# 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 time
import warnings
from dataclasses import dataclass, field
from typing import List, Optional
warnings.filterwarnings("ignore") # ignore warning
import pyrallis
import torch
from torchvision.utils import save_image
from tqdm import tqdm
from diffusion import DPMS, FlowEuler, SASolverSampler
from diffusion.data.datasets.utils import (
ASPECT_RATIO_512_TEST,
ASPECT_RATIO_1024_TEST,
ASPECT_RATIO_2048_TEST,
ASPECT_RATIO_4096_TEST,
)
from diffusion.model.builder import build_model, get_tokenizer_and_text_encoder, get_vae, vae_decode
from diffusion.model.utils import get_weight_dtype, prepare_prompt_ar
from diffusion.utils.config import SanaConfig, model_init_config
from diffusion.utils.logger import get_root_logger
# from diffusion.utils.misc import read_config
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}")
@torch.inference_mode()
def visualize(items, bs, sample_steps, cfg_scale, pag_scale=1.0):
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}"
assert bs == 1
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),
)
prompt = data_dict[chunk[0]]["prompt"]
prompts = [
prepare_prompt_ar(prompt, base_ratios, device=device, show=False)[0].strip()
] * args.sample_per_prompt
latent_size_h, latent_size_w = latent_size, latent_size
# check exists
save_file_name = f"{chunk[0]}_0.jpg" # 004971-0071_7.png
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(
len(prompts), config.vae.vae_latent_dim, latent_size, latent_size, device=device, generator=generator
)
continue
# prepare text feature
caption_token = tokenizer(
prompts, max_length=max_sequence_length, padding="max_length", truncation=True, return_tensors="pt"
).to(device)
caption_embs = text_encoder(caption_token.input_ids, caption_token.attention_mask)[0][:, None]
emb_masks, null_y = caption_token.attention_mask, 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,
latent_size,
device=device,
generator=generator,
)
model_kwargs = dict(data_info={"img_hw": hw, "aspect_ratio": ar}, mask=emb_masks)
if args.sampling_algo == "dpm-solver":
dpm_solver = DPMS(
model.forward_with_dpmsolver,
condition=caption_embs,
uncondition=null_y,
cfg_scale=cfg_scale,
model_kwargs=model_kwargs,
)
samples = dpm_solver.sample(
z,
steps=sample_steps,
order=2,
skip_type="time_uniform",
method="multistep",
)
elif args.sampling_algo == "sa-solver":
sa_solver = SASolverSampler(model.forward_with_dpmsolver, device=device)
samples = sa_solver.sample(
S=25,
batch_size=n,
shape=(config.vae.vae_latent_dim, latent_size_h, latent_size_w),
eta=1,
conditioning=caption_embs,
unconditional_conditioning=null_y,
unconditional_guidance_scale=cfg_scale,
model_kwargs=model_kwargs,
)[0]
elif args.sampling_algo == "flow_euler":
flow_solver = FlowEuler(
model, condition=caption_embs, uncondition=null_y, cfg_scale=cfg_scale, model_kwargs=model_kwargs
)
samples = flow_solver.sample(
z,
steps=sample_steps,
)
elif 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(vae_dtype)
samples = vae_decode(config.vae.vae_type, vae, samples)
torch.cuda.empty_cache()
os.umask(0o000)
for i in range(bs):
for j, sample in enumerate(samples):
save_file_name = f"{chunk[i]}_{j}.jpg"
save_path = os.path.join(save_root, save_file_name)
# logger.info(f"Saving path: {save_path}")
save_image(sample, save_path, nrow=1, normalize=True, value_range=(-1, 1))
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, help="config")
return parser.parse_known_args()[0]
@dataclass
class SanaInference(SanaConfig):
config: str = ""
model_path: Optional[str] = field(default=None, metadata={"help": "Path to the model file (optional)"})
txt_file: str = "asset/samples/samples.txt"
json_file: Optional[str] = None
sample_nums: int = 100_000
bs: int = 1
sample_per_prompt: int = 10
cfg_scale: float = 4.5
pag_scale: float = 1.0
sampling_algo: str = field(
default="flow_dpm-solver", metadata={"choices": ["dpm-solver", "sa-solver", "flow_euler", "flow_dpm-solver"]}
)
seed: int = 0
dataset: str = "custom"
step: int = -1
add_label: str = ""
tar_and_del: bool = field(default=False, metadata={"help": "if tar and del the saved dir"})
exist_time_prefix: str = ""
gpu_id: int = 0
custom_image_size: Optional[int] = None
start_index: int = 0
end_index: int = 30_000
interval_guidance: List[float] = field(
default_factory=lambda: [0, 1], metadata={"help": "A list value, like [0, 1.] for use cfg"}
)
ablation_selections: Optional[List[float]] = field(
default=None, metadata={"help": "A list value, like [0, 1.] for ablation"}
)
ablation_key: Optional[str] = field(default=None, metadata={"choices": ["step", "cfg_scale", "pag_scale"]})
if_save_dirname: bool = field(
default=False,
metadata={"help": "if save img save dir name at wor_dir/metrics/tmp_time.time().txt for metric testing"},
)
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.custom_image_size:
args.image_size = args.custom_image_size
print(f"custom_image_size: {args.image_size}")
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 = {"dpm-solver": 20, "sa-solver": 25, "flow_dpm-solver": 20, "flow_euler": 28}
sample_steps = args.step if args.step != -1 else sample_steps_dict[args.sampling_algo]
weight_dtype = get_weight_dtype(config.model.mixed_precision)
logger.info(f"Inference with {weight_dtype}, default guidance_type: {guidance_type}, flow_shift: {flow_shift}")
vae_dtype = get_weight_dtype(config.vae.weight_dtype)
vae = get_vae(config.vae.vae_type, config.vae.vae_pretrained, device).to(vae_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=getattr(config.model, "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()):,}"
)
args.model_path = args.model_path or args.position_model_path
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"
)
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])
)
dict_prompt = args.json_file is not None
if dict_prompt:
data_dict = json.load(open(args.json_file))
items = list(data_dict.keys())
else:
with open(args.txt_file) as f:
items = [item.strip() for item in f.readlines()]
logger.info(f"Eval first {min(args.sample_nums, len(items))}/{len(items)} samples")
items = items[: max(0, args.sample_nums)]
items = items[max(0, args.start_index) : min(len(items), args.end_index)]
match = re.search(r".*epoch_(\d+).*step_(\d+).*", args.model_path)
epoch_name, step_name = match.groups() if match else ("unknown", "unknown")
img_save_dir = os.path.join(str(work_dir), "vis")
os.umask(0o000)
os.makedirs(img_save_dir, exist_ok=True)
logger.info(f"Sampler {args.sampling_algo}")
def create_save_root(args, dataset, epoch_name, step_name, sample_steps, guidance_type):
save_root = os.path.join(
img_save_dir,
# f"{datetime.now().date() if args.exist_time_prefix == '' else args.exist_time_prefix}_"
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
dataset = "MJHQ-30K" if args.json_file and "MJHQ-30K" in args.json_file else args.dataset
if args.ablation_selections and args.ablation_key:
for ablation_factor in args.ablation_selections:
setattr(args, args.ablation_key, eval(ablation_factor))
print(f"Setting {args.ablation_key}={eval(ablation_factor)}")
sample_steps = args.step if args.step != -1 else sample_steps_dict[args.sampling_algo]
guidance_type = guidance_type_select(guidance_type, args.pag_scale, config.model.attn_type)
save_root = create_save_root(args, dataset, epoch_name, step_name, sample_steps, guidance_type)
os.makedirs(save_root, exist_ok=True)
if args.if_save_dirname and args.gpu_id == 0:
# save at work_dir/metrics/tmp_xxx.txt for metrics testing
with open(f"{work_dir}/metrics/tmp_{dataset}_{time.time()}.txt", "w") as f:
print(f"save tmp file at {work_dir}/metrics/tmp_{dataset}_{time.time()}.txt")
f.write(os.path.basename(save_root))
logger.info(f"Inference with {weight_dtype}, guidance_type: {guidance_type}, flow_shift: {flow_shift}")
visualize(items, args.bs, sample_steps, args.cfg_scale, args.pag_scale)
else:
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.if_save_dirname and args.gpu_id == 0:
os.makedirs(f"{work_dir}/metrics", exist_ok=True)
# save at work_dir/metrics/tmp_xxx.txt for metrics testing
with open(f"{work_dir}/metrics/tmp_{dataset}_{time.time()}.txt", "w") as f:
print(f"save tmp file at {work_dir}/metrics/tmp_{dataset}_{time.time()}.txt")
f.write(os.path.basename(save_root))
visualize(items, args.bs, sample_steps, args.cfg_scale, args.pag_scale)
if args.tar_and_del:
create_tar(save_root)
delete_directory(save_root)