autoregressive/sample/sample_t2i.py

import torch
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
torch.set_float32_matmul_precision('high')
setattr(torch.nn.Linear, 'reset_parameters', lambda self: None)     # disable default parameter init for faster speed
setattr(torch.nn.LayerNorm, 'reset_parameters', lambda self: None)  # disable default parameter init for faster speed
from torchvision.utils import save_image

import os
import sys
current_directory = os.getcwd()
sys.path.append(current_directory)
import time
import argparse
from tokenizer.tokenizer_image.vq_model import VQ_models
from language.t5 import T5Embedder
from autoregressive.models.gpt import GPT_models
from autoregressive.models.gpt_t2i import GPT_models
from autoregressive.models.generate import generate
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from dataset.t2i_control import build_t2i_control_code
from accelerate import Accelerator
from dataset.build import build_dataset
from pathlib import Path
from accelerate.utils import ProjectConfiguration, set_seed
import torch.nn.functional as F
from condition.canny import CannyDetector
from condition.hed import HEDdetector
import numpy as np
from PIL import Image
from condition.lineart import LineArt
import cv2
from transformers import DPTImageProcessor, DPTForDepthEstimation
def main(args):
    # Setup PyTorch:
    torch.manual_seed(args.seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    torch.set_grad_enabled(False)
    device = "cuda" if torch.cuda.is_available() else "cpu"

    # create and load model
    vq_model = VQ_models[args.vq_model](
        codebook_size=args.codebook_size,
        codebook_embed_dim=args.codebook_embed_dim)
    vq_model.to(device)
    vq_model.eval()
    checkpoint = torch.load(args.vq_ckpt, map_location="cpu")
    vq_model.load_state_dict(checkpoint["model"])
    del checkpoint
    print(f"image tokenizer is loaded")

    # create and load gpt model
    precision = {'none': torch.float32, 'bf16': torch.bfloat16, 'fp16': torch.float16}[args.precision]
    latent_size = args.image_size // args.downsample_size
    gpt_model = GPT_models[args.gpt_model](
        block_size=latent_size ** 2,
        cls_token_num=args.cls_token_num,
        model_type=args.gpt_type,
        condition_type=args.condition_type,
        adapter_size=args.adapter_size,
    ).to(device=device, dtype=precision)

    _, file_extension = os.path.splitext(args.gpt_ckpt)
    if file_extension.lower() == '.safetensors':
        from safetensors.torch import load_file
        model_weight = load_file(args.gpt_ckpt)
        gpt_model.load_state_dict(model_weight, strict=False)
        gpt_model.eval()
    else:
        checkpoint = torch.load(args.gpt_ckpt, map_location="cpu")
        if "model" in checkpoint:  # ddp
            model_weight = checkpoint["model"]
        elif "module" in checkpoint: # deepspeed
            model_weight = checkpoint["module"]
        elif "state_dict" in checkpoint:
            model_weight = checkpoint["state_dict"]
        else:
            raise Exception("please check model weight")
        gpt_model.load_state_dict(model_weight, strict=False)
        gpt_model.eval()
        del checkpoint
    print(f"gpt model is loaded")

    if args.compile:
        print(f"compiling the model...")
        gpt_model = torch.compile(
            gpt_model,
            mode="reduce-overhead",
            fullgraph=True
        ) # requires PyTorch 2.0 (optional)
    else:
        print(f"no need to compile model in demo") 
    
    assert os.path.exists(args.t5_path)
    t5_model = T5Embedder(
        device=device, 
        local_cache=True, 
        cache_dir=args.t5_path, 
        dir_or_name=args.t5_model_type,
        torch_dtype=precision,
        model_max_length=args.t5_feature_max_len,
    )
    

    if args.condition_type == 'canny':
        get_control = CannyDetector()
    elif args.condition_type == 'hed':
        get_control = HEDdetector().to(device).eval()
    elif args.condition_type == 'lineart':
        get_control = LineArt()
        get_control.load_state_dict(torch.load('condition/ckpts/model.pth', map_location=torch.device('cpu')))
        get_control.to(device)
    elif args.condition_type == 'depth':
        processor = DPTImageProcessor.from_pretrained("condition/ckpts/dpt_large")
        model = DPTForDepthEstimation.from_pretrained("condition/ckpts/dpt_large").to(device)
    with torch.no_grad():
        
        condition_path = args.condition_path
        if args.condition_type == 'seg':
            condition_img = torch.from_numpy(np.array(Image.open(condition_path)))
            condition_img = condition_img.permute(2,0,1).unsqueeze(0).repeat(2,1,1,1)
        elif args.condition_type == 'canny':
            condition_img = get_control(np.array(Image.open(condition_path)))
            condition_img = torch.from_numpy(condition_img[None,None,...]).repeat(2,3,1,1)
        elif args.condition_type == 'hed':
            condition_img = get_control(torch.from_numpy(np.array(Image.open(condition_path))).permute(2,0,1).unsqueeze(0).to(device))
            condition_img = condition_img.unsqueeze(1).repeat(2,3,1,1)
        elif args.condition_type == 'lineart':
            condition_img = get_control(torch.from_numpy(np.array(Image.open(condition_path))).permute(2,0,1).unsqueeze(0).to(device).float())
            condition_img = 1 - condition_img
            condition_img = condition_img.repeat(2,3,1,1) * 255
        elif args.condition_type == 'depth':
            images = Image.open(condition_path)
            inputs = processor(images=images, return_tensors="pt", size=(512,512)).to(device)
            outputs = model(**inputs)
            condition_img = outputs.predicted_depth
            condition_img = condition_img.unsqueeze(0).repeat(2,3,1,1)
            condition_img = (condition_img * 255 / condition_img.max())
        # 所有加载的条件图像都会进行归一化处理，
        condition_img = condition_img.to(device)
        condition_img = 2*(condition_img/255 - 0.5)#使得图像值在 [-1, 1] 范围内，以便与生成模型的输入匹配。
        prompts = [args.prompt if args.prompt is not None else "a high-quality image"]
        prompts = prompts * 2
        # 通过 T5Embedder 模型来获取文本提示（prompt）的嵌入向量 caption_embs
        caption_embs, emb_masks = t5_model.get_text_embeddings(prompts)

        if not args.no_left_padding:
            print(f"processing left-padding...")    
            # a naive way to implement left-padding
            new_emb_masks = torch.flip(emb_masks, dims=[-1])
            new_caption_embs = []
            for idx, (caption_emb, emb_mask) in enumerate(zip(caption_embs, emb_masks)):
                valid_num = int(emb_mask.sum().item())
                print(f'  prompt {idx} token len: {valid_num}')
                new_caption_emb = torch.cat([caption_emb[valid_num:],caption_emb[:valid_num]])
                new_caption_embs.append(new_caption_emb)
            new_caption_embs = torch.stack(new_caption_embs)
        else:
            new_caption_embs, new_emb_masks = caption_embs, emb_masks
        c_indices = new_caption_embs * new_emb_masks[:,:, None]
        c_emb_masks = new_emb_masks
        qzshape = [len(c_indices), args.codebook_embed_dim, args.image_H//args.downsample_size, args.image_W//args.downsample_size]
        t1 = time.time()
        # generate() 函数使用这些输入（文本token：c_indices和图像条件）来生成对应的 图像 token
        index_sample = generate(
            gpt_model, c_indices, (args.image_H//args.downsample_size)*(args.image_W//args.downsample_size),#latent_size ** 2, 
            c_emb_masks, condition=condition_img.to(precision),
            cfg_scale=args.cfg_scale,
            temperature=args.temperature, top_k=args.top_k,
            top_p=args.top_p, sample_logits=True, 
            control_strength=args.control_strength,
            )
        sampling_time = time.time() - t1
        print(f"Full sampling takes about {sampling_time:.2f} seconds.")    
        
        t2 = time.time()
        print(index_sample.shape)
        # 生成的图像 token（index_sample）会通过 vq_model.decode_code() 解码成实际的图像样本
        samples = vq_model.decode_code(index_sample, qzshape) # output value is between [-1, 1]
        decoder_time = time.time() - t2
        print(f"decoder takes about {decoder_time:.2f} seconds.")

        samples = torch.cat((condition_img[0:1], samples), dim=0)
        save_image(samples, f"sample/example/sample_t2i_{args.condition_type}.png", nrow=4, normalize=True, value_range=(-1, 1))
        print(f"image is saved to sample/example/sample_t2i_{args.condition_type}.png")
        print(prompts)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--t5-path", type=str, default='checkpoints/t5-ckpt')
    parser.add_argument("--t5-model-type", type=str, default='flan-t5-xl')
    parser.add_argument("--t5-feature-max-len", type=int, default=120)
    parser.add_argument("--t5-feature-dim", type=int, default=2048)
    parser.add_argument("--no-left-padding", action='store_true', default=False)
    parser.add_argument("--gpt-model", type=str, choices=list(GPT_models.keys()), default="GPT-XL")
    parser.add_argument("--gpt-ckpt", type=str, default=None)
    parser.add_argument("--gpt-type", type=str, choices=['c2i', 't2i'], default="t2i", help="class->image or text->image")  
    parser.add_argument("--cls-token-num", type=int, default=120, help="max token number of condition input")
    parser.add_argument("--precision", type=str, default='bf16', choices=["none", "fp16", "bf16"]) 
    parser.add_argument("--compile", action='store_true', default=False)
    parser.add_argument("--vq-model", type=str, choices=list(VQ_models.keys()), default="VQ-16")
    parser.add_argument("--vq-ckpt", type=str, default=None, help="ckpt path for vq model")
    parser.add_argument("--codebook-size", type=int, default=16384, help="codebook size for vector quantization")
    parser.add_argument("--codebook-embed-dim", type=int, default=8, help="codebook dimension for vector quantization")
    parser.add_argument("--image-size", type=int, choices=[256, 320, 384, 400, 448, 512, 576, 640, 704, 768], default=768)
    parser.add_argument("--image-H", type=int, default=512)
    parser.add_argument("--image-W", type=int, default=512)
    parser.add_argument("--downsample-size", type=int, choices=[8, 16], default=16)
    parser.add_argument("--cfg-scale", type=float, default=4)
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--top-k", type=int, default=2000, help="top-k value to sample with")
    parser.add_argument("--temperature", type=float, default=1.0, help="temperature value to sample with")
    parser.add_argument("--top-p", type=float, default=1.0, help="top-p value to sample with")

    parser.add_argument("--mixed-precision", type=str, default='bf16', choices=["none", "fp16", "bf16"]) 
    parser.add_argument("--condition-type", type=str, choices=['seg', 'canny', 'hed', 'lineart', 'depth', 'canny_base'], default="canny")
    parser.add_argument("--prompt", type=str, default='a high-quality image')
    parser.add_argument("--condition-path", type=str, default='condition/example/t2i/multigen/landscape.png')
    parser.add_argument("--adapter-size", type=str, default='small')

    parser.add_argument("--control-strength", type=float, default=1.0)
    args = parser.parse_args()
    main(args)