chore: import upstream snapshot with attribution
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wehub-resource-sync
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# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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import argparse
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import json
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import os
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import time
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import datasets
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import numpy as np
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import torch
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from einops import rearrange
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from PIL import Image
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from pytorch_lightning import seed_everything
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from torchvision.transforms import ToTensor
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from torchvision.utils import make_grid
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from tqdm import tqdm, trange
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from diffusion.utils.logger import get_root_logger
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_CITATION = """\
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@article{ghosh2024geneval,
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title={Geneval: An object-focused framework for evaluating text-to-image alignment},
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author={Ghosh, Dhruba and Hajishirzi, Hannaneh and Schmidt, Ludwig},
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journal={Advances in Neural Information Processing Systems},
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volume={36},
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year={2024}
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}
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"""
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_DESCRIPTION = (
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"We demonstrate the advantages of evaluating text-to-image models using existing object detection methods, "
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"to produce a fine-grained instance-level analysis of compositional capabilities."
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)
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def set_env(seed=0):
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torch.manual_seed(seed)
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torch.set_grad_enabled(False)
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@torch.inference_mode()
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def visualize():
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tqdm_desc = f"{save_root.split('/')[-1]} Using GPU: {args.gpu_id}: {args.start_index}-{args.end_index}"
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for index, metadata in tqdm(list(enumerate(metadatas)), desc=tqdm_desc, position=args.gpu_id, leave=True):
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metadata["include"] = (
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metadata["include"] if isinstance(metadata["include"], list) else eval(metadata["include"])
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)
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seed_everything(args.seed)
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index += args.start_index
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outpath = os.path.join(save_root, f"{index:0>5}")
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os.makedirs(outpath, exist_ok=True)
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sample_path = os.path.join(outpath, "samples")
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os.makedirs(sample_path, exist_ok=True)
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prompt = metadata["prompt"]
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# print(f"Prompt ({index: >3}/{len(metadatas)}): '{prompt}'")
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with open(os.path.join(outpath, "metadata.jsonl"), "w") as fp:
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json.dump(metadata, fp)
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sample_count = 0
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with torch.no_grad():
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all_samples = list()
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for _ in range((args.n_samples + batch_size - 1) // batch_size):
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#
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# check exists
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save_path = os.path.join(sample_path, f"{sample_count:05}.png")
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if os.path.exists(save_path):
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continue
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else:
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# Generate images
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samples = model(
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prompt,
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height=None,
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width=None,
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num_inference_steps=50,
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guidance_scale=9.0,
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num_images_per_prompt=min(batch_size, args.n_samples - sample_count),
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negative_prompt=None,
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).images
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for sample in samples:
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sample.save(os.path.join(sample_path, f"{sample_count:05}.png"))
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sample_count += 1
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if not args.skip_grid:
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all_samples.append(torch.stack([ToTensor()(sample) for sample in samples], 0))
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if not args.skip_grid and all_samples:
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# additionally, save as grid
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grid = torch.stack(all_samples, 0)
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grid = rearrange(grid, "n b c h w -> (n b) c h w")
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grid = make_grid(grid, nrow=n_rows, normalize=True, value_range=(-1, 1))
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# to image
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grid = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
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grid = Image.fromarray(grid.astype(np.uint8))
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grid.save(os.path.join(outpath, f"grid.png"))
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del grid
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del all_samples
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print("Done.")
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def parse_args():
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parser = argparse.ArgumentParser()
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# GenEval
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parser.add_argument("--dataset", default="GenEval", type=str)
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parser.add_argument("--model_path", default=None, type=str, help="Path to the model file (optional)")
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parser.add_argument("--outdir", type=str, nargs="?", help="dir to write results to", default="outputs")
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parser.add_argument("--seed", default=0, type=int)
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parser.add_argument(
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"--n_samples",
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type=int,
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default=4,
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help="number of samples",
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)
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parser.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="how many samples can be produced simultaneously",
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)
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parser.add_argument(
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"--diffusers",
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action="store_true",
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help="if use diffusers pipeline",
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)
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parser.add_argument(
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"--skip_grid",
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action="store_true",
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help="skip saving grid",
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)
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parser.add_argument("--work_dir", default=None, type=str)
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parser.add_argument("--sample_nums", default=553, type=int)
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parser.add_argument("--add_label", default="", type=str)
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parser.add_argument("--exist_time_prefix", default="", type=str)
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parser.add_argument("--gpu_id", type=int, default=0)
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parser.add_argument("--start_index", type=int, default=0)
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parser.add_argument("--end_index", type=int, default=553)
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parser.add_argument(
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"--if_save_dirname",
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action="store_true",
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help="if save img save dir name at wor_dir/metrics/tmp_time.time().txt for metric testing",
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)
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args = parser.parse_args()
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return args
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if __name__ == "__main__":
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args = parse_args()
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set_env(args.seed)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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logger = get_root_logger()
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generator = torch.Generator(device=device).manual_seed(args.seed)
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n_rows = batch_size = args.n_samples
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assert args.batch_size == 1, ValueError(f"{batch_size} > 1 is not available in GenEval")
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from diffusers import DiffusionPipeline, StableDiffusionPipeline
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model = DiffusionPipeline.from_pretrained(
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args.model_path, torch_dtype=torch.float16, use_safetensors=True, variant="fp16"
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)
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model.enable_xformers_memory_efficient_attention()
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device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
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model = model.to(device)
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model.enable_attention_slicing()
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# dataset
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metadatas = datasets.load_dataset(
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"scripts/inference_geneval.py", trust_remote_code=True, split=f"train[{args.start_index}:{args.end_index}]"
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)
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logger.info(f"Eval {len(metadatas)} samples")
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# save path
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if args.work_dir is None:
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work_dir = (
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f"/{os.path.join(*args.model_path.split('/')[:-1])}"
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if args.model_path.startswith("/")
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else os.path.join(*args.model_path.split("/")[:-1])
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)
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else:
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work_dir = args.work_dir
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args.work_dir = work_dir
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img_save_dir = os.path.join(str(work_dir), "vis")
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os.umask(0o000)
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os.makedirs(img_save_dir, exist_ok=True)
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save_root = (
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os.path.join(
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img_save_dir,
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f"{args.dataset}_{model.config['_class_name']}_bs{batch_size}_seed{args.seed}_imgnums{args.sample_nums}",
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)
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+ args.add_label
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)
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print(f"images save at: {img_save_dir}")
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os.makedirs(save_root, exist_ok=True)
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if args.if_save_dirname and args.gpu_id == 0:
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# save at work_dir/metrics/tmp_xxx.txt for metrics testing
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os.makedirs(f"{work_dir}/metrics", exist_ok=True)
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with open(f"{work_dir}/metrics/tmp_geneval_{time.time()}.txt", "w") as f:
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print(f"save tmp file at {work_dir}/metrics/tmp_geneval_{time.time()}.txt")
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f.write(os.path.basename(save_root))
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visualize()
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