDataset(**DConf.Train.MVImageNet) -# dataset6 = SAMDataset(**DConf.Train.SAM) -# dataset7 = UVODataset(**DConf.Train.UVO.train) -dataset8 = VitonHDDataset(**DConf.Train.VitonHD) -# dataset9 = UVOValDataset(**DConf.Train.UVO.val) -# dataset10 = MoseDataset(**DConf.Train.Mose) -# dataset11 = FashionTryonDataset(**DConf.Train.FashionTryon) -# dataset12 = LvisDataset(**DConf.Train.Lvis) - -# image_data = 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