mirror of
https://github.com/Sygil-Dev/sygil-webui.git
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010b27ce9a
* repo-merge * cutdown size * Create setup.py * webui.cmd * ldm * Update environment.yaml * Update environment.yaml
774 lines
34 KiB
Python
774 lines
34 KiB
Python
"""
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wild mixture of
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https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
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https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
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https://github.com/CompVis/taming-transformers
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-- merci
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"""
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import time
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import torch
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from einops import rearrange
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from tqdm import tqdm
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from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
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from ldm.models.autoencoder import VQModelInterface
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import torch.nn as nn
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import numpy as np
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import pytorch_lightning as pl
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from functools import partial
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from pytorch_lightning.utilities.distributed import rank_zero_only
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from ldm.util import exists, default, instantiate_from_config
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from ldm.modules.diffusionmodules.util import make_beta_schedule
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from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
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from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
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def disabled_train(self):
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"""Overwrite model.train with this function to make sure train/eval mode
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does not change anymore."""
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return self
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class DDPM(pl.LightningModule):
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# classic DDPM with Gaussian diffusion, in image space
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def __init__(self,
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timesteps=1000,
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beta_schedule="linear",
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ckpt_path=None,
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ignore_keys=[],
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load_only_unet=False,
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monitor="val/loss",
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use_ema=True,
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first_stage_key="image",
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image_size=256,
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channels=3,
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log_every_t=100,
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clip_denoised=True,
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linear_start=1e-4,
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linear_end=2e-2,
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cosine_s=8e-3,
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given_betas=None,
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original_elbo_weight=0.,
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v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
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l_simple_weight=1.,
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conditioning_key=None,
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parameterization="eps", # all assuming fixed variance schedules
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scheduler_config=None,
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use_positional_encodings=False,
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):
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super().__init__()
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assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
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self.parameterization = parameterization
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print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
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self.cond_stage_model = None
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self.clip_denoised = clip_denoised
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self.log_every_t = log_every_t
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self.first_stage_key = first_stage_key
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self.image_size = image_size # try conv?
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self.channels = channels
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self.use_positional_encodings = use_positional_encodings
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self.use_scheduler = scheduler_config is not None
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if self.use_scheduler:
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self.scheduler_config = scheduler_config
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self.v_posterior = v_posterior
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self.original_elbo_weight = original_elbo_weight
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self.l_simple_weight = l_simple_weight
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if monitor is not None:
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self.monitor = monitor
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
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self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
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linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
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def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
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linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
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if exists(given_betas):
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betas = given_betas
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else:
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betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
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cosine_s=cosine_s)
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alphas = 1. - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
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timesteps, = betas.shape
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self.num_timesteps = int(timesteps)
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self.linear_start = linear_start
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self.linear_end = linear_end
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assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
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to_torch = partial(torch.tensor, dtype=torch.float32)
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self.register_buffer('betas', to_torch(betas))
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self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
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self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
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class FirstStage(DDPM):
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"""main class"""
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def __init__(self,
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first_stage_config,
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num_timesteps_cond=None,
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cond_stage_key="image",
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cond_stage_trainable=False,
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concat_mode=True,
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cond_stage_forward=None,
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conditioning_key=None,
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scale_factor=1.0,
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scale_by_std=False,
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*args, **kwargs):
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self.num_timesteps_cond = default(num_timesteps_cond, 1)
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self.scale_by_std = scale_by_std
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assert self.num_timesteps_cond <= kwargs['timesteps']
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# for backwards compatibility after implementation of DiffusionWrapper
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if conditioning_key is None:
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conditioning_key = 'concat' if concat_mode else 'crossattn'
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ckpt_path = kwargs.pop("ckpt_path", None)
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ignore_keys = kwargs.pop("ignore_keys", [])
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super().__init__()
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self.concat_mode = concat_mode
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self.cond_stage_trainable = cond_stage_trainable
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self.cond_stage_key = cond_stage_key
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try:
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self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
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except:
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self.num_downs = 0
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if not scale_by_std:
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self.scale_factor = scale_factor
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self.instantiate_first_stage(first_stage_config)
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self.cond_stage_forward = cond_stage_forward
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self.clip_denoised = False
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self.bbox_tokenizer = None
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self.restarted_from_ckpt = False
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys)
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self.restarted_from_ckpt = True
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def instantiate_first_stage(self, config):
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model = instantiate_from_config(config)
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self.first_stage_model = model.eval()
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self.first_stage_model.train = disabled_train
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for param in self.first_stage_model.parameters():
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param.requires_grad = False
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def get_first_stage_encoding(self, encoder_posterior):
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if isinstance(encoder_posterior, DiagonalGaussianDistribution):
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z = encoder_posterior.sample()
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elif isinstance(encoder_posterior, torch.Tensor):
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z = encoder_posterior
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else:
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raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
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return self.scale_factor * z
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@torch.no_grad()
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def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
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if predict_cids:
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if z.dim() == 4:
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z = torch.argmax(z.exp(), dim=1).long()
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z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
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z = rearrange(z, 'b h w c -> b c h w').contiguous()
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z = 1. / self.scale_factor * z
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if hasattr(self, "split_input_params"):
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if isinstance(self.first_stage_model, VQModelInterface):
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return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
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else:
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return self.first_stage_model.decode(z)
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else:
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if isinstance(self.first_stage_model, VQModelInterface):
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return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
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else:
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return self.first_stage_model.decode(z)
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@torch.no_grad()
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def encode_first_stage(self, x):
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if hasattr(self, "split_input_params"):
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if self.split_input_params["patch_distributed_vq"]:
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ks = self.split_input_params["ks"] # eg. (128, 128)
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stride = self.split_input_params["stride"] # eg. (64, 64)
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df = self.split_input_params["vqf"]
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self.split_input_params['original_image_size'] = x.shape[-2:]
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bs, nc, h, w = x.shape
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if ks[0] > h or ks[1] > w:
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ks = (min(ks[0], h), min(ks[1], w))
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print("reducing Kernel")
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if stride[0] > h or stride[1] > w:
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stride = (min(stride[0], h), min(stride[1], w))
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print("reducing stride")
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fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
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z = unfold(x) # (bn, nc * prod(**ks), L)
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# Reshape to img shape
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z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
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output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
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for i in range(z.shape[-1])]
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o = torch.stack(output_list, axis=-1)
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o = o * weighting
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# Reverse reshape to img shape
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o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
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# stitch crops together
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decoded = fold(o)
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decoded = decoded / normalization
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return decoded
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else:
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return self.first_stage_model.encode(x)
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else:
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return self.first_stage_model.encode(x)
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class CondStage(DDPM):
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"""main class"""
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def __init__(self,
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cond_stage_config,
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num_timesteps_cond=None,
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cond_stage_key="image",
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cond_stage_trainable=False,
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concat_mode=True,
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cond_stage_forward=None,
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conditioning_key=None,
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scale_factor=1.0,
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scale_by_std=False,
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*args, **kwargs):
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self.num_timesteps_cond = default(num_timesteps_cond, 1)
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self.scale_by_std = scale_by_std
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assert self.num_timesteps_cond <= kwargs['timesteps']
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# for backwards compatibility after implementation of DiffusionWrapper
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if conditioning_key is None:
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conditioning_key = 'concat' if concat_mode else 'crossattn'
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if cond_stage_config == '__is_unconditional__':
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conditioning_key = None
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ckpt_path = kwargs.pop("ckpt_path", None)
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ignore_keys = kwargs.pop("ignore_keys", [])
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super().__init__()
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self.concat_mode = concat_mode
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self.cond_stage_trainable = cond_stage_trainable
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self.cond_stage_key = cond_stage_key
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self.num_downs = 0
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if not scale_by_std:
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self.scale_factor = scale_factor
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self.instantiate_cond_stage(cond_stage_config)
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self.cond_stage_forward = cond_stage_forward
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self.clip_denoised = False
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self.bbox_tokenizer = None
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self.restarted_from_ckpt = False
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys)
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self.restarted_from_ckpt = True
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def instantiate_cond_stage(self, config):
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if not self.cond_stage_trainable:
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if config == "__is_first_stage__":
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print("Using first stage also as cond stage.")
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self.cond_stage_model = self.first_stage_model
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elif config == "__is_unconditional__":
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print(f"Training {self.__class__.__name__} as an unconditional model.")
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self.cond_stage_model = None
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# self.be_unconditional = True
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else:
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model = instantiate_from_config(config)
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self.cond_stage_model = model.eval()
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self.cond_stage_model.train = disabled_train
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for param in self.cond_stage_model.parameters():
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param.requires_grad = False
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else:
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assert config != '__is_first_stage__'
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assert config != '__is_unconditional__'
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model = instantiate_from_config(config)
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self.cond_stage_model = model
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def get_learned_conditioning(self, c):
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if self.cond_stage_forward is None:
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if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
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c = self.cond_stage_model.encode(c)
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if isinstance(c, DiagonalGaussianDistribution):
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c = c.mode()
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else:
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c = self.cond_stage_model(c)
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else:
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assert hasattr(self.cond_stage_model, self.cond_stage_forward)
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c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
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return c
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class DiffusionWrapper(pl.LightningModule):
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def __init__(self, diff_model_config):
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super().__init__()
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self.diffusion_model = instantiate_from_config(diff_model_config)
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def forward(self, x, t, cc):
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out = self.diffusion_model(x, t, context=cc)
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return out
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class DiffusionWrapperOut(pl.LightningModule):
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def __init__(self, diff_model_config):
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super().__init__()
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self.diffusion_model = instantiate_from_config(diff_model_config)
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def forward(self, h,emb,tp,hs, cc):
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return self.diffusion_model(h,emb,tp,hs, context=cc)
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class UNet(DDPM):
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"""main class"""
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def __init__(self,
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unetConfigEncode,
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unetConfigDecode,
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num_timesteps_cond=None,
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cond_stage_key="image",
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cond_stage_trainable=False,
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concat_mode=True,
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cond_stage_forward=None,
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conditioning_key=None,
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scale_factor=1.0,
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unet_bs = 1,
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scale_by_std=False,
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*args, **kwargs):
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self.num_timesteps_cond = default(num_timesteps_cond, 1)
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self.scale_by_std = scale_by_std
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assert self.num_timesteps_cond <= kwargs['timesteps']
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# for backwards compatibility after implementation of DiffusionWrapper
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if conditioning_key is None:
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conditioning_key = 'concat' if concat_mode else 'crossattn'
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ckpt_path = kwargs.pop("ckpt_path", None)
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ignore_keys = kwargs.pop("ignore_keys", [])
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super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
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self.concat_mode = concat_mode
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self.cond_stage_trainable = cond_stage_trainable
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self.cond_stage_key = cond_stage_key
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self.num_downs = 0
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self.cdevice = "cuda"
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self.unetConfigEncode = unetConfigEncode
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self.unetConfigDecode = unetConfigDecode
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if not scale_by_std:
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self.scale_factor = scale_factor
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else:
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self.register_buffer('scale_factor', torch.tensor(scale_factor))
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self.cond_stage_forward = cond_stage_forward
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self.clip_denoised = False
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self.bbox_tokenizer = None
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self.model1 = DiffusionWrapper(self.unetConfigEncode)
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self.model2 = DiffusionWrapperOut(self.unetConfigDecode)
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self.model1.eval()
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self.model2.eval()
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self.turbo = False
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self.unet_bs = unet_bs
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self.restarted_from_ckpt = False
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if ckpt_path is not None:
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self.init_from_ckpt(ckpt_path, ignore_keys)
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self.restarted_from_ckpt = True
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def make_cond_schedule(self, ):
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self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
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ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
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self.cond_ids[:self.num_timesteps_cond] = ids
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@rank_zero_only
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@torch.no_grad()
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def on_train_batch_start(self, batch, batch_idx):
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# only for very first batch
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if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
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assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
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# set rescale weight to 1./std of encodings
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print("### USING STD-RESCALING ###")
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x = super().get_input(batch, self.first_stage_key)
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x = x.to(self.cdevice)
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encoder_posterior = self.encode_first_stage(x)
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z = self.get_first_stage_encoding(encoder_posterior).detach()
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del self.scale_factor
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self.register_buffer('scale_factor', 1. / z.flatten().std())
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print(f"setting self.scale_factor to {self.scale_factor}")
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print("### USING STD-RESCALING ###")
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def apply_model(self, x_noisy, t, cond, return_ids=False):
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if(not self.turbo):
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self.model1.to(self.cdevice)
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step = self.unet_bs
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h,emb,hs = self.model1(x_noisy[0:step], t[:step], cond[:step])
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bs = cond.shape[0]
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assert bs%2 == 0
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lenhs = len(hs)
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for i in range(step,bs,step):
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h_temp,emb_temp,hs_temp = self.model1(x_noisy[i:i+step], t[i:i+step], cond[i:i+step])
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h = torch.cat((h,h_temp))
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emb = torch.cat((emb,emb_temp))
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for j in range(lenhs):
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hs[j] = torch.cat((hs[j], hs_temp[j]))
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if(not self.turbo):
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self.model1.to("cpu")
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self.model2.to(self.cdevice)
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hs_temp = [hs[j][:step] for j in range(lenhs)]
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x_recon = self.model2(h[:step],emb[:step],x_noisy.dtype,hs_temp,cond[:step])
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for i in range(step,bs,step):
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hs_temp = [hs[j][i:i+step] for j in range(lenhs)]
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x_recon1 = self.model2(h[i:i+step],emb[i:i+step],x_noisy.dtype,hs_temp,cond[i:i+step])
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x_recon = torch.cat((x_recon, x_recon1))
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if(not self.turbo):
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self.model2.to("cpu")
|
|
|
|
if isinstance(x_recon, tuple) and not return_ids:
|
|
return x_recon[0]
|
|
else:
|
|
return x_recon
|
|
|
|
def register_buffer1(self, name, attr):
|
|
if type(attr) == torch.Tensor:
|
|
if attr.device != torch.device(self.cdevice):
|
|
attr = attr.to(torch.device(self.cdevice))
|
|
setattr(self, name, attr)
|
|
|
|
def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
|
|
|
|
|
|
self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
|
|
num_ddpm_timesteps=self.num_timesteps,verbose=verbose)
|
|
alphas_cumprod = self.alphas_cumprod
|
|
assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
|
|
to_torch = lambda x: x.to(self.cdevice)
|
|
|
|
self.register_buffer1('betas', to_torch(self.betas))
|
|
self.register_buffer1('alphas_cumprod', to_torch(alphas_cumprod))
|
|
self.register_buffer1('alphas_cumprod_prev', to_torch(self.alphas_cumprod_prev))
|
|
self.register_buffer1('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
|
|
|
|
# ddim sampling parameters
|
|
ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=self.alphas_cumprod.cpu(),
|
|
ddim_timesteps=self.ddim_timesteps,
|
|
eta=ddim_eta,verbose=verbose)
|
|
self.register_buffer1('ddim_sigmas', ddim_sigmas)
|
|
self.register_buffer1('ddim_alphas', ddim_alphas)
|
|
self.register_buffer1('ddim_alphas_prev', ddim_alphas_prev)
|
|
self.register_buffer1('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
|
|
self.ddim_sqrt_one_minus_alphas = np.sqrt(1. - ddim_alphas)
|
|
sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
|
|
(1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
|
|
1 - self.alphas_cumprod / self.alphas_cumprod_prev))
|
|
self.register_buffer1('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
|
|
|
|
@torch.no_grad()
|
|
def sample(self,
|
|
S,
|
|
batch_size,
|
|
shape,
|
|
seed,
|
|
conditioning=None,
|
|
callback=None,
|
|
img_callback=None,
|
|
quantize_x0=False,
|
|
eta=0.,
|
|
mask=None,
|
|
x0=None,
|
|
temperature=1.,
|
|
noise_dropout=0.,
|
|
score_corrector=None,
|
|
corrector_kwargs=None,
|
|
verbose=True,
|
|
x_T=None,
|
|
log_every_t=100,
|
|
unconditional_guidance_scale=1.,
|
|
unconditional_conditioning=None,
|
|
):
|
|
if conditioning is not None:
|
|
if isinstance(conditioning, dict):
|
|
cbs = conditioning[list(conditioning.keys())[0]].shape[0]
|
|
if cbs != batch_size:
|
|
print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
|
|
else:
|
|
if conditioning.shape[0] != batch_size:
|
|
print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
|
|
|
|
self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=False)
|
|
|
|
# sampling
|
|
C, H, W = shape
|
|
size = (batch_size, C, H, W)
|
|
print(f'Data shape for PLMS sampling is {size}')
|
|
|
|
if(self.turbo):
|
|
self.model1.to(self.cdevice)
|
|
self.model2.to(self.cdevice)
|
|
|
|
samples = self.plms_sampling(conditioning, size, seed,
|
|
callback=callback,
|
|
img_callback=img_callback,
|
|
quantize_denoised=quantize_x0,
|
|
mask=mask, x0=x0,
|
|
ddim_use_original_steps=False,
|
|
noise_dropout=noise_dropout,
|
|
temperature=temperature,
|
|
score_corrector=score_corrector,
|
|
corrector_kwargs=corrector_kwargs,
|
|
x_T=x_T,
|
|
log_every_t=log_every_t,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=unconditional_conditioning,
|
|
)
|
|
|
|
if(self.turbo):
|
|
self.model1.to("cpu")
|
|
self.model2.to("cpu")
|
|
|
|
return samples
|
|
|
|
@torch.no_grad()
|
|
def plms_sampling(self, cond, shape, seed,
|
|
x_T=None, ddim_use_original_steps=False,
|
|
callback=None, timesteps=None, quantize_denoised=False,
|
|
mask=None, x0=None, img_callback=None, log_every_t=100,
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
|
unconditional_guidance_scale=1., unconditional_conditioning=None,):
|
|
device = self.betas.device
|
|
b = shape[0]
|
|
if x_T is None:
|
|
_, b1, b2, b3 = shape
|
|
img_shape = (1, b1, b2, b3)
|
|
tens = []
|
|
print("seeds used = ", [seed+s for s in range(b)])
|
|
for _ in range(b):
|
|
torch.manual_seed(seed)
|
|
tens.append(torch.randn(img_shape, device=device))
|
|
seed+=1
|
|
img = torch.cat(tens)
|
|
del tens
|
|
else:
|
|
img = x_T
|
|
|
|
if timesteps is None:
|
|
timesteps = self.num_timesteps if ddim_use_original_steps else self.ddim_timesteps
|
|
elif timesteps is not None and not ddim_use_original_steps:
|
|
subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
|
|
timesteps = self.ddim_timesteps[:subset_end]
|
|
|
|
time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
|
|
total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
|
|
print(f"Running PLMS Sampling with {total_steps} timesteps")
|
|
|
|
iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
|
|
old_eps = []
|
|
|
|
for i, step in enumerate(iterator):
|
|
index = total_steps - i - 1
|
|
ts = torch.full((b,), step, device=device, dtype=torch.long)
|
|
ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
|
|
|
|
if mask is not None:
|
|
assert x0 is not None
|
|
img_orig = self.q_sample(x0, ts) # TODO: deterministic forward pass?
|
|
img = img_orig * mask + (1. - mask) * img
|
|
|
|
outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
|
|
quantize_denoised=quantize_denoised, temperature=temperature,
|
|
noise_dropout=noise_dropout, score_corrector=score_corrector,
|
|
corrector_kwargs=corrector_kwargs,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=unconditional_conditioning,
|
|
old_eps=old_eps, t_next=ts_next)
|
|
img, pred_x0, e_t = outs
|
|
old_eps.append(e_t)
|
|
if len(old_eps) >= 4:
|
|
old_eps.pop(0)
|
|
if callback: callback(i)
|
|
if img_callback: img_callback(pred_x0, i)
|
|
|
|
return img
|
|
|
|
@torch.no_grad()
|
|
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
|
unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
|
|
b, *_, device = *x.shape, x.device
|
|
|
|
def get_model_output(x, t):
|
|
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
|
e_t = self.apply_model(x, t, c)
|
|
else:
|
|
x_in = torch.cat([x] * 2)
|
|
t_in = torch.cat([t] * 2)
|
|
c_in = torch.cat([unconditional_conditioning, c])
|
|
e_t_uncond, e_t = self.apply_model(x_in, t_in, c_in).chunk(2)
|
|
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
|
|
|
if score_corrector is not None:
|
|
assert self.parameterization == "eps"
|
|
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
|
|
|
return e_t
|
|
|
|
alphas = self.alphas_cumprod if use_original_steps else self.ddim_alphas
|
|
alphas_prev = self.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
|
sqrt_one_minus_alphas = self.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
|
sigmas = self.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
|
|
|
def get_x_prev_and_pred_x0(e_t, index):
|
|
# select parameters corresponding to the currently considered timestep
|
|
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
|
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
|
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
|
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
|
|
|
# current prediction for x_0
|
|
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
|
if quantize_denoised:
|
|
pred_x0, _, *_ = self.first_stage_model.quantize(pred_x0)
|
|
# direction pointing to x_t
|
|
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
|
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
|
if noise_dropout > 0.:
|
|
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
|
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
|
return x_prev, pred_x0
|
|
|
|
e_t = get_model_output(x, t)
|
|
if len(old_eps) == 0:
|
|
# Pseudo Improved Euler (2nd order)
|
|
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
|
|
e_t_next = get_model_output(x_prev, t_next)
|
|
e_t_prime = (e_t + e_t_next) / 2
|
|
elif len(old_eps) == 1:
|
|
# 2nd order Pseudo Linear Multistep (Adams-Bashforth)
|
|
e_t_prime = (3 * e_t - old_eps[-1]) / 2
|
|
elif len(old_eps) == 2:
|
|
# 3nd order Pseudo Linear Multistep (Adams-Bashforth)
|
|
e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
|
|
elif len(old_eps) >= 3:
|
|
# 4nd order Pseudo Linear Multistep (Adams-Bashforth)
|
|
e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
|
|
|
|
x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
|
|
|
|
return x_prev, pred_x0, e_t
|
|
|
|
|
|
@torch.no_grad()
|
|
def stochastic_encode(self, x0, t, seed, ddim_eta,ddim_steps,use_original_steps=False, noise=None, mask=None):
|
|
# fast, but does not allow for exact reconstruction
|
|
# t serves as an index to gather the correct alphas
|
|
self.make_schedule(ddim_num_steps=ddim_steps, ddim_eta=ddim_eta, verbose=False)
|
|
|
|
if use_original_steps:
|
|
sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
|
|
sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
|
|
else:
|
|
sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
|
|
sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
|
|
|
|
if noise is None:
|
|
b0, b1, b2, b3 = x0.shape
|
|
img_shape = (1, b1, b2, b3)
|
|
tens = []
|
|
print("seeds used = ", [seed+s for s in range(b0)])
|
|
for _ in range(b0):
|
|
torch.manual_seed(seed)
|
|
tens.append(torch.randn(img_shape, device=x0.device))
|
|
seed+=1
|
|
noise = torch.cat(tens)
|
|
del tens
|
|
if mask is not None:
|
|
noise = noise*mask
|
|
return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
|
|
extract_into_tensor(sqrt_one_minus_alphas_cumprod.to(self.cdevice), t, x0.shape) * noise)
|
|
|
|
@torch.no_grad()
|
|
def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
|
|
mask = None,use_original_steps=False):
|
|
|
|
|
|
if(self.turbo):
|
|
self.model1.to(self.cdevice)
|
|
self.model2.to(self.cdevice)
|
|
|
|
timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
|
|
timesteps = timesteps[:t_start]
|
|
|
|
time_range = np.flip(timesteps)
|
|
total_steps = timesteps.shape[0]
|
|
print(f"Running DDIM Sampling with {total_steps} timesteps")
|
|
|
|
iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
|
|
x_dec = x_latent
|
|
# x0 = x_latent
|
|
for i, step in enumerate(iterator):
|
|
index = total_steps - i - 1
|
|
ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
|
|
|
|
|
|
# if mask is not None:
|
|
# x_dec = x0 * mask + (1. - mask) * x_dec
|
|
|
|
x_dec = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
|
|
unconditional_guidance_scale=unconditional_guidance_scale,
|
|
unconditional_conditioning=unconditional_conditioning)
|
|
# if mask is not None:
|
|
# return x0 * mask + (1. - mask) * x_dec
|
|
|
|
if(self.turbo):
|
|
self.model1.to("cpu")
|
|
self.model2.to("cpu")
|
|
|
|
return x_dec
|
|
|
|
@torch.no_grad()
|
|
def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
|
|
temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
|
|
unconditional_guidance_scale=1., unconditional_conditioning=None):
|
|
b, *_, device = *x.shape, x.device
|
|
|
|
if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
|
|
e_t = self.apply_model(x, t, c)
|
|
else:
|
|
x_in = torch.cat([x] * 2)
|
|
t_in = torch.cat([t] * 2)
|
|
c_in = torch.cat([unconditional_conditioning, c])
|
|
# print("xin shape = ", x_in.shape)
|
|
e_t_uncond, e_t = self.apply_model(x_in, t_in, c_in).chunk(2)
|
|
e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
|
|
|
|
if score_corrector is not None:
|
|
assert self.model.parameterization == "eps"
|
|
e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
|
|
|
|
alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
|
|
alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
|
|
sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
|
|
sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
|
|
# select parameters corresponding to the currently considered timestep
|
|
a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
|
|
a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
|
|
sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
|
|
sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
|
|
|
|
# current prediction for x_0
|
|
pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
|
|
if quantize_denoised:
|
|
pred_x0, _, *_ = self.first_stage_model.quantize(pred_x0)
|
|
# direction pointing to x_t
|
|
dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
|
|
noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
|
|
if noise_dropout > 0.:
|
|
noise = torch.nn.functional.dropout(noise, p=noise_dropout)
|
|
x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
|
|
return x_prev |