notebook_helpers.py

import ipywidgets as widgets
import os
import time
import torch
import torch
import torchvision
from IPython.display import Image as ipyimg
from PIL import Image
from einops import rearrange, repeat
# todo ?
from google.colab import files
from numpy import asarray
from omegaconf import OmegaConf
from torchvision.datasets.utils import download_url

from ldm.models.diffusion.ddim import DDIMSampler
from ldm.util import instantiate_from_config
from ldm.util import ismap


def download_models(mode):
    if mode == "superresolution":
        # this is the small bsr light model
        url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'
        url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'

        path_conf = 'logs/diffusion/superresolution_bsr/configs/project.yaml'
        path_ckpt = 'logs/diffusion/superresolution_bsr/checkpoints/last.ckpt'

        download_url(url_conf, path_conf)
        download_url(url_ckpt, path_ckpt)

        path_conf = path_conf + '/?dl=1'  # fix it
        path_ckpt = path_ckpt + '/?dl=1'  # fix it
        return path_conf, path_ckpt

    else:
        raise NotImplementedError


def load_model_from_config(config, ckpt):
    print(f"Loading model from {ckpt}")
    pl_sd = torch.load(ckpt, map_location="cpu")
    global_step = pl_sd["global_step"]
    sd = pl_sd["state_dict"]
    model = instantiate_from_config(config.model)
    m, u = model.load_state_dict(sd, strict=False)
    model.cuda()
    model.eval()
    return {"model": model}, global_step


def get_model(mode):
    path_conf, path_ckpt = download_models(mode)
    config = OmegaConf.load(path_conf)
    model, step = load_model_from_config(config, path_ckpt)
    return model


def get_custom_cond(mode):
    dest = "data/example_conditioning"

    if mode == "superresolution":
        uploaded_img = files.upload()
        filename = next(iter(uploaded_img))
        name, filetype = filename.split(".")  # todo assumes just one dot in name !
        os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")

    elif mode == "text_conditional":
        w = widgets.Text(value='A cake with cream!', disabled=True)
        display(w)

        with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
            f.write(w.value)

    elif mode == "class_conditional":
        w = widgets.IntSlider(min=0, max=1000)
        display(w)
        with open(f"{dest}/{mode}/custom.txt", 'w') as f:
            f.write(w.value)

    else:
        raise NotImplementedError(f"cond not implemented for mode{mode}")


def get_cond_options(mode):
    path = "data/example_conditioning"
    path = os.path.join(path, mode)
    onlyfiles = [f for f in sorted(os.listdir(path))]
    return path, onlyfiles


def select_cond_path(mode):
    path = "data/example_conditioning"  # todo
    path = os.path.join(path, mode)
    onlyfiles = [f for f in sorted(os.listdir(path))]

    selected = widgets.RadioButtons(
        options=onlyfiles,
        description='Select conditioning:',
        disabled=False
    )
    display(selected)
    selected_path = os.path.join(path, selected.value)
    return selected_path


def get_cond(mode, selected_path):
    example = dict()
    if mode == "superresolution":
        up_f = 4
        visualize_cond_img(selected_path)

        c = Image.open(selected_path)
        c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
        c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], antialias=True)
        c_up = rearrange(c_up, '1 c h w -> 1 h w c')
        c = rearrange(c, '1 c h w -> 1 h w c')
        c = 2. * c - 1.

        c = c.to(torch.device("cuda"))
        example["LR_image"] = c
        example["image"] = c_up

    return example


def visualize_cond_img(path):
    display(ipyimg(filename=path))


def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None):
    example = get_cond(task, selected_path)

    save_intermediate_vid = False
    n_runs = 1
    masked = False
    guider = None
    ckwargs = None
    mode = 'ddim'
    ddim_use_x0_pred = False
    temperature = 1.
    eta = 1.
    make_progrow = True
    custom_shape = None

    height, width = example["image"].shape[1:3]
    split_input = height >= 128 and width >= 128

    if split_input:
        ks = 128
        stride = 64
        vqf = 4  #
        model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
                                    "vqf": vqf,
                                    "patch_distributed_vq": True,
                                    "tie_braker": False,
                                    "clip_max_weight": 0.5,
                                    "clip_min_weight": 0.01,
                                    "clip_max_tie_weight": 0.5,
                                    "clip_min_tie_weight": 0.01}
    else:
        if hasattr(model, "split_input_params"):
            delattr(model, "split_input_params")

    invert_mask = False

    x_T = None
    for n in range(n_runs):
        if custom_shape is not None:
            x_T = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
            x_T = repeat(x_T, '1 c h w -> b c h w', b=custom_shape[0])

        logs = make_convolutional_sample(example, model,
                                         mode=mode, custom_steps=custom_steps,
                                         eta=eta, swap_mode=False, masked=masked,
                                         invert_mask=invert_mask, quantize_x0=False,
                                         custom_schedule=None, decode_interval=10,
                                         resize_enabled=resize_enabled, custom_shape=custom_shape,
                                         temperature=temperature, noise_dropout=0.,
                                         corrector=guider, corrector_kwargs=ckwargs, x_T=x_T,
                                         save_intermediate_vid=save_intermediate_vid,
                                         make_progrow=make_progrow, ddim_use_x0_pred=ddim_use_x0_pred
                                         )
    return logs


@torch.no_grad()
def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
                    mask=None, x0=None, quantize_x0=False, img_callback=None,
                    temperature=1., noise_dropout=0., score_corrector=None,
                    corrector_kwargs=None, x_T=None, log_every_t=None
                    ):
    ddim = DDIMSampler(model)
    bs = shape[0]  # dont know where this comes from but wayne
    shape = shape[1:]  # cut batch dim
    print(f"Sampling with eta = {eta}; steps: {steps}")
    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
                                         score_corrector=score_corrector,
                                         corrector_kwargs=corrector_kwargs, x_T=x_T)

    return samples, intermediates


@torch.no_grad()
def make_convolutional_sample(batch, model, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False,
                              invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
                              resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
                              corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,
                              ddim_use_x0_pred=False):
    log = dict()

    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
                                        return_first_stage_outputs=True,
                                        force_c_encode=not (hasattr(model, 'split_input_params')
                                                            and model.cond_stage_key == 'coordinates_bbox'),
                                        return_original_cond=True)

    log_every_t = 1 if save_intermediate_vid else None

    if custom_shape is not None:
        z = torch.randn(custom_shape)
        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")

    z0 = None

    log["input"] = x
    log["reconstruction"] = xrec

    if ismap(xc):
        log["original_conditioning"] = model.to_rgb(xc)
        if hasattr(model, 'cond_stage_key'):
            log[model.cond_stage_key] = model.to_rgb(xc)

    else:
        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
        if model.cond_stage_model:
            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
            if model.cond_stage_key == 'class_label':
                log[model.cond_stage_key] = xc[model.cond_stage_key]

    with model.ema_scope("Plotting"):
        t0 = time.time()
        img_cb = None

        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
                                                eta=eta,
                                                quantize_x0=quantize_x0, img_callback=img_cb, mask=None, x0=z0,
                                                temperature=temperature, noise_dropout=noise_dropout,
                                                score_corrector=corrector, corrector_kwargs=corrector_kwargs,
                                                x_T=x_T, log_every_t=log_every_t)
        t1 = time.time()

        if ddim_use_x0_pred:
            sample = intermediates['pred_x0'][-1]

    x_sample = model.decode_first_stage(sample)

    try:
        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
        log["sample_noquant"] = x_sample_noquant
        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
    except:
        pass

    log["sample"] = x_sample
    log["time"] = t1 - t0

    return log