nvlabs--sana/scripts/inference_geneval_diffusers.py

# 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 time

import datasets
import numpy as np
import torch
from einops import rearrange
from PIL import Image
from pytorch_lightning import seed_everything
from torchvision.transforms import ToTensor
from torchvision.utils import make_grid
from tqdm import tqdm, trange

from diffusion.utils.logger import get_root_logger

_CITATION = """\
@article{ghosh2024geneval,
  title={Geneval: An object-focused framework for evaluating text-to-image alignment},
  author={Ghosh, Dhruba and Hajishirzi, Hannaneh and Schmidt, Ludwig},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}
"""

_DESCRIPTION = (
    "We demonstrate the advantages of evaluating text-to-image models using existing object detection methods, "
    "to produce a fine-grained instance-level analysis of compositional capabilities."
)


def set_env(seed=0):
    torch.manual_seed(seed)
    torch.set_grad_enabled(False)


@torch.inference_mode()
def visualize():

    tqdm_desc = f"{save_root.split('/')[-1]} Using GPU: {args.gpu_id}: {args.start_index}-{args.end_index}"
    for index, metadata in tqdm(list(enumerate(metadatas)), desc=tqdm_desc, position=args.gpu_id, leave=True):
        metadata["include"] = (
            metadata["include"] if isinstance(metadata["include"], list) else eval(metadata["include"])
        )
        seed_everything(args.seed)
        index += args.start_index

        outpath = os.path.join(save_root, f"{index:0>5}")
        os.makedirs(outpath, exist_ok=True)
        sample_path = os.path.join(outpath, "samples")
        os.makedirs(sample_path, exist_ok=True)

        prompt = metadata["prompt"]
        # print(f"Prompt ({index: >3}/{len(metadatas)}): '{prompt}'")
        with open(os.path.join(outpath, "metadata.jsonl"), "w") as fp:
            json.dump(metadata, fp)

        sample_count = 0

        with torch.no_grad():
            all_samples = list()
            for _ in range((args.n_samples + batch_size - 1) // batch_size):
                #
                # check exists
                save_path = os.path.join(sample_path, f"{sample_count:05}.png")
                if os.path.exists(save_path):
                    continue

                else:
                    # Generate images
                    samples = model(
                        prompt,
                        height=None,
                        width=None,
                        num_inference_steps=50,
                        guidance_scale=9.0,
                        num_images_per_prompt=min(batch_size, args.n_samples - sample_count),
                        negative_prompt=None,
                    ).images
                    for sample in samples:
                        sample.save(os.path.join(sample_path, f"{sample_count:05}.png"))
                        sample_count += 1
                    if not args.skip_grid:
                        all_samples.append(torch.stack([ToTensor()(sample) for sample in samples], 0))

            if not args.skip_grid and all_samples:
                # additionally, save as grid
                grid = torch.stack(all_samples, 0)
                grid = rearrange(grid, "n b c h w -> (n b) c h w")
                grid = make_grid(grid, nrow=n_rows, normalize=True, value_range=(-1, 1))

                # to image
                grid = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
                grid = Image.fromarray(grid.astype(np.uint8))
                grid.save(os.path.join(outpath, f"grid.png"))
                del grid
        del all_samples

    print("Done.")


def parse_args():
    parser = argparse.ArgumentParser()
    # GenEval
    parser.add_argument("--dataset", default="GenEval", type=str)
    parser.add_argument("--model_path", default=None, type=str, help="Path to the model file (optional)")
    parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default="outputs")
    parser.add_argument("--seed", default=0, type=int)
    parser.add_argument(
        "--n_samples",
        type=int,
        default=4,
        help="number of samples",
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        default=1,
        help="how many samples can be produced simultaneously",
    )
    parser.add_argument(
        "--diffusers",
        action="store_true",
        help="if use diffusers pipeline",
    )
    parser.add_argument(
        "--skip_grid",
        action="store_true",
        help="skip saving grid",
    )

    parser.add_argument("--work_dir", default=None, type=str)
    parser.add_argument("--sample_nums", default=553, type=int)
    parser.add_argument("--add_label", default="", type=str)
    parser.add_argument("--exist_time_prefix", default="", type=str)
    parser.add_argument("--gpu_id", type=int, default=0)
    parser.add_argument("--start_index", type=int, default=0)
    parser.add_argument("--end_index", type=int, default=553)
    parser.add_argument(
        "--if_save_dirname",
        action="store_true",
        help="if save img save dir name at wor_dir/metrics/tmp_time.time().txt for metric testing",
    )

    args = parser.parse_args()
    return args


if __name__ == "__main__":
    args = parse_args()
    set_env(args.seed)
    device = "cuda" if torch.cuda.is_available() else "cpu"
    logger = get_root_logger()
    generator = torch.Generator(device=device).manual_seed(args.seed)
    n_rows = batch_size = args.n_samples
    assert args.batch_size == 1, ValueError(f"{batch_size} > 1 is not available in GenEval")

    from diffusers import DiffusionPipeline, StableDiffusionPipeline

    model = DiffusionPipeline.from_pretrained(
        args.model_path, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
    )
    model.enable_xformers_memory_efficient_attention()
    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)
    model.enable_attention_slicing()

    # dataset
    metadatas = datasets.load_dataset(
        "scripts/inference_geneval.py", trust_remote_code=True, split=f"train[{args.start_index}:{args.end_index}]"
    )
    logger.info(f"Eval {len(metadatas)} samples")

    # save path
    if args.work_dir is None:
        work_dir = (
            f"/{os.path.join(*args.model_path.split('/')[:-1])}"
            if args.model_path.startswith("/")
            else os.path.join(*args.model_path.split("/")[:-1])
        )
    else:
        work_dir = args.work_dir
    args.work_dir = work_dir
    img_save_dir = os.path.join(str(work_dir), "vis")

    os.umask(0o000)
    os.makedirs(img_save_dir, exist_ok=True)

    save_root = (
        os.path.join(
            img_save_dir,
            f"{args.dataset}_{model.config['_class_name']}_bs{batch_size}_seed{args.seed}_imgnums{args.sample_nums}",
        )
        + args.add_label
    )
    print(f"images save at: {img_save_dir}")
    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
        os.makedirs(f"{work_dir}/metrics", exist_ok=True)
        with open(f"{work_dir}/metrics/tmp_geneval_{time.time()}.txt", "w") as f:
            print(f"save tmp file at {work_dir}/metrics/tmp_geneval_{time.time()}.txt")
            f.write(os.path.basename(save_root))

    visualize()