rename to iopaint
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Qing
336
iopaint/model/ldm.py
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336
iopaint/model/ldm.py
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import os
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import numpy as np
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import torch
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from loguru import logger
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from iopaint.model.base import InpaintModel
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from iopaint.model.ddim_sampler import DDIMSampler
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from iopaint.model.plms_sampler import PLMSSampler
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from iopaint.schema import InpaintRequest, LDMSampler
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torch.manual_seed(42)
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import torch.nn as nn
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from iopaint.helper import (
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download_model,
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norm_img,
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get_cache_path_by_url,
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load_jit_model,
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)
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from iopaint.model.utils import (
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make_beta_schedule,
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timestep_embedding,
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)
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LDM_ENCODE_MODEL_URL = os.environ.get(
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"LDM_ENCODE_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_encode.pt",
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)
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LDM_ENCODE_MODEL_MD5 = os.environ.get(
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"LDM_ENCODE_MODEL_MD5", "23239fc9081956a3e70de56472b3f296"
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)
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LDM_DECODE_MODEL_URL = os.environ.get(
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"LDM_DECODE_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/add_ldm/cond_stage_model_decode.pt",
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)
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LDM_DECODE_MODEL_MD5 = os.environ.get(
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"LDM_DECODE_MODEL_MD5", "fe419cd15a750d37a4733589d0d3585c"
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)
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LDM_DIFFUSION_MODEL_URL = os.environ.get(
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"LDM_DIFFUSION_MODEL_URL",
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"https://github.com/Sanster/models/releases/download/add_ldm/diffusion.pt",
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)
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LDM_DIFFUSION_MODEL_MD5 = os.environ.get(
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"LDM_DIFFUSION_MODEL_MD5", "b0afda12bf790c03aba2a7431f11d22d"
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)
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class DDPM(nn.Module):
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# classic DDPM with Gaussian diffusion, in image space
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def __init__(
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self,
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device,
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timesteps=1000,
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beta_schedule="linear",
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linear_start=0.0015,
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linear_end=0.0205,
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cosine_s=0.008,
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original_elbo_weight=0.0,
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v_posterior=0.0, # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
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l_simple_weight=1.0,
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parameterization="eps", # all assuming fixed variance schedules
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use_positional_encodings=False,
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):
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super().__init__()
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self.device = device
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self.parameterization = parameterization
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self.use_positional_encodings = use_positional_encodings
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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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self.register_schedule(
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beta_schedule=beta_schedule,
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timesteps=timesteps,
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linear_start=linear_start,
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linear_end=linear_end,
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cosine_s=cosine_s,
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)
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def register_schedule(
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self,
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given_betas=None,
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beta_schedule="linear",
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timesteps=1000,
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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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):
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betas = make_beta_schedule(
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self.device,
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beta_schedule,
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timesteps,
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linear_start=linear_start,
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linear_end=linear_end,
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cosine_s=cosine_s,
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)
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alphas = 1.0 - betas
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alphas_cumprod = np.cumprod(alphas, axis=0)
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alphas_cumprod_prev = np.append(1.0, 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 (
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alphas_cumprod.shape[0] == self.num_timesteps
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), "alphas have to be defined for each timestep"
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to_torch = lambda x: torch.tensor(x, dtype=torch.float32).to(self.device)
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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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# calculations for diffusion q(x_t | x_{t-1}) and others
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self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
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self.register_buffer(
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"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
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)
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self.register_buffer(
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"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
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)
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# calculations for posterior q(x_{t-1} | x_t, x_0)
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posterior_variance = (1 - self.v_posterior) * betas * (
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1.0 - alphas_cumprod_prev
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) / (1.0 - alphas_cumprod) + self.v_posterior * betas
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# above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
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self.register_buffer("posterior_variance", to_torch(posterior_variance))
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# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
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self.register_buffer(
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"posterior_log_variance_clipped",
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to_torch(np.log(np.maximum(posterior_variance, 1e-20))),
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)
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self.register_buffer(
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"posterior_mean_coef1",
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to_torch(betas * np.sqrt(alphas_cumprod_prev) / (1.0 - alphas_cumprod)),
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)
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self.register_buffer(
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"posterior_mean_coef2",
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to_torch(
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(1.0 - alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - alphas_cumprod)
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),
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)
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if self.parameterization == "eps":
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lvlb_weights = self.betas**2 / (
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2
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* self.posterior_variance
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* to_torch(alphas)
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* (1 - self.alphas_cumprod)
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)
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elif self.parameterization == "x0":
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lvlb_weights = (
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0.5
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* np.sqrt(torch.Tensor(alphas_cumprod))
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/ (2.0 * 1 - torch.Tensor(alphas_cumprod))
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)
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else:
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raise NotImplementedError("mu not supported")
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# TODO how to choose this term
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lvlb_weights[0] = lvlb_weights[1]
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self.register_buffer("lvlb_weights", lvlb_weights, persistent=False)
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assert not torch.isnan(self.lvlb_weights).all()
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class LatentDiffusion(DDPM):
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def __init__(
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self,
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diffusion_model,
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device,
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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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scale_factor=1.0,
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scale_by_std=False,
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*args,
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**kwargs,
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):
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self.num_timesteps_cond = 1
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self.scale_by_std = scale_by_std
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super().__init__(device, *args, **kwargs)
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self.diffusion_model = diffusion_model
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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 = 2
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self.scale_factor = scale_factor
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def make_cond_schedule(
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self,
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):
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self.cond_ids = torch.full(
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size=(self.num_timesteps,),
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fill_value=self.num_timesteps - 1,
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dtype=torch.long,
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)
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ids = torch.round(
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torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)
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).long()
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self.cond_ids[: self.num_timesteps_cond] = ids
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def register_schedule(
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self,
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given_betas=None,
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beta_schedule="linear",
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timesteps=1000,
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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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):
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super().register_schedule(
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given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s
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)
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self.shorten_cond_schedule = self.num_timesteps_cond > 1
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if self.shorten_cond_schedule:
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self.make_cond_schedule()
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def apply_model(self, x_noisy, t, cond):
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# x_recon = self.model(x_noisy, t, cond['c_concat'][0]) # cond['c_concat'][0].shape 1,4,128,128
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t_emb = timestep_embedding(x_noisy.device, t, 256, repeat_only=False)
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x_recon = self.diffusion_model(x_noisy, t_emb, cond)
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return x_recon
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class LDM(InpaintModel):
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name = "ldm"
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pad_mod = 32
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is_erase_model = True
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def __init__(self, device, fp16: bool = True, **kwargs):
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self.fp16 = fp16
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super().__init__(device)
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self.device = device
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def init_model(self, device, **kwargs):
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self.diffusion_model = load_jit_model(
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LDM_DIFFUSION_MODEL_URL, device, LDM_DIFFUSION_MODEL_MD5
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)
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self.cond_stage_model_decode = load_jit_model(
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LDM_DECODE_MODEL_URL, device, LDM_DECODE_MODEL_MD5
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)
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self.cond_stage_model_encode = load_jit_model(
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LDM_ENCODE_MODEL_URL, device, LDM_ENCODE_MODEL_MD5
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)
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if self.fp16 and "cuda" in str(device):
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self.diffusion_model = self.diffusion_model.half()
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self.cond_stage_model_decode = self.cond_stage_model_decode.half()
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self.cond_stage_model_encode = self.cond_stage_model_encode.half()
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self.model = LatentDiffusion(self.diffusion_model, device)
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@staticmethod
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def download():
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download_model(LDM_DIFFUSION_MODEL_URL, LDM_DIFFUSION_MODEL_MD5)
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download_model(LDM_DECODE_MODEL_URL, LDM_DECODE_MODEL_MD5)
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download_model(LDM_ENCODE_MODEL_URL, LDM_ENCODE_MODEL_MD5)
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@staticmethod
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def is_downloaded() -> bool:
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model_paths = [
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get_cache_path_by_url(LDM_DIFFUSION_MODEL_URL),
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get_cache_path_by_url(LDM_DECODE_MODEL_URL),
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get_cache_path_by_url(LDM_ENCODE_MODEL_URL),
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]
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return all([os.path.exists(it) for it in model_paths])
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@torch.cuda.amp.autocast()
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def forward(self, image, mask, config: InpaintRequest):
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"""
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image: [H, W, C] RGB
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mask: [H, W, 1]
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return: BGR IMAGE
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"""
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# image [1,3,512,512] float32
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# mask: [1,1,512,512] float32
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# masked_image: [1,3,512,512] float32
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if config.ldm_sampler == LDMSampler.ddim:
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sampler = DDIMSampler(self.model)
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elif config.ldm_sampler == LDMSampler.plms:
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sampler = PLMSSampler(self.model)
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else:
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raise ValueError()
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steps = config.ldm_steps
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image = norm_img(image)
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mask = norm_img(mask)
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mask[mask < 0.5] = 0
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mask[mask >= 0.5] = 1
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image = torch.from_numpy(image).unsqueeze(0).to(self.device)
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mask = torch.from_numpy(mask).unsqueeze(0).to(self.device)
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masked_image = (1 - mask) * image
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mask = self._norm(mask)
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masked_image = self._norm(masked_image)
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c = self.cond_stage_model_encode(masked_image)
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torch.cuda.empty_cache()
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cc = torch.nn.functional.interpolate(mask, size=c.shape[-2:]) # 1,1,128,128
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c = torch.cat((c, cc), dim=1) # 1,4,128,128
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shape = (c.shape[1] - 1,) + c.shape[2:]
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samples_ddim = sampler.sample(
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steps=steps, conditioning=c, batch_size=c.shape[0], shape=shape
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)
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torch.cuda.empty_cache()
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x_samples_ddim = self.cond_stage_model_decode(
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samples_ddim
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) # samples_ddim: 1, 3, 128, 128 float32
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torch.cuda.empty_cache()
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# image = torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
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# mask = torch.clamp((mask + 1.0) / 2.0, min=0.0, max=1.0)
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inpainted_image = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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# inpainted = (1 - mask) * image + mask * predicted_image
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inpainted_image = inpainted_image.cpu().numpy().transpose(0, 2, 3, 1)[0] * 255
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inpainted_image = inpainted_image.astype(np.uint8)[:, :, ::-1]
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return inpainted_image
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def _norm(self, tensor):
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return tensor * 2.0 - 1.0
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