wehub-resource-sync
428 lines
16 KiB
Python
428 lines
16 KiB
Python
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
#
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
import argparse
|
|
import json
|
|
import os
|
|
import re
|
|
import subprocess
|
|
import tarfile
|
|
import warnings
|
|
from dataclasses import dataclass, field
|
|
from typing import List, Optional
|
|
|
|
import pyrallis
|
|
import torch
|
|
from torchvision.utils import save_image
|
|
from tqdm import tqdm
|
|
|
|
warnings.filterwarnings("ignore") # ignore warning
|
|
|
|
import cv2
|
|
from termcolor import colored
|
|
|
|
from diffusion import DPMS
|
|
from diffusion.data.datasets.utils import ASPECT_RATIO_512_TEST, ASPECT_RATIO_1024_TEST
|
|
from diffusion.model.builder import build_model, get_tokenizer_and_text_encoder, get_vae, vae_decode, vae_encode
|
|
from diffusion.model.utils import prepare_prompt_ar
|
|
from diffusion.utils.config import SanaConfig, model_init_config
|
|
from diffusion.utils.logger import get_root_logger
|
|
from tools.controlnet.utils import get_scribble_map, transform_control_signal
|
|
from tools.download import find_model
|
|
|
|
|
|
def set_env(seed=0, latent_size=256):
|
|
torch.manual_seed(seed)
|
|
torch.set_grad_enabled(False)
|
|
for _ in range(30):
|
|
torch.randn(1, 4, latent_size, latent_size)
|
|
|
|
|
|
def get_dict_chunks(data, bs):
|
|
keys = []
|
|
for k in data:
|
|
keys.append(k)
|
|
if len(keys) == bs:
|
|
yield keys
|
|
keys = []
|
|
if keys:
|
|
yield keys
|
|
|
|
|
|
def create_tar(data_path):
|
|
tar_path = f"{data_path}.tar"
|
|
with tarfile.open(tar_path, "w") as tar:
|
|
tar.add(data_path, arcname=os.path.basename(data_path))
|
|
print(f"Created tar file: {tar_path}")
|
|
return tar_path
|
|
|
|
|
|
def delete_directory(exp_name):
|
|
if os.path.exists(exp_name):
|
|
subprocess.run(["rm", "-r", exp_name], check=True)
|
|
print(f"Deleted directory: {exp_name}")
|
|
|
|
|
|
def create_save_root(args, dataset, epoch_name, step_name, sample_steps, guidance_type):
|
|
save_root = os.path.join(
|
|
img_save_dir,
|
|
f"{dataset}_epoch{epoch_name}_step{step_name}_scale{args.cfg_scale}"
|
|
f"_step{sample_steps}_size{args.image_size}_bs{args.bs}_samp{args.sampling_algo}"
|
|
f"_seed{args.seed}_{str(weight_dtype).split('.')[-1]}",
|
|
)
|
|
|
|
if args.pag_scale != 1.0:
|
|
save_root = save_root.replace(f"scale{args.cfg_scale}", f"scale{args.cfg_scale}_pagscale{args.pag_scale}")
|
|
if flow_shift != 1.0:
|
|
save_root += f"_flowshift{flow_shift}"
|
|
if guidance_type != "classifier-free":
|
|
save_root += f"_{guidance_type}"
|
|
if args.interval_guidance[0] != 0 and args.interval_guidance[1] != 1:
|
|
save_root += f"_intervalguidance{args.interval_guidance[0]}{args.interval_guidance[1]}"
|
|
|
|
save_root += f"_imgnums{args.sample_nums}" + args.add_label
|
|
return save_root
|
|
|
|
|
|
def guidance_type_select(default_guidance_type, pag_scale, attn_type):
|
|
guidance_type = default_guidance_type
|
|
if not (pag_scale > 1.0 and attn_type == "linear"):
|
|
logger.info("Setting back to classifier-free")
|
|
guidance_type = "classifier-free"
|
|
return guidance_type
|
|
|
|
|
|
def get_ar_from_ref_image(ref_image_path):
|
|
def reduce_ratio(h, w):
|
|
def gcd(a, b):
|
|
while b:
|
|
a, b = b, a % b
|
|
return a
|
|
|
|
divisor = gcd(h, w)
|
|
return f"{h // divisor}:{w // divisor}"
|
|
|
|
ref_image = cv2.imread(ref_image_path)
|
|
h, w = ref_image.shape[:2]
|
|
return reduce_ratio(h, w)
|
|
|
|
|
|
@torch.inference_mode()
|
|
def visualize(config, args, model, items, bs, sample_steps, cfg_scale, pag_scale=1.0):
|
|
assert bs == 1, "only support batch size 1 currently"
|
|
|
|
if isinstance(items, dict):
|
|
get_chunks = get_dict_chunks
|
|
else:
|
|
from diffusion.data.datasets.utils import get_chunks
|
|
|
|
generator = torch.Generator(device=device).manual_seed(args.seed)
|
|
tqdm_desc = f"{save_root.split('/')[-1]} Using GPU: {args.gpu_id}: {args.start_index}-{args.end_index}"
|
|
for chunk in tqdm(list(get_chunks(items, bs)), desc=tqdm_desc, unit="batch", position=args.gpu_id, leave=True):
|
|
# data prepare
|
|
prompts, hw, ar = (
|
|
[],
|
|
torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(bs, 1),
|
|
torch.tensor([[1.0]], device=device).repeat(bs, 1),
|
|
)
|
|
|
|
if "ref_image_path" in chunk[0]:
|
|
prompt, ref_image_path = chunk[0]["prompt"], chunk[0]["ref_image_path"]
|
|
args.reference_image_path = ref_image_path
|
|
ar = get_ar_from_ref_image(args.reference_image_path)
|
|
else:
|
|
assert "ref_controlmap_path" in chunk[0], "neither ref_image_path nor ref_controlmap_path is provided"
|
|
prompt, ref_controlmap_path = chunk[0]["prompt"], chunk[0]["ref_controlmap_path"]
|
|
args.controlmap_path = ref_controlmap_path
|
|
ar = get_ar_from_ref_image(args.controlmap_path)
|
|
|
|
prompt += f" --ar {ar}"
|
|
prompt_clean, _, hw, ar, custom_hw = prepare_prompt_ar(prompt, base_ratios, device=device, show=False)
|
|
latent_size_h, latent_size_w = (
|
|
(int(hw[0, 0] // config.vae.vae_downsample_rate), int(hw[0, 1] // config.vae.vae_downsample_rate))
|
|
if args.image_size == 1024
|
|
else (latent_size, latent_size)
|
|
)
|
|
prompts.append(prompt_clean.strip())
|
|
|
|
# check exists
|
|
save_file_name = f"{prompts[0]}.jpg"
|
|
save_path = os.path.join(save_root, save_file_name)
|
|
if os.path.exists(save_path):
|
|
# make sure the noise is totally same
|
|
torch.randn(bs, config.vae.vae_latent_dim, latent_size_h, latent_size_w, device=device, generator=generator)
|
|
continue
|
|
|
|
# prepare text feature
|
|
if not config.text_encoder.chi_prompt:
|
|
max_length_all = config.text_encoder.model_max_length
|
|
prompts_all = prompts
|
|
else:
|
|
chi_prompt = "\n".join(config.text_encoder.chi_prompt)
|
|
prompts_all = [chi_prompt + prompt for prompt in prompts]
|
|
num_chi_prompt_tokens = len(tokenizer.encode(chi_prompt))
|
|
max_length_all = (
|
|
num_chi_prompt_tokens + config.text_encoder.model_max_length - 2
|
|
) # magic number 2: [bos], [_]
|
|
|
|
caption_token = tokenizer(
|
|
prompts_all, max_length=max_length_all, padding="max_length", truncation=True, return_tensors="pt"
|
|
).to(device)
|
|
select_index = [0] + list(range(-config.text_encoder.model_max_length + 1, 0))
|
|
caption_embs = text_encoder(caption_token.input_ids, caption_token.attention_mask)[0][:, None][
|
|
:, :, select_index
|
|
]
|
|
emb_masks = caption_token.attention_mask[:, select_index]
|
|
null_y = null_caption_embs.repeat(len(prompts), 1, 1)[:, None]
|
|
|
|
# start sampling
|
|
with torch.no_grad():
|
|
n = len(prompts)
|
|
z = torch.randn(
|
|
n, config.vae.vae_latent_dim, latent_size_h, latent_size_w, device=device, generator=generator
|
|
)
|
|
|
|
if args.reference_image_path is not None:
|
|
input_image = cv2.imread(args.reference_image_path)
|
|
control_signal = get_scribble_map(
|
|
input_image=input_image,
|
|
det="Scribble_HED",
|
|
detect_resolution=int(hw.min()),
|
|
thickness=int(args.thickness),
|
|
)
|
|
control_signal = transform_control_signal(control_signal, hw).to(device).to(weight_dtype)
|
|
else:
|
|
control_signal = transform_control_signal(args.controlmap_path, hw).to(device).to(weight_dtype)
|
|
|
|
control_signal_latent = vae_encode(
|
|
config.vae.vae_type, vae, control_signal, config.vae.sample_posterior, device
|
|
)
|
|
|
|
model_kwargs = dict(
|
|
data_info={"img_hw": hw, "aspect_ratio": ar, "control_signal": control_signal_latent},
|
|
mask=emb_masks,
|
|
)
|
|
if args.sampling_algo == "flow_dpm-solver":
|
|
dpm_solver = DPMS(
|
|
model.forward_with_dpmsolver,
|
|
condition=caption_embs,
|
|
uncondition=null_y,
|
|
guidance_type=guidance_type,
|
|
cfg_scale=cfg_scale,
|
|
pag_scale=pag_scale,
|
|
pag_applied_layers=pag_applied_layers,
|
|
model_type="flow",
|
|
model_kwargs=model_kwargs,
|
|
schedule="FLOW",
|
|
interval_guidance=args.interval_guidance,
|
|
)
|
|
samples = dpm_solver.sample(
|
|
z,
|
|
steps=sample_steps,
|
|
order=2,
|
|
skip_type="time_uniform_flow",
|
|
method="multistep",
|
|
flow_shift=flow_shift,
|
|
)
|
|
else:
|
|
raise ValueError(f"{args.sampling_algo} is not defined")
|
|
|
|
samples = samples.to(weight_dtype)
|
|
samples = vae_decode(config.vae.vae_type, vae, samples)
|
|
torch.cuda.empty_cache()
|
|
|
|
return dict(samples=samples, control_signal=control_signal)
|
|
|
|
|
|
def get_args():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--config", type=str, help="config")
|
|
parser.add_argument("--model_path", default=None, type=str, help="Path to the model file (optional)")
|
|
|
|
return parser.parse_known_args()[0]
|
|
|
|
|
|
@dataclass
|
|
class SanaInference(SanaConfig):
|
|
config: Optional[str] = ""
|
|
model_path: Optional[str] = "output/pretrained_models/Sana_1600M_1024px.pth"
|
|
work_dir: str = "output/inference"
|
|
version: str = "sigma"
|
|
txt_file: str = "asset/samples/samples_mini.txt"
|
|
json_file: Optional[str] = None
|
|
sample_nums: int = 100_000
|
|
bs: int = 1
|
|
cfg_scale: float = 4.5
|
|
pag_scale: float = 1.0
|
|
sampling_algo: str = "flow_dpm-solver"
|
|
seed: int = 0
|
|
dataset: str = "custom_controlnet"
|
|
step: int = -1
|
|
add_label: str = ""
|
|
tar_and_del: bool = False
|
|
exist_time_prefix: str = ""
|
|
gpu_id: int = 0
|
|
start_index: int = 0
|
|
end_index: int = 30_000
|
|
interval_guidance: List[float] = field(default_factory=lambda: [0, 1])
|
|
ablation_selections: Optional[List[float]] = None
|
|
ablation_key: Optional[str] = None
|
|
debug: bool = False
|
|
if_save_dirname: bool = False
|
|
# controlnet
|
|
reference_image_path: Optional[str] = None
|
|
controlmap_path: Optional[str] = None
|
|
thickness: int = 2
|
|
blend_alpha: float = 0.0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
args = get_args()
|
|
config = args = pyrallis.parse(config_class=SanaInference, config_path=args.config)
|
|
|
|
args.image_size = config.model.image_size
|
|
|
|
if args.json_file is None:
|
|
assert (args.reference_image_path is None) != (
|
|
args.controlmap_path is None
|
|
), "only one of reference_image_path/controlmap_path can be None"
|
|
|
|
set_env(args.seed, args.image_size // config.vae.vae_downsample_rate)
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
logger = get_root_logger()
|
|
|
|
# only support fixed latent size currently
|
|
latent_size = args.image_size // config.vae.vae_downsample_rate
|
|
max_sequence_length = config.text_encoder.model_max_length
|
|
pe_interpolation = config.model.pe_interpolation
|
|
micro_condition = config.model.micro_condition
|
|
flow_shift = config.scheduler.flow_shift
|
|
pag_applied_layers = config.model.pag_applied_layers
|
|
guidance_type = "classifier-free_PAG"
|
|
assert (
|
|
isinstance(args.interval_guidance, list)
|
|
and len(args.interval_guidance) == 2
|
|
and args.interval_guidance[0] <= args.interval_guidance[1]
|
|
)
|
|
args.interval_guidance = [max(0, args.interval_guidance[0]), min(1, args.interval_guidance[1])]
|
|
sample_steps_dict = {"flow_dpm-solver": 20, "flow_euler": 28}
|
|
sample_steps = args.step if args.step != -1 else sample_steps_dict[args.sampling_algo]
|
|
if config.model.mixed_precision == "fp16":
|
|
weight_dtype = torch.float16
|
|
elif config.model.mixed_precision == "bf16":
|
|
weight_dtype = torch.bfloat16
|
|
elif config.model.mixed_precision == "fp32":
|
|
weight_dtype = torch.float32
|
|
else:
|
|
raise ValueError(f"weigh precision {config.model.mixed_precision} is not defined")
|
|
logger.info(f"Inference with {weight_dtype}, default guidance_type: {guidance_type}, flow_shift: {flow_shift}")
|
|
|
|
vae = get_vae(config.vae.vae_type, config.vae.vae_pretrained, device).to(weight_dtype)
|
|
tokenizer, text_encoder = get_tokenizer_and_text_encoder(name=config.text_encoder.text_encoder_name, device=device)
|
|
|
|
null_caption_token = tokenizer(
|
|
"", max_length=max_sequence_length, padding="max_length", truncation=True, return_tensors="pt"
|
|
).to(device)
|
|
null_caption_embs = text_encoder(null_caption_token.input_ids, null_caption_token.attention_mask)[0]
|
|
|
|
# model setting
|
|
model_kwargs = model_init_config(config, latent_size=latent_size)
|
|
model = build_model(
|
|
config.model.model, use_fp32_attention=config.model.get("fp32_attention", False), **model_kwargs
|
|
).to(device)
|
|
logger.info(
|
|
f"{model.__class__.__name__}:{config.model.model}, Model Parameters: {sum(p.numel() for p in model.parameters()):,}"
|
|
)
|
|
logger.info("Generating sample from ckpt: %s" % args.model_path)
|
|
state_dict = find_model(args.model_path)
|
|
if "pos_embed" in state_dict["state_dict"]:
|
|
del state_dict["state_dict"]["pos_embed"]
|
|
|
|
missing, unexpected = model.load_state_dict(state_dict["state_dict"], strict=False)
|
|
logger.warning(f"Missing keys: {missing}")
|
|
logger.warning(f"Unexpected keys: {unexpected}")
|
|
model.eval().to(weight_dtype)
|
|
base_ratios = eval(f"ASPECT_RATIO_{args.image_size}_TEST")
|
|
args.sampling_algo = (
|
|
args.sampling_algo
|
|
if ("flow" not in args.model_path or args.sampling_algo == "flow_dpm-solver")
|
|
else "flow_euler"
|
|
)
|
|
|
|
if args.work_dir is None:
|
|
work_dir = (
|
|
f"/{os.path.join(*args.model_path.split('/')[:-2])}"
|
|
if args.model_path.startswith("/")
|
|
else os.path.join(*args.model_path.split("/")[:-2])
|
|
)
|
|
img_save_dir = os.path.join(str(work_dir), "vis")
|
|
else:
|
|
img_save_dir = args.work_dir
|
|
|
|
dict_prompt = args.json_file is not None
|
|
if dict_prompt:
|
|
data_dict = json.load(open(args.json_file))
|
|
items = data_dict
|
|
args.sample_nums = len(items)
|
|
else:
|
|
raise ValueError("json_file is not provided")
|
|
|
|
match = re.search(r".*epoch_(\d+).*step_(\d+).*", args.model_path)
|
|
epoch_name, step_name = match.groups() if match else ("unknown", "unknown")
|
|
|
|
os.umask(0o000)
|
|
os.makedirs(img_save_dir, exist_ok=True)
|
|
logger.info(f"Sampler {args.sampling_algo}")
|
|
|
|
dataset = "MJHQ-30K" if args.json_file and "MJHQ-30K" in args.json_file else args.dataset
|
|
|
|
guidance_type = guidance_type_select(guidance_type, args.pag_scale, config.model.attn_type)
|
|
logger.info(f"Inference with {weight_dtype}, guidance_type: {guidance_type}, flow_shift: {flow_shift}")
|
|
save_root = create_save_root(args, dataset, epoch_name, step_name, sample_steps, guidance_type)
|
|
os.makedirs(save_root, exist_ok=True)
|
|
|
|
if args.debug:
|
|
print(f"debug mode, use fixed items")
|
|
|
|
for idx, item in enumerate(items):
|
|
# args.seed = idx
|
|
results = visualize(
|
|
config=config,
|
|
args=args,
|
|
model=model,
|
|
items=[item],
|
|
bs=args.bs,
|
|
sample_steps=sample_steps,
|
|
cfg_scale=args.cfg_scale,
|
|
pag_scale=args.pag_scale,
|
|
)
|
|
os.umask(0o000)
|
|
sample, control_signal = results["samples"][0], results["control_signal"][0]
|
|
# Blend the mask and image.
|
|
if args.blend_alpha > 0:
|
|
print(f"blend image and mask with alpha: {args.blend_alpha}")
|
|
sample = sample * (1 - args.blend_alpha) + control_signal * args.blend_alpha
|
|
|
|
save_file_name = f"{idx}_{item['prompt'][:100]}.jpg"
|
|
save_path = os.path.join(save_root, save_file_name)
|
|
save_image(sample, save_path, nrow=1, normalize=True, value_range=(-1, 1))
|
|
|
|
print(
|
|
colored(f"Sana inference has finished. Results stored at ", "green"),
|
|
colored(f"{img_save_dir}", attrs=["bold"]),
|
|
".",
|
|
)
|