sample.py

# This code is built from the Stable Diffusion repository: https://github.com/CompVis/stable-diffusion.
# Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors.
# CreativeML Open RAIL-M
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# Adobe’s modifications are Copyright 2022 Adobe Research. All rights reserved.
# Adobe’s modifications are licensed under the Adobe Research License. To view a copy of the license, visit
# LICENSE.md.
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# CreativeML Open RAIL-M License
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# Attachment A

# Use Restrictions

# You agree not to use the Model or Derivatives of the Model:
# - In any way that violates any applicable national, federal, state, local or international law or regulation;
# - For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
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import argparse, os, sys, glob
sys.path.append('stable-diffusion')
import torch
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from einops import rearrange
from torchvision.utils import make_grid
from pytorch_lightning import seed_everything
from torch import autocast
from contextlib import contextmanager, nullcontext

from ldm.util import instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
from ldm.models.diffusion.plms import PLMSSampler
import wandb

import torch.nn.functional as F
import math


def load_model_from_config(config, ckpt, verbose=False):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    if "global_step" in pl_sd:
        print(f"Global Step: {pl_sd['global_step']}")
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)

    token_weights = sd["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]
    del sd["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]
    m, u = model.load_state_dict(sd, strict=False)
    model.cond_stage_model.transformer.text_model.embeddings.token_embedding.weight.data[:token_weights.shape[0]] = token_weights
    if len(m) > 0 and verbose:
        print("missing keys:")
        print(m)
    if len(u) > 0 and verbose:
        print("unexpected keys:")
        print(u)

    model.cuda()
    model.eval()
    return model


from PIL import Image
from src.attention_controllers import AttentionStore
from typing import Tuple, List
import cv2
import PIL
# from src import ptp_utils

def text_under_image(image: np.ndarray, text: str, text_color: Tuple[int, int, int] = (0, 0, 0)):
    h, w, c = image.shape
    offset = int(h * .2)
    img = np.ones((h + offset, w, c), dtype=np.uint8) * 255
    font = cv2.FONT_HERSHEY_SIMPLEX
    # font = ImageFont.truetype("/usr/share/fonts/truetype/noto/NotoMono-Regular.ttf", font_size)
    img[:h] = image
    textsize = cv2.getTextSize(text, font, 1, 2)[0]
    text_x, text_y = (w - textsize[0]) // 2, h + offset - textsize[1] // 2
    cv2.putText(img, text, (text_x, text_y ), font, 1, text_color, 2)
    return img

def view_images(images, save_path, base_count, num_rows=1, offset_ratio=0.02, step=None):
    if type(images) is list:
        num_empty = len(images) % num_rows
    elif images.ndim == 4:
        num_empty = images.shape[0] % num_rows
    else:
        images = [images]
        num_empty = 0

    empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
    images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
    num_items = len(images)

    h, w, c = images[0].shape
    offset = int(h * offset_ratio)
    num_cols = num_items // num_rows
    image_ = np.ones((h * num_rows + offset * (num_rows - 1),
                      w * num_cols + offset * (num_cols - 1), 3), dtype=np.uint8) * 255
    for i in range(num_rows):
        for j in range(num_cols):
            image_[i * (h + offset): i * (h + offset) + h:, j * (w + offset): j * (w + offset) + w] = images[
                i * num_cols + j]

    pil_img = Image.fromarray(image_)
    base_count += 40
    if step is not None:
        pil_img.save(os.path.join(save_path, f"{base_count:05}_{step}.png"))
    else:
        pil_img.save(os.path.join(save_path, f"{base_count:05}.png"))
    # display(pil_img)

def aggregate_attention(prompts, attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int):
    out = []
    attention_maps = attention_store.get_average_attention()
    num_pixels = res ** 2
    for location in from_where:
        for item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]:
            if item.shape[1] == num_pixels:
                cross_maps = item.reshape(len(prompts), -1, res, res, item.shape[-1])[select]
                out.append(cross_maps)
    out = torch.cat(out, dim=0)
    out = out.sum(0) / out.shape[0]
    return out.cpu()

def aggregate_attention_all(prompts, attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int, len_tokens: int):
    attention_maps = attention_store.get_average_attention()

    out = []
    for _, value in attention_maps.items():
        

        for item in value:
            res = int(math.sqrt(item.shape[1]))
            cross_maps = item.reshape(len(prompts), -1, res , res, item.shape[-1])[select]
            if res!=16:
                continue
            cross_maps_512 = torch.zeros((cross_maps.shape[0], 256, 256, 77))
            for token in range(len_tokens):
                token_cross_map = cross_maps[:, :, :, token].reshape(1, cross_maps.shape[0], res, res)
                token_cross_map = F.interpolate(token_cross_map, size=(256, 256), mode='bicubic').reshape(cross_maps.shape[0], 256, 256)
                cross_maps_512[:, :, :, token] = token_cross_map[:, :, :]
            out.append(cross_maps_512)
    out = torch.cat(out, dim=0)
    out = out.sum(0) / out.shape[0]

    absolute = True
    if not absolute:
        out = (out - out.min()) / (out.max() - out.min() + 1e-8)

    return out.cpu()


def aggregate_attention_all_step(prompts, attention_store: AttentionStore, res: int, from_where: List[str], is_cross: bool, select: int, len_tokens: int):
    attention_all_step_maps = attention_store.all_step_attention

    out_all_step = {}
    for step, attention_maps in attention_all_step_maps.items():
        out = []
        for _, value in attention_maps.items():
            for item in value:
                res = int(math.sqrt(item.shape[1]))
                cross_maps = item.reshape(len(prompts), -1, res , res, item.shape[-1])[select]
                cross_maps_512 = torch.zeros((cross_maps.shape[0], 256, 256, 77))
                for token in range(len_tokens):
                    token_cross_map = cross_maps[:, :, :, token].reshape(1, cross_maps.shape[0], res, res)
                    token_cross_map = F.interpolate(token_cross_map, size=(256, 256), mode='bicubic').reshape(cross_maps.shape[0], 256, 256)
                    cross_maps_512[:, :, :, token] = token_cross_map[:, :, :]
                out.append(cross_maps_512)
        print(step)
        out = torch.cat(out, dim=0)
        out = out.sum(0) / out.shape[0]

        absolute = True
        if not absolute:
            out = (out - out.min()) / (out.max() - out.min() + 1e-8)
        out_all_step[step] = out.cpu()

    return out_all_step

def show_cross_attention(prompts, tokenizer, save_path, base_count, attention_store: AttentionStore, res: int, from_where: List[str], select: int = 0):
    tokens = tokenizer.encode(prompts[select])
    decoder = tokenizer.decode
    
    attention_maps = aggregate_attention_all(prompts, attention_store, res, from_where, True, select, len(tokens))
    images = []
    for i in range(len(tokens)):
        image = attention_maps[:, :, i]
        image = 255 * image / image.max()
        image[image < 0] = 0 # process the overflow
        image = image.unsqueeze(-1).expand(*image.shape, 3)
        image = image.numpy().astype(np.uint8)
        image = np.array(Image.fromarray(image).resize((256, 256)))
        # tmp_image = Image.fromarray(image) 
        # tmp_image.save(os.path.join(save_path, '{}_{}.png'.format(base_count, decoder(int(tokens[i])))))
        text_under_image_string = decoder(int(tokens[i]))
        if text_under_image_string == '<new1>':
            text_under_image_string = 'V1*'
        elif text_under_image_string == '<new2>':
            text_under_image_string = 'V2*'
        image = text_under_image(image, text_under_image_string)
        # image = text_under_image(image, decoder(int(tokens[i])))

        images.append(image)
    view_images(np.stack(images, axis=0), save_path, base_count)

    if attention_store.all_step_attention_store:
        attention_maps_all_step = aggregate_attention_all_step(prompts, attention_store, res, from_where, True, select, len(tokens))
        for step, attention_maps in attention_maps_all_step.items():
            images = []
            for i in range(len(tokens)):
                image = attention_maps[:, :, i]
                image = 255 * image / image.max()
                image = image.unsqueeze(-1).expand(*image.shape, 3)
                image = image.numpy().astype(np.uint8)
                image = np.array(Image.fromarray(image).resize((256, 256)))
                image = text_under_image(image, decoder(int(tokens[i])))
                images.append(image)
            view_images(np.stack(images, axis=0), save_path, base_count, step=step)


def main():
    parser = argparse.ArgumentParser()

    parser.add_argument(
        "--prompt",
        type=str,
        nargs="?",
        default="a painting of a virus monster playing guitar",
        help="the prompt to render"
    )
    parser.add_argument(
        "--outdir",
        type=str,
        nargs="?",
        help="dir to write results to",
        default="outputs/txt2img-samples"
    )
    parser.add_argument(
        "--skip_grid",
        action='store_true',
        help="do not save a grid, only individual samples. Helpful when evaluating lots of samples",
    )
    parser.add_argument(
        "--skip_save",
        action='store_true',
        help="do not save individual samples. For speed measurements.",
    )
    parser.add_argument(
        "--ddim_steps",
        type=int,
        default=200,
        help="number of ddim sampling steps",
    )
    parser.add_argument(
        "--plms",
        action='store_true',
        help="use plms sampling",
    )
    parser.add_argument(
        "--laion400m",
        action='store_true',
        help="uses the LAION400M model",
    )
    parser.add_argument(
        "--fixed_code",
        action='store_true',
        help="if enabled, uses the same starting code across samples ",
    )
    parser.add_argument(
        "--ddim_eta",
        type=float,
        default=1.0,
        help="ddim eta (eta=0.0 corresponds to deterministic sampling",
    )
    parser.add_argument(
        "--n_iter",
        type=int,
        default=1,
        help="sample this often",
    )
    parser.add_argument(
        "--H",
        type=int,
        default=512,
        help="image height, in pixel space",
    )
    parser.add_argument(
        "--W",
        type=int,
        default=512,
        help="image width, in pixel space",
    )
    parser.add_argument(
        "--C",
        type=int,
        default=4,
        help="latent channels",
    )
    parser.add_argument(
        "--f",
        type=int,
        default=8,
        help="downsampling factor",
    )
    parser.add_argument(
        "--n_samples",
        type=int,
        default=6,
        help="how many samples to produce for each given prompt. A.k.a. batch size",
    )
    parser.add_argument(
        "--n_rows",
        type=int,
        default=6,
        help="rows in the grid (default: n_samples)",
    )
    parser.add_argument(
        "--scale",
        type=float,
        default=6,
        help="unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) - eps(x, empty))",
    )
    parser.add_argument(
        "--from-file",
        type=str,
        help="if specified, load prompts from this file",
    )
    parser.add_argument(
        "--config",
        type=str,
        default="configs/custom-diffusion/finetune.yaml",
        help="path to config which constructs model",
    )
    parser.add_argument(
        "--ckpt",
        type=str,
        required=True,
        help="path to checkpoint of the pre-trained model",
    )
    parser.add_argument(
        "--delta_ckpt",
        type=str,
        default=None,
        help="path to delta checkpoint of fine-tuned custom diffusion block",
    )
    parser.add_argument(
        "--seed",
        type=int,
        default=42,
        help="the seed (for reproducible sampling)",
    )
    parser.add_argument(
        "--precision",
        type=str,
        help="evaluate at this precision",
        choices=["full", "autocast"],
        default="autocast"
    )
    parser.add_argument(
        "--wandb_log",
        action='store_true',
        help="save grid images to wandb.",
    )
    parser.add_argument(
        "--compress",
        action='store_true',
        help="delta path provided is a compressed checkpoint.",
    )
    parser.add_argument(
        "--modifier_token",
        type=str,
        default=None,
        help="A token to use as a modifier for the concept.",
    )
    parser.add_argument(
        "--check_token_attn",
        action='store_true',
        default=False,
        help="",
    )
    parser.add_argument(
        "--file_name",
        type=str,
        default=None,
        help="",
    )
    parser.add_argument(
        "--all_step_attention_store",
        action='store_true',
        default=False,
        help="",
    )

    # parser.add_argument(
    #     "--diffusion_with_mask",
    #     action='store_true',
    #     default=False,
    #     help="",
    # )
    opt = parser.parse_args()

    if opt.wandb_log:
        if opt.delta_ckpt is not None:
            name = opt.delta_ckpt.split('/')[-3]
        elif 'checkpoints' in opt.ckpt:
            name = opt.ckpt.split('/')[-3]
        else:
            name = opt.ckpt.split('/')[-1]
        wandb.init(project="custom-diffusion", entity="cmu-gil", name=name )

    if opt.delta_ckpt is not None:
        if len(glob.glob(os.path.join(opt.delta_ckpt.split('checkpoints')[0], "configs/*.yaml"))) > 0:
            opt.config = sorted(glob.glob(os.path.join(opt.delta_ckpt.split('checkpoints')[0], "configs/*.yaml")))[-1]
    else:
        if len(glob.glob(os.path.join(opt.ckpt.split('checkpoints')[0], "configs/*.yaml"))) > 0:
            opt.config = sorted(glob.glob(os.path.join(opt.ckpt.split('checkpoints')[0], "configs/*.yaml")))[-1]
    seed_everything(opt.seed)
    config = OmegaConf.load(f"{opt.config}")
    if opt.modifier_token is not None:
        config.model.params.cond_stage_config.target = 'src.custom_modules.FrozenCLIPEmbedderWrapper'
        config.model.params.cond_stage_config.params = {}
        config.model.params.cond_stage_config.params.modifier_token = opt.modifier_token

    config.model.params.attention_store_background_mask = False
    config.model.params.attention_store_step = False
    config.model.params.check_token_attn = opt.check_token_attn
    config.model.params.all_step_attention_store = opt.all_step_attention_store
    model = load_model_from_config(config, f"{opt.ckpt}")
    if opt.delta_ckpt is not None:
        delta_st = torch.load(opt.delta_ckpt)
        embed = None
        if 'embed' in delta_st['state_dict']:
            embed = delta_st['state_dict']['embed'].reshape(-1,768)
            del delta_st['state_dict']['embed']
            print(embed.shape)
        delta_st = delta_st['state_dict']
        if opt.compress:
            for name in delta_st.keys():
                if 'to_k' in name or 'to_v' in name:
                    delta_st[name] = model.state_dict()[name] + delta_st[name]['u']@delta_st[name]['v']
            model.load_state_dict(delta_st, strict=False)
        else:
            model.load_state_dict(delta_st, strict=False)
        if embed is not None:
            print("loading new embedding")
            print(model.cond_stage_model.transformer.text_model.embeddings.token_embedding.weight.data.shape)
            model.cond_stage_model.transformer.text_model.embeddings.token_embedding.weight.data[-embed.shape[0]:] = embed

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
    model = model.to(device)

    if opt.plms:
        sampler = PLMSSampler(model)
    else:
        sampler = DDIMSampler(model)

    if opt.delta_ckpt is not None:
        outpath = os.path.dirname(os.path.dirname(opt.delta_ckpt))
    elif opt.ckpt is not None:
        outpath = os.path.dirname(os.path.dirname(opt.ckpt))
    else:        
        os.makedirs(opt.outdir, exist_ok=True)
        outpath = opt.outdir

    batch_size = opt.n_samples
    n_rows = opt.n_rows if opt.n_rows > 0 else batch_size
    if not opt.from_file:
        prompt = opt.prompt
        assert prompt is not None
        data = [batch_size * [prompt]]
    else:
        print(f"reading prompts from {opt.from_file}")
        with open(opt.from_file, "r") as f:
            data = f.read().splitlines()
            data = [batch_size * [prompt] for prompt in data]
    if opt.file_name is not None:
        sample_path = os.path.join(outpath, opt.file_name)
    elif opt.check_token_attn:
        sample_path = os.path.join(outpath, "check_token_attn_sample")
    # elif opt.file_name is not None:
    #     sample_path = os.path.join(outpath, opt.file_name)
    else:
        sample_path = os.path.join(outpath, "samples")

    os.makedirs(sample_path, exist_ok=True)
    base_count = len(os.listdir(sample_path))
    grid_count = len(os.listdir(outpath)) - 1

    start_code = None
    if opt.fixed_code:
        start_code = torch.randn([opt.n_samples, opt.C, opt.H // opt.f, opt.W // opt.f], device=device)

    precision_scope = autocast if opt.precision == "autocast" else nullcontext
    with torch.no_grad():
        with precision_scope("cuda"):
            with model.ema_scope():
                for prompts in tqdm(data, desc="data"):
                    all_samples = list()
                    for n in trange(opt.n_iter, desc="Sampling"):
                        # if model.controller is not None:
                        #     model.controller.between_steps()
                        print(prompts[0])
                        uc = None
                        timesteps = [(1+step*(int(1000/opt.ddim_steps))) for step in range(opt.ddim_steps)]
                        if opt.scale != 1.0:
                            for timestep in timesteps:
                                ts = torch.full((opt.n_samples,), timestep, device=device, dtype=torch.long)
                                uc = model.get_learned_conditioning(batch_size * [""], ts)
                        if isinstance(prompts, tuple):
                            prompts = list(prompts)
                        c = model.get_learned_conditioning(prompts)
                        shape = [opt.C, opt.H // opt.f, opt.W // opt.f]
                        if opt.diffusion_with_mask:
                            x0 = None
                            mask = None
                            # x0, mask = load_x0_and_mask(model)
                            # x0 = x0.cuda()
                            # mask = mask.cuda()
                        else:
                            x0 = None
                            mask = None
                        samples_ddim, _ = sampler.sample(S=opt.ddim_steps,
                                                         conditioning=c,
                                                         batch_size=opt.n_samples,
                                                         shape=shape,
                                                         verbose=False,
                                                         unconditional_guidance_scale=opt.scale,
                                                         unconditional_conditioning=uc,
                                                         eta=opt.ddim_eta,
                                                         x_T=start_code,
                                                         x0=x0,
                                                         mask=mask)
                        # print(samples_ddim.size())
                        x_samples_ddim = model.decode_first_stage(samples_ddim)
                        x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
                        x_samples_ddim = x_samples_ddim.cpu()

                        if not opt.skip_save:
                            for x_sample in x_samples_ddim:
                                x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                                img = Image.fromarray(x_sample.astype(np.uint8))
                                img.save(os.path.join(sample_path, f"{base_count:05}.png"))
                                base_count += 1
                            if opt.check_token_attn:
                                show_cross_attention(prompts, model.cond_stage_model.tokenizer, sample_path, base_count, model.controller, res=16, from_where=("up", "down"))
                                model.controller.reset()
                        if not opt.skip_grid:
                            all_samples.append(x_samples_ddim)

                    if not opt.skip_grid:
                        # 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)

                        # to image
                        grid = 255. * rearrange(grid, 'c h w -> h w c').cpu().numpy()
                        img = Image.fromarray(grid.astype(np.uint8))
                        sampling_method = 'plms' if opt.plms else 'ddim'
                        img.save(os.path.join(outpath, f'{prompts[0].replace(" ", "-")}_{opt.scale}_{sampling_method}_{opt.ddim_steps}_{opt.ddim_eta}.png'))
                        if opt.wandb_log:
                            wandb.log({  f'{prompts[0].replace(" ", "-")}_{opt.scale}_{sampling_method}_{opt.ddim_steps}_{opt.ddim_eta}.png'  : [wandb.Image(img)]})
                        grid_count += 1


    print(f"Your samples are ready and waiting for you here: \n{outpath} \n"
          f" \nEnjoy.")


if __name__ == "__main__":
    main()