wehub-resource-sync
426 lines
18 KiB
Python
426 lines
18 KiB
Python
# 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 re
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import time
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import warnings
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from dataclasses import dataclass, field
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from typing import Optional
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import numpy as np
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import pyrallis
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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 torchvision.utils import _log_api_usage_once, make_grid, save_image
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from tqdm import tqdm
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warnings.filterwarnings("ignore") # ignore warning
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from diffusion import DPMS, FlowEuler, SASolverSampler
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from diffusion.data.datasets.utils import (
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ASPECT_RATIO_512_TEST,
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ASPECT_RATIO_1024_TEST,
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ASPECT_RATIO_2048_TEST,
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ASPECT_RATIO_4096_TEST,
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get_chunks,
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)
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from diffusion.model.builder import build_model, get_tokenizer_and_text_encoder, get_vae, vae_decode
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from diffusion.model.utils import get_weight_dtype, prepare_prompt_ar
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from diffusion.utils.config import SanaConfig, model_init_config
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from diffusion.utils.logger import get_root_logger
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# from diffusion.utils.misc import read_config
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from tools.download import find_model
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@torch.no_grad()
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def pil_image(
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tensor,
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**kwargs,
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) -> Image:
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if not torch.jit.is_scripting() and not torch.jit.is_tracing():
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_log_api_usage_once(save_image)
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grid = make_grid(tensor, **kwargs)
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# Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer
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ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
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img = Image.fromarray(ndarr)
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return img
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def set_env(seed=0, latent_size=256):
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torch.manual_seed(seed)
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torch.set_grad_enabled(False)
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for _ in range(30):
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torch.randn(1, 4, latent_size, latent_size)
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@torch.inference_mode()
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def visualize(items, bs, sample_steps, cfg_scale, pag_scale=1.0):
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generator = torch.Generator(device=device).manual_seed(args.seed)
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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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assert bs == 1
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for chunk in tqdm(list(get_chunks(items, bs)), desc=tqdm_desc, unit="batch", position=args.gpu_id, leave=True):
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prompt = data_dict[chunk[0]]["prompt"]
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# Generate images
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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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prompts, hw, ar = (
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[],
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torch.tensor([[args.image_size, args.image_size]], dtype=torch.float, device=device).repeat(
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batch_size, 1
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),
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torch.tensor([[1.0]], device=device).repeat(batch_size, 1),
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)
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for _ in range(batch_size):
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prompts.append(prepare_prompt_ar(prompt, base_ratios, device=device, show=False)[0].strip())
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latent_size_h, latent_size_w = latent_size, latent_size
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# check exists
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save_file_name = f"{chunk[0]}.jpg"
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save_path = os.path.join(save_root, save_file_name)
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if os.path.exists(save_path):
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# make sure the noise is totally same
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torch.randn(
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len(prompts),
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config.vae.vae_latent_dim,
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latent_size,
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latent_size,
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device=device,
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generator=generator,
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)
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continue
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# prepare text feature
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caption_token = tokenizer(
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prompts, max_length=max_sequence_length, padding="max_length", truncation=True, return_tensors="pt"
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).to(device)
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caption_embs = text_encoder(caption_token.input_ids, caption_token.attention_mask)[0][:, None]
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emb_masks, null_y = caption_token.attention_mask, null_caption_embs.repeat(len(prompts), 1, 1)[:, None]
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# start sampling
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with torch.no_grad():
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n = len(prompts)
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z = torch.randn(
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n,
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config.vae.vae_latent_dim,
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latent_size,
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latent_size,
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device=device,
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generator=generator,
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)
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model_kwargs = dict(data_info={"img_hw": hw, "aspect_ratio": ar}, mask=emb_masks)
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if args.sampling_algo == "dpm-solver":
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dpm_solver = DPMS(
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model.forward_with_dpmsolver,
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condition=caption_embs,
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uncondition=null_y,
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cfg_scale=cfg_scale,
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model_kwargs=model_kwargs,
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)
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samples = dpm_solver.sample(
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z,
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steps=sample_steps,
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order=2,
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skip_type="time_uniform",
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method="multistep",
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)
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elif args.sampling_algo == "sa-solver":
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sa_solver = SASolverSampler(model.forward_with_dpmsolver, device=device)
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samples = sa_solver.sample(
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S=25,
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batch_size=n,
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shape=(config.vae.vae_latent_dim, latent_size_h, latent_size_w),
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eta=1,
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conditioning=caption_embs,
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unconditional_conditioning=null_y,
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unconditional_guidance_scale=cfg_scale,
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model_kwargs=model_kwargs,
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)[0]
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elif args.sampling_algo == "flow_euler":
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flow_solver = FlowEuler(
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model,
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condition=caption_embs,
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uncondition=null_y,
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cfg_scale=cfg_scale,
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model_kwargs=model_kwargs,
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)
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samples = flow_solver.sample(
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z,
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steps=sample_steps,
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)
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elif args.sampling_algo == "flow_dpm-solver":
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dpm_solver = DPMS(
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model.forward_with_dpmsolver,
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condition=caption_embs,
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uncondition=null_y,
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guidance_type=guidance_type,
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cfg_scale=cfg_scale,
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pag_scale=pag_scale,
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pag_applied_layers=pag_applied_layers,
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model_type="flow",
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model_kwargs=model_kwargs,
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schedule="FLOW",
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interval_guidance=args.interval_guidance,
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)
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samples = dpm_solver.sample(
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z,
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steps=sample_steps,
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order=2,
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skip_type="time_uniform_flow",
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method="multistep",
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flow_shift=flow_shift,
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)
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else:
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raise ValueError(f"{args.sampling_algo} is not defined")
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samples = samples.to(vae_dtype)
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samples = vae_decode(config.vae.vae_type, vae, samples)
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torch.cuda.empty_cache()
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all_samples.append(samples)
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if 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(save_path)
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del grid
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del all_samples
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print("Done.")
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--config", type=str, help="config")
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parser.add_argument("--model_path", default=None, type=str, help="Path to the model file (optional)")
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return parser.parse_known_args()[0]
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@dataclass
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class SanaInference(SanaConfig):
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config: Optional[str] = "configs/sana_config/1024ms/Sana_1600M_img1024.yaml" # config
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dataset: str = "DPG"
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outdir: str = "outputs"
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n_samples: int = 4
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batch_size: int = 1
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skip_grid: bool = False
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position_model_path: str = "output/pretrained_models/Sana.pth"
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model_path: str = None
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txt_file: str = "asset/samples/samples.txt"
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json_file: str = None
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sample_nums: int = 1065
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cfg_scale: float = 4.5
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pag_scale: float = 1.0
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sampling_algo: str = field(
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default="dpm-solver", metadata={"choices": ["dpm-solver", "sa-solver", "flow_euler", "flow_dpm-solver"]}
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)
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bs: int = 1
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seed: int = 0
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step: int = -1
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add_label: str = ""
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tar_and_del: bool = False
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exist_time_prefix: str = ""
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gpu_id: int = 0
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image_size: int = 512
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custom_image_size: int = None
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start_index: int = 0
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end_index: int = 553
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interval_guidance: list = field(
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default_factory=lambda: [0, 1], metadata={"help": "A list value, like [0, 1.] for use cfg"}
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)
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ablation_selections: list = None
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ablation_key: str = field(default=None, metadata={"choices": ["step", "cfg_scale", "pag_scale"]})
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if_save_dirname: bool = False
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if __name__ == "__main__":
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args = get_args()
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config = args = pyrallis.parse(config_class=SanaInference, config_path=args.config)
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# config = read_config(args.config)
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args.image_size = config.model.image_size
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if args.custom_image_size:
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args.image_size = args.custom_image_size
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print(f"custom_image_size: {args.image_size}")
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set_env(args.seed, args.image_size // config.vae.vae_downsample_rate)
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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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n_rows = args.n_samples // 2
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batch_size = args.n_samples
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assert args.batch_size == 1, ValueError(f"{batch_size} > 1 is not available in DPG-bench")
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# only support fixed latent size currently
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latent_size = args.image_size // config.vae.vae_downsample_rate
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max_sequence_length = config.text_encoder.model_max_length
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pe_interpolation = config.model.pe_interpolation
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micro_condition = config.model.micro_condition
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flow_shift = config.scheduler.flow_shift
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pag_applied_layers = config.model.pag_applied_layers
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guidance_type = "classifier-free_PAG"
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# guidance_type = config.guidance_type
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assert (
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isinstance(args.interval_guidance, list)
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and len(args.interval_guidance) == 2
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and args.interval_guidance[0] <= args.interval_guidance[1]
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)
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args.interval_guidance = [max(0, args.interval_guidance[0]), min(1, args.interval_guidance[1])]
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sample_steps_dict = {"dpm-solver": 20, "sa-solver": 25, "flow_dpm-solver": 20, "flow_euler": 28}
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sample_steps = args.step if args.step != -1 else sample_steps_dict[args.sampling_algo]
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weight_dtype = get_weight_dtype(config.model.mixed_precision)
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logger.info(f"Inference with {weight_dtype}, default guidance_type: {guidance_type}, flow_shift: {flow_shift}")
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vae_dtype = get_weight_dtype(config.vae.weight_dtype)
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vae = get_vae(config.vae.vae_type, config.vae.vae_pretrained, device).to(vae_dtype)
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tokenizer, text_encoder = get_tokenizer_and_text_encoder(name=config.text_encoder.text_encoder_name, device=device)
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null_caption_token = tokenizer(
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"", max_length=max_sequence_length, padding="max_length", truncation=True, return_tensors="pt"
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).to(device)
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null_caption_embs = text_encoder(null_caption_token.input_ids, null_caption_token.attention_mask)[0]
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# model setting
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model_kwargs = model_init_config(config, latent_size=latent_size)
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model = build_model(
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config.model.model, use_fp32_attention=config.model.get("fp32_attention", False), **model_kwargs
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).to(device)
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# model = build_model(config.model, **model_kwargs).to(device)
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logger.info(
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f"{model.__class__.__name__}:{config.model.model}, Model Parameters: {sum(p.numel() for p in model.parameters()):,}"
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)
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args.model_path = args.model_path or args.position_model_path
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logger.info("Generating sample from ckpt: %s" % args.model_path)
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state_dict = find_model(args.model_path)
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if "pos_embed" in state_dict["state_dict"]:
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del state_dict["state_dict"]["pos_embed"]
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missing, unexpected = model.load_state_dict(state_dict["state_dict"], strict=False)
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logger.warning(f"Missing keys: {missing}")
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logger.warning(f"Unexpected keys: {unexpected}")
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model.eval().to(weight_dtype)
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base_ratios = eval(f"ASPECT_RATIO_{args.image_size}_TEST")
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args.sampling_algo = (
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args.sampling_algo
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if ("flow" not in args.model_path or args.sampling_algo == "flow_dpm-solver")
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else "flow_euler"
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)
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work_dir = (
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f"/{os.path.join(*args.model_path.split('/')[:-2])}"
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if args.model_path.startswith("/")
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else os.path.join(*args.model_path.split("/")[:-2])
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)
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# dataset
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dict_prompt = args.json_file is not None
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if dict_prompt:
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data_dict = json.load(open(args.json_file))
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items = list(data_dict.keys())
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else:
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with open(args.txt_file) as f:
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items = [item.strip() for item in f.readlines()]
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logger.info(f"Eval first {min(args.sample_nums, len(items))}/{len(items)} samples")
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items = items[: max(0, args.sample_nums)]
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items = items[max(0, args.start_index) : min(len(items), args.end_index)] # save path
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match = re.search(r".*epoch_(\d+).*step_(\d+).*", args.model_path)
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epoch_name, step_name = match.groups() if match else ("unknown", "unknown")
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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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logger.info(f"Sampler {args.sampling_algo}")
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def create_save_root(args, dataset, epoch_name, step_name, sample_steps, guidance_type):
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save_root = os.path.join(
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img_save_dir,
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# f"{datetime.now().date() if args.exist_time_prefix == '' else args.exist_time_prefix}_"
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f"{dataset}_epoch{epoch_name}_step{step_name}_scale{args.cfg_scale}"
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f"_step{sample_steps}_size{args.image_size}_bs{batch_size}_samp{args.sampling_algo}"
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f"_seed{args.seed}_{str(weight_dtype).split('.')[-1]}",
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)
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if args.pag_scale != 1.0:
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save_root = save_root.replace(f"scale{args.cfg_scale}", f"scale{args.cfg_scale}_pagscale{args.pag_scale}")
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if flow_shift != 1.0:
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save_root += f"_flowshift{flow_shift}"
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if guidance_type != "classifier-free":
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save_root += f"_{guidance_type}"
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if args.interval_guidance[0] != 0 and args.interval_guidance[1] != 1:
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save_root += f"_intervalguidance{args.interval_guidance[0]}{args.interval_guidance[1]}"
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save_root += f"_imgnums{args.sample_nums}" + args.add_label
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return save_root
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def guidance_type_select(default_guidance_type, pag_scale, attn_type):
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guidance_type = default_guidance_type
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if not (pag_scale > 1.0 and attn_type == "linear"):
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logger.info("Setting back to classifier-free")
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guidance_type = "classifier-free"
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return guidance_type
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dataset = "MJHQ-30K" if args.json_file and "MJHQ-30K" in args.json_file else args.dataset
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if args.ablation_selections and args.ablation_key:
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for ablation_factor in args.ablation_selections:
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setattr(args, args.ablation_key, eval(ablation_factor))
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print(f"Setting {args.ablation_key}={eval(ablation_factor)}")
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sample_steps = args.step if args.step != -1 else sample_steps_dict[args.sampling_algo]
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guidance_type = guidance_type_select(guidance_type, args.pag_scale, config.model.attn_type)
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save_root = create_save_root(args, dataset, epoch_name, step_name, sample_steps, guidance_type)
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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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with open(f"{work_dir}/metrics/tmp_{dataset}_{time.time()}.txt", "w") as f:
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print(f"save tmp file at {work_dir}/metrics/tmp_{dataset}_{time.time()}.txt")
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f.write(os.path.basename(save_root))
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logger.info(f"Inference with {weight_dtype}, guidance_type: {guidance_type}, flow_shift: {flow_shift}")
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visualize(items, args.bs, sample_steps, args.cfg_scale, args.pag_scale)
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else:
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guidance_type = guidance_type_select(guidance_type, args.pag_scale, config.model.attn_type)
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logger.info(f"Inference with {weight_dtype}, guidance_type: {guidance_type}, flow_shift: {flow_shift}")
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save_root = create_save_root(args, dataset, epoch_name, step_name, sample_steps, guidance_type)
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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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os.makedirs(f"{work_dir}/metrics", exist_ok=True)
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# save at work_dir/metrics/tmp_dpg_xxx.txt for metrics testing
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with open(f"{work_dir}/metrics/tmp_{dataset}_{time.time()}.txt", "w") as f:
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print(f"save tmp file at {work_dir}/metrics/tmp_{dataset}_{time.time()}.txt")
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f.write(os.path.basename(save_root))
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visualize(items, args.bs, sample_steps, args.cfg_scale, args.pag_scale)
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