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"""
This file is used for T2I generation, it also compute the clip similarity between the generated images and the input prompt
"""
from absl import flags
from absl import app
from ml_collections import config_flags
import os
import ml_collections
import torch
from torch import multiprocessing as mp
import torch.nn as nn
import accelerate
import utils
import tempfile
from absl import logging
import builtins
import einops
import math
import numpy as np
import time
from PIL import Image
from diffusion.flow_matching import FlowMatching, ODEFlowMatchingSolver, ODEEulerFlowMatchingSolver
from tools.clip_score import ClipSocre
import libs.autoencoder
from libs.clip import FrozenCLIPEmbedder
from libs.t5 import T5Embedder
def unpreprocess(x):
x = 0.5 * (x + 1.)
x.clamp_(0., 1.)
return x
def get_caption(llm, text_model, _batch_prompt):
_batch_con = _batch_prompt
if llm == "clip":
_latent, _latent_and_others = text_model.encode(_batch_con)
_con = _latent_and_others['token_embedding'].detach()
elif llm == "t5":
_latent, _latent_and_others = text_model.get_text_embeddings(_batch_con)
_con = (_latent_and_others['token_embedding'] * 10.0).detach()
else:
raise NotImplementedError
_con_mask = _latent_and_others['token_mask'].detach()
_batch_token = _latent_and_others['tokens'].detach()
_batch_caption = _batch_con
return (_con, _con_mask, _batch_token, _batch_caption)
def evaluate(config):
if config.get('benchmark', False):
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
mp.set_start_method('spawn')
accelerator = accelerate.Accelerator()
device = accelerator.device
accelerate.utils.set_seed(config.seed, device_specific=True)
logging.info(f'Process {accelerator.process_index} using device: {device}')
config.mixed_precision = accelerator.mixed_precision
config = ml_collections.FrozenConfigDict(config)
if accelerator.is_main_process:
utils.set_logger(log_level='info', fname=config.output_path)
else:
utils.set_logger(log_level='error')
builtins.print = lambda *args: None
nnet = utils.get_nnet(**config.nnet)
nnet = accelerator.prepare(nnet)
logging.info(f'load nnet from {config.nnet_path}')
accelerator.unwrap_model(nnet).load_state_dict(torch.load(config.nnet_path, map_location='cpu'))
nnet.eval()
##
if config.nnet.model_args.clip_dim == 4096:
llm = "t5"
t5 = T5Embedder(device=device)
elif config.nnet.model_args.clip_dim == 768:
llm = "clip"
clip = FrozenCLIPEmbedder()
clip.eval()
clip.to(device)
else:
raise NotImplementedError
if llm == "clip":
context_generator = get_caption(llm, clip, _batch_prompt=[config.prompt]*config.sample.mini_batch_size)
elif llm == "t5":
context_generator = get_caption(llm, t5, _batch_prompt=[config.prompt]*config.sample.mini_batch_size)
else:
raise NotImplementedError
##
autoencoder = libs.autoencoder.get_model(**config.autoencoder)
autoencoder.to(device)
@torch.cuda.amp.autocast()
def encode(_batch):
return autoencoder.encode(_batch)
@torch.cuda.amp.autocast()
def decode(_batch):
return autoencoder.decode(_batch)
bdv_nnet = None # We don't use Autoguidance
ClipSocre_model = ClipSocre(device=device) # we also return clip score
#######
logging.info(config.sample)
logging.info(f'sample: n_samples={config.sample.n_samples}, mode=t2i, mixed_precision={config.mixed_precision}')
def ode_fm_solver_sample(nnet_ema, _n_samples, _sample_steps, bdv_nnet=bdv_nnet, context=None, caption=None, testbatch_img_blurred=None, two_stage_generation=-1, token=None, token_mask=None, return_clipScore=False, ClipSocre_model=None):
with torch.no_grad():
del testbatch_img_blurred
_z_gaussian = torch.randn(_n_samples, *config.z_shape, device=device)
if 'dimr' in config.nnet.name or 'dit' in config.nnet.name:
_z_x0, _mu, _log_var = nnet_ema(context, text_encoder = True, shape = _z_gaussian.shape, mask=token_mask)
_z_init = _z_x0.reshape(_z_gaussian.shape)
else:
raise NotImplementedError
assert config.sample.scale > 1
if config.cfg != -1:
_cfg = config.cfg
else:
_cfg = config.sample.scale
has_null_indicator = hasattr(config.nnet.model_args, "cfg_indicator")
_sample_steps = config.sample.sample_steps
ode_solver = ODEEulerFlowMatchingSolver(nnet_ema, bdv_model_fn=bdv_nnet, step_size_type="step_in_dsigma", guidance_scale=_cfg)
_z, _ = ode_solver.sample(x_T=_z_init, batch_size=_n_samples, sample_steps=_sample_steps, unconditional_guidance_scale=_cfg, has_null_indicator=has_null_indicator)
image_unprocessed = decode(_z)
clip_score = ClipSocre_model.calculate_clip_score(caption, image_unprocessed)
return image_unprocessed, clip_score
def sample_fn(_n_samples, return_caption=False, return_clipScore=False, ClipSocre_model=None, config=None):
_context, _token_mask, _token, _caption = context_generator
assert _context.size(0) == _n_samples
assert return_clipScore
assert not return_caption
return ode_fm_solver_sample(nnet, _n_samples, config.sample.sample_steps, bdv_nnet=bdv_nnet, context=_context, token=_token, token_mask=_token_mask, return_clipScore=return_clipScore, ClipSocre_model=ClipSocre_model, caption=_caption)
with tempfile.TemporaryDirectory() as temp_path:
path = config.img_save_path or config.sample.path or temp_path
if accelerator.is_main_process:
os.makedirs(path, exist_ok=True)
logging.info(f'Samples are saved in {path}')
clip_score_list = utils.sample2dir_wCLIP(accelerator, path, config.sample.n_samples, config.sample.mini_batch_size, sample_fn, unpreprocess, return_clipScore=True, ClipSocre_model=ClipSocre_model, config=config)
if clip_score_list is not None:
_clip_score_list = torch.cat(clip_score_list)
if accelerator.is_main_process:
logging.info(f'nnet_path={config.nnet_path}, clip_score{len(_clip_score_list)}={_clip_score_list.mean().item()}')
FLAGS = flags.FLAGS
config_flags.DEFINE_config_file(
"config", None, "Training configuration.", lock_config=False)
flags.mark_flags_as_required(["config"])
flags.DEFINE_string("nnet_path", None, "The nnet to evaluate.")
flags.DEFINE_string("prompt", None, "The prompt used for generation.")
flags.DEFINE_string("output_path", None, "The path to output log.")
flags.DEFINE_float("cfg", -1, 'cfg scale, will use the scale defined in the config file is not assigned')
flags.DEFINE_string("img_save_path", None, "The path to image log.")
def main(argv):
config = FLAGS.config
config.nnet_path = FLAGS.nnet_path
config.prompt = FLAGS.prompt
config.output_path = FLAGS.output_path
config.img_save_path = FLAGS.img_save_path
config.cfg = FLAGS.cfg
evaluate(config)
if __name__ == "__main__":
app.run(main)
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