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inference.py
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inference.py
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import os
from typing import overload, Generator, Dict
from argparse import Namespace
import numpy as np
import torch
from PIL import Image
from omegaconf import OmegaConf
from model.cldm import ControlLDM
from model.gaussian_diffusion import Diffusion
from model.bsrnet import RRDBNet
from model.scunet import SCUNet
from model.swinir import SwinIR
from utils.common import instantiate_from_config, load_file_from_url, count_vram_usage
from utils.face_restoration_helper import FaceRestoreHelper
from utils.helpers import (
Pipeline,
BSRNetPipeline, SwinIRPipeline, SCUNetPipeline,
bicubic_resize
)
from utils.cond_fn import MSEGuidance, WeightedMSEGuidance
MODELS = {
### stage_1 model weights
"bsrnet": "https://github.com/cszn/KAIR/releases/download/v1.0/BSRNet.pth",
# the following checkpoint is up-to-date, but we use the old version in our paper
# "swinir_face": "https://github.com/zsyOAOA/DifFace/releases/download/V1.0/General_Face_ffhq512.pth",
"swinir_face": "https://huggingface.co/lxq007/DiffBIR/resolve/main/face_swinir_v1.ckpt",
"scunet_psnr": "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth",
"swinir_general": "https://huggingface.co/lxq007/DiffBIR/resolve/main/general_swinir_v1.ckpt",
### stage_2 model weights
"sd_v21": "https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt",
"v1_face": "https://huggingface.co/lxq007/DiffBIR-v2/resolve/main/v1_face.pth",
"v1_general": "https://huggingface.co/lxq007/DiffBIR-v2/resolve/main/v1_general.pth",
"v2": "https://huggingface.co/lxq007/DiffBIR-v2/resolve/main/v2.pth"
}
def load_model_from_url(url: str) -> Dict[str, torch.Tensor]:
sd_path = load_file_from_url(url, model_dir="weights")
sd = torch.load(sd_path, map_location="cpu")
if "state_dict" in sd:
sd = sd["state_dict"]
if list(sd.keys())[0].startswith("module"):
sd = {k[len("module."):]: v for k, v in sd.items()}
return sd
class InferenceLoop:
def __init__(self, args: Namespace) -> "InferenceLoop":
self.args = args
self.loop_ctx = {}
self.pipeline: Pipeline = None
self.init_stage1_model()
self.init_stage2_model()
self.init_cond_fn()
self.init_pipeline()
@overload
def init_stage1_model(self) -> None:
...
@count_vram_usage
def init_stage2_model(self) -> None:
### load uent, vae, clip
self.cldm: ControlLDM = instantiate_from_config(OmegaConf.load("configs/inference/cldm.yaml"))
sd = load_model_from_url(MODELS["sd_v21"])
unused = self.cldm.load_pretrained_sd(sd)
print(f"strictly load pretrained sd_v2.1, unused weights: {unused}")
### load controlnet
if self.args.version == "v1":
if self.args.task == "fr":
control_sd = load_model_from_url(MODELS["v1_face"])
elif self.args.task == "sr":
control_sd = load_model_from_url(MODELS["v1_general"])
else:
raise ValueError(f"DiffBIR v1 doesn't support task: {self.args.task}, please use v2 by passsing '--version v2'")
else:
control_sd = load_model_from_url(MODELS["v2"])
self.cldm.load_controlnet_from_ckpt(control_sd)
print(f"strictly load controlnet weight")
self.cldm.eval().to(self.args.device)
### load diffusion
self.diffusion: Diffusion = instantiate_from_config(OmegaConf.load("configs/inference/diffusion.yaml"))
self.diffusion.to(self.args.device)
def init_cond_fn(self) -> None:
if not self.args.guidance:
self.cond_fn = None
return
if self.args.g_loss == "mse":
cond_fn_cls = MSEGuidance
elif self.args.g_loss == "w_mse":
cond_fn_cls = WeightedMSEGuidance
else:
raise ValueError(self.args.g_loss)
self.cond_fn = cond_fn_cls(
scale=self.args.g_scale, t_start=self.args.g_start, t_stop=self.args.g_stop,
space=self.args.g_space, repeat=self.args.g_repeat
)
@overload
def init_pipeline(self) -> None:
...
def setup(self) -> None:
self.output_dir = self.args.output
os.makedirs(self.output_dir, exist_ok=True)
def lq_loader(self) -> Generator[np.ndarray, None, None]:
img_exts = [".png", ".jpg", ".jpeg"]
if os.path.isdir(self.args.input):
file_names = sorted([
file_name for file_name in os.listdir(self.args.input) if os.path.splitext(file_name)[-1] in img_exts
])
file_paths = [os.path.join(self.args.input, file_name) for file_name in file_names]
else:
assert os.path.splitext(self.args.input)[-1] in img_exts
file_paths = [self.args.input]
def _loader() -> Generator[np.ndarray, None, None]:
for file_path in file_paths:
### load lq
lq = np.array(Image.open(file_path).convert("RGB"))
print(f"load lq: {file_path}")
### set context for saving results
self.loop_ctx["file_stem"] = os.path.splitext(os.path.basename(file_path))[0]
for i in range(self.args.n_samples):
self.loop_ctx["repeat_idx"] = i
yield lq
return _loader
def after_load_lq(self, lq: np.ndarray) -> np.ndarray:
return lq
@torch.no_grad()
def run(self) -> None:
self.setup()
# We don't support batch processing since input images may have different size
loader = self.lq_loader()
for lq in loader():
lq = self.after_load_lq(lq)
sample = self.pipeline.run(
lq[None], self.args.steps, 1.0, self.args.tiled,
self.args.tile_size, self.args.tile_stride,
self.args.pos_prompt, self.args.neg_prompt, self.args.cfg_scale,
self.args.better_start
)[0]
self.save(sample)
def save(self, sample: np.ndarray) -> None:
file_stem, repeat_idx = self.loop_ctx["file_stem"], self.loop_ctx["repeat_idx"]
file_name = f"{file_stem}_{repeat_idx}.png" if self.args.n_samples > 1 else f"{file_stem}.png"
save_path = os.path.join(self.args.output, file_name)
Image.fromarray(sample).save(save_path)
print(f"save result to {save_path}")
class BSRInferenceLoop(InferenceLoop):
@count_vram_usage
def init_stage1_model(self) -> None:
self.bsrnet: RRDBNet = instantiate_from_config(OmegaConf.load("configs/inference/bsrnet.yaml"))
sd = load_model_from_url(MODELS["bsrnet"])
self.bsrnet.load_state_dict(sd, strict=True)
self.bsrnet.eval().to(self.args.device)
def init_pipeline(self) -> None:
self.pipeline = BSRNetPipeline(self.bsrnet, self.cldm, self.diffusion, self.cond_fn, self.args.device, self.args.upscale)
class BFRInferenceLoop(InferenceLoop):
@count_vram_usage
def init_stage1_model(self) -> None:
self.swinir_face: SwinIR = instantiate_from_config(OmegaConf.load("configs/inference/swinir.yaml"))
sd = load_model_from_url(MODELS["swinir_face"])
self.swinir_face.load_state_dict(sd, strict=True)
self.swinir_face.eval().to(self.args.device)
def init_pipeline(self) -> None:
self.pipeline = SwinIRPipeline(self.swinir_face, self.cldm, self.diffusion, self.cond_fn, self.args.device)
def after_load_lq(self, lq: np.ndarray) -> np.ndarray:
# For BFR task, super resolution is achieved by directly upscaling lq
return bicubic_resize(lq, self.args.upscale)
class BIDInferenceLoop(InferenceLoop):
@count_vram_usage
def init_stage1_model(self) -> None:
self.scunet_psnr: SCUNet = instantiate_from_config(OmegaConf.load("configs/inference/scunet.yaml"))
sd = load_model_from_url(MODELS["scunet_psnr"])
self.scunet_psnr.load_state_dict(sd, strict=True)
self.scunet_psnr.eval().to(self.args.device)
def init_pipeline(self) -> None:
self.pipeline = SCUNetPipeline(self.scunet_psnr, self.cldm, self.diffusion, self.cond_fn, self.args.device)
def after_load_lq(self, lq: np.ndarray) -> np.ndarray:
# For BID task, super resolution is achieved by directly upscaling lq
return bicubic_resize(lq, self.args.upscale)
class V1InferenceLoop(InferenceLoop):
@count_vram_usage
def init_stage1_model(self) -> None:
self.swinir: SwinIR = instantiate_from_config(OmegaConf.load("configs/inference/swinir.yaml"))
if self.args.task == "fr":
sd = load_model_from_url(MODELS["swinir_face"])
elif self.args.task == "sr":
sd = load_model_from_url(MODELS["swinir_general"])
else:
raise ValueError(f"DiffBIR v1 doesn't support task: {self.args.task}, please use v2 by passsing '--version v2'")
self.swinir.load_state_dict(sd, strict=True)
self.swinir.eval().to(self.args.device)
def init_pipeline(self) -> None:
self.pipeline = SwinIRPipeline(self.swinir, self.cldm, self.diffusion, self.cond_fn, self.args.device)
def after_load_lq(self, lq: np.ndarray) -> np.ndarray:
# For BFR task, super resolution is achieved by directly upscaling lq
return bicubic_resize(lq, self.args.upscale)
class UnAlignedBFRInferenceLoop(InferenceLoop):
@count_vram_usage
def init_stage1_model(self) -> None:
self.bsrnet: RRDBNet = instantiate_from_config(OmegaConf.load("configs/inference/bsrnet.yaml"))
sd = load_model_from_url(MODELS["bsrnet"])
self.bsrnet.load_state_dict(sd, strict=True)
self.bsrnet.eval().to(self.args.device)
self.swinir_face: SwinIR = instantiate_from_config(OmegaConf.load("configs/inference/swinir.yaml"))
sd = load_model_from_url(MODELS["swinir_face"])
self.swinir_face.load_state_dict(sd, strict=True)
self.swinir_face.eval().to(self.args.device)
def init_pipeline(self) -> None:
self.pipes = {
"bg": BSRNetPipeline(self.bsrnet, self.cldm, self.diffusion, self.cond_fn, self.args.device, self.args.upscale),
"face": SwinIRPipeline(self.swinir_face, self.cldm, self.diffusion, self.cond_fn, self.args.device)
}
self.pipeline = self.pipes["face"]
def setup(self) -> None:
super().setup()
self.cropped_face_dir = os.path.join(self.args.output, "cropped_faces")
os.makedirs(self.cropped_face_dir, exist_ok=True)
self.restored_face_dir = os.path.join(self.args.output, "restored_faces")
os.makedirs(self.restored_face_dir, exist_ok=True)
self.restored_bg_dir = os.path.join(self.args.output, "restored_backgrounds")
os.makedirs(self.restored_bg_dir, exist_ok=True)
def lq_loader(self) -> Generator[np.ndarray, None, None]:
base_loader = super().lq_loader()
self.face_helper = FaceRestoreHelper(
device=self.args.device,
upscale_factor=1,
face_size=512,
use_parse=True,
det_model="retinaface_resnet50"
)
def _loader() -> Generator[np.ndarray, None, None]:
for lq in base_loader():
### set input image
self.face_helper.clean_all()
upscaled_bg = bicubic_resize(lq, self.args.upscale)
self.face_helper.read_image(upscaled_bg)
### get face landmarks for each face
self.face_helper.get_face_landmarks_5(resize=640, eye_dist_threshold=5)
self.face_helper.align_warp_face()
print(f"detect {len(self.face_helper.cropped_faces)} faces")
### restore each face (has been upscaeled)
for i, lq_face in enumerate(self.face_helper.cropped_faces):
self.loop_ctx["is_face"] = True
self.loop_ctx["face_idx"] = i
self.loop_ctx["cropped_face"] = lq_face
yield lq_face
### restore background (hasn't been upscaled)
self.loop_ctx["is_face"] = False
yield lq
return _loader
def after_load_lq(self, lq: np.ndarray) -> np.ndarray:
if self.loop_ctx["is_face"]:
self.pipeline = self.pipes["face"]
else:
self.pipeline = self.pipes["bg"]
return lq
def save(self, sample: np.ndarray) -> None:
file_stem, repeat_idx = self.loop_ctx["file_stem"], self.loop_ctx["repeat_idx"]
if self.loop_ctx["is_face"]:
face_idx = self.loop_ctx["face_idx"]
file_name = f"{file_stem}_{repeat_idx}_face_{face_idx}.png"
Image.fromarray(sample).save(os.path.join(self.restored_face_dir, file_name))
cropped_face = self.loop_ctx["cropped_face"]
Image.fromarray(cropped_face).save(os.path.join(self.cropped_face_dir, file_name))
self.face_helper.add_restored_face(sample)
else:
self.face_helper.get_inverse_affine()
# paste each restored face to the input image
restored_img = self.face_helper.paste_faces_to_input_image(
upsample_img=sample
)
file_name = f"{file_stem}_{repeat_idx}.png"
Image.fromarray(sample).save(os.path.join(self.restored_bg_dir, file_name))
Image.fromarray(restored_img).save(os.path.join(self.output_dir, file_name))