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predict.py
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predict.py
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# Prediction interface for Cog ⚙️
# https://github.com/replicate/cog/blob/main/docs/python.md
from cog import BasePredictor, Input, Path
import os
import cv2
import time
import torch
import einops
import random
import subprocess
import numpy as np
from cldm.ddim_hacked import DDIMSampler
from cldm.model import create_model, load_state_dict
from cldm.hack import disable_verbosity
from datasets.data_utils import *
from omegaconf import OmegaConf
save_memory = False
MODEL_URL = "https://weights.replicate.delivery/default/ali-vilab/anydoor.tar"
MODEL_CACHE="checkpoints"
def download(url, dest):
start = time.time()
print("downloading url: ", url)
print("downloading to: ", dest)
subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
print("downloading took: ", time.time() - start)
def process_pairs(ref_image, ref_mask, tar_image, tar_mask, max_ratio = 0.8, enable_shape_control = False):
# ========= 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(11, 15) / 10 #11,13
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.astype(np.uint8), (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.astype(np.uint8), (224,224) ).astype(np.uint8)
ref_mask = ref_mask_3[:,:,0]
# collage aug
masked_ref_image_compose, ref_mask_compose = masked_ref_image, ref_mask
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]) #1.1 1.3
tar_box_yyxx_full = tar_box_yyxx
# crop
tar_box_yyxx_crop = expand_bbox(tar_image, tar_box_yyxx, ratio=[1.3, 3.0])
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,:]
cropped_tar_mask = tar_mask[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.astype(np.uint8), (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
if enable_shape_control:
collage_mask = np.stack([cropped_tar_mask,cropped_tar_mask,cropped_tar_mask],-1)
# 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 = 2, 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.astype(np.uint8), (512,512)).astype(np.float32)
collage = cv2.resize(collage.astype(np.uint8), (512,512)).astype(np.float32)
collage_mask = cv2.resize(collage_mask.astype(np.uint8), (512,512), interpolation = cv2.INTER_NEAREST).astype(np.float32)
collage_mask[collage_mask == 2] = -1
masked_ref_image = masked_ref_image / 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.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 ),
tar_box_yyxx=np.array(tar_box_yyxx_full),
)
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
class Predictor(BasePredictor):
def setup(self) -> None:
"""Load the model into memory to make running multiple predictions efficient"""
# if checkpoints folder does not exist, create it
if not os.path.exists(MODEL_CACHE):
download(MODEL_URL, MODEL_CACHE)
disable_verbosity()
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
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'))
self.model = model.cuda()
self.ddim_sampler = DDIMSampler(model)
def inference_single_image(self, ref_image, ref_mask, tar_image, tar_mask, strength, ddim_steps, guidance_scale, seed, enable_shape_control):
item = process_pairs(ref_image, ref_mask, tar_image, tar_mask, enable_shape_control)
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:
self.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": [self.model.get_learned_conditioning( clip_input )]}
un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [self.model.get_learned_conditioning([torch.zeros((1,3,224,224))] * num_samples)]}
shape = (4, H // 8, W // 8)
if save_memory:
self.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)
self.model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)
samples, intermediates = self.ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond)
if save_memory:
self.model.low_vram_shift(is_diffusing=False)
x_samples = self.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()
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
def predict(
self,
reference_image_path: Path = Input(description="Source Image"),
reference_image_mask: Path = Input(description="Source Image"),
bg_image_path: Path = Input(description="Target Image"),
bg_mask_path: Path = Input(description="Target Image mask"),
control_strength: float = Input(description="Control Strength", default=1.0, ge=0.0, le=2.0),
steps: int = Input(description="Steps", default=50, ge=1, le=100),
guidance_scale: float = Input(description="Guidance Scale", default=4.5, ge=0.1, le=30.0),
enable_shape_control: bool = Input(description="Enable Shape Control", default=False),
seed: int = Input(description="Random seed. Leave blank to randomize the seed", default=None),
) -> Path:
"""Run a single prediction on the model"""
if seed is None:
seed = int.from_bytes(os.urandom(4), "big")
print(f"Using seed: {seed}")
save_path = "/tmp/output.png"
image = cv2.imread(str(reference_image_path), cv2.IMREAD_UNCHANGED)
if image.shape[2] == 1:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
elif image.shape[2] == 4:
image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
ref_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
ref_mask = (cv2.imread(str(reference_image_mask))[:,:,-1] > 128).astype(np.uint8)
# background image
back_image = cv2.imread(str(bg_image_path)).astype(np.uint8)
back_image = cv2.cvtColor(back_image, cv2.COLOR_BGR2RGB)
# background mask
tar_mask = cv2.imread(str(bg_mask_path))[:,:,0] > 128
tar_mask = tar_mask.astype(np.uint8)
gen_image = self.inference_single_image(
ref_image,ref_mask, back_image.copy(), tar_mask,
control_strength, steps, guidance_scale, seed, enable_shape_control)
h,w = back_image.shape[0], back_image.shape[0]
ref_image = cv2.resize(ref_image, (w,h))
vis_image = cv2.hconcat([gen_image])
cv2.imwrite(save_path, vis_image [:,:,::-1])
return Path(save_path)