276 lines
11 KiB
Python
276 lines
11 KiB
Python
import torch
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import os
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from tqdm import tqdm
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from PIL import Image, ImageDraw ,ImageFont
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from matplotlib import pyplot as plt
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import torchvision.transforms as T
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import os
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import yaml
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import numpy as np
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def load_512(image_path, left=0, right=0, top=0, bottom=0, device=None):
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if type(image_path) is str:
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image = np.array(Image.open(image_path).convert('RGB'))[:, :, :3]
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else:
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image = image_path
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h, w, c = image.shape
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left = min(left, w-1)
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right = min(right, w - left - 1)
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top = min(top, h - left - 1)
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bottom = min(bottom, h - top - 1)
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image = image[top:h-bottom, left:w-right]
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h, w, c = image.shape
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if h < w:
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offset = (w - h) // 2
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image = image[:, offset:offset + h]
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elif w < h:
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offset = (h - w) // 2
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image = image[offset:offset + w]
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image = np.array(Image.fromarray(image).resize((512, 512)))
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image = torch.from_numpy(image).float() / 127.5 - 1
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image = image.permute(2, 0, 1).unsqueeze(0).to(device, dtype =torch.float32)
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return image
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def mu_tilde(model, xt,x0, timestep):
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"mu_tilde(x_t, x_0) DDPM paper eq. 7"
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prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
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alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
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alpha_t = model.scheduler.alphas[timestep]
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beta_t = 1 - alpha_t
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alpha_bar = model.scheduler.alphas_cumprod[timestep]
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return ((alpha_prod_t_prev ** 0.5 * beta_t) / (1-alpha_bar)) * x0 + ((alpha_t**0.5 *(1-alpha_prod_t_prev)) / (1- alpha_bar))*xt
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def sample_xts_from_x0(model, x0, num_inference_steps=50):
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"""
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Samples from P(x_1:T|x_0)
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"""
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# torch.manual_seed(43256465436)
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alpha_bar = model.scheduler.alphas_cumprod
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sqrt_one_minus_alpha_bar = (1-alpha_bar) ** 0.5
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alphas = model.scheduler.alphas
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betas = 1 - alphas
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variance_noise_shape = (
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num_inference_steps,
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model.unet.in_channels,
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model.unet.sample_size,
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model.unet.sample_size)
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timesteps = model.scheduler.timesteps.to(model.device)
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t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
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xts = torch.zeros(variance_noise_shape).to(x0.device, dtype =torch.float16)
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for t in reversed(timesteps):
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idx = t_to_idx[int(t)]
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xts[idx] = x0 * (alpha_bar[t] ** 0.5) + torch.randn_like(x0, dtype =torch.float16) * sqrt_one_minus_alpha_bar[t]
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xts = torch.cat([xts, x0 ],dim = 0)
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return xts
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def encode_text(model, prompts):
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text_input = model.tokenizer(
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prompts,
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padding="max_length",
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max_length=model.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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with torch.no_grad():
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text_encoding = model.text_encoder(text_input.input_ids.to(model.device))[0]
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return text_encoding
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def forward_step(model, model_output, timestep, sample):
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next_timestep = min(model.scheduler.config.num_train_timesteps - 2,
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timestep + model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps)
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# 2. compute alphas, betas
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alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
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# alpha_prod_t_next = self.scheduler.alphas_cumprod[next_timestep] if next_ltimestep >= 0 else self.scheduler.final_alpha_cumprod
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beta_prod_t = 1 - alpha_prod_t
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# 3. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
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# 5. TODO: simple noising implementatiom
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next_sample = model.scheduler.add_noise(pred_original_sample,
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model_output,
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torch.LongTensor([next_timestep]))
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return next_sample
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def get_variance(model, timestep): #, prev_timestep):
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prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
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alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
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alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
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beta_prod_t = 1 - alpha_prod_t
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beta_prod_t_prev = 1 - alpha_prod_t_prev
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variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
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return variance
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def inversion_forward_process(model, x0,
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etas = None,
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prog_bar = False,
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prompt = "",
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cfg_scale = 3.5,
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num_inference_steps=50, eps = None):
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if not prompt=="":
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text_embeddings = encode_text(model, prompt)
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uncond_embedding = encode_text(model, "")
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timesteps = model.scheduler.timesteps.to(model.device)
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variance_noise_shape = (
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num_inference_steps,
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model.unet.in_channels,
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model.unet.sample_size,
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model.unet.sample_size)
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if etas is None or (type(etas) in [int, float] and etas == 0):
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eta_is_zero = True
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zs = None
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else:
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eta_is_zero = False
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if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
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xts = sample_xts_from_x0(model, x0, num_inference_steps=num_inference_steps)
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alpha_bar = model.scheduler.alphas_cumprod
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zs = torch.zeros(size=variance_noise_shape, device=model.device, dtype =torch.float16)
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t_to_idx = {int(v):k for k,v in enumerate(timesteps)}
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xt = x0
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op = tqdm(reversed(timesteps), desc= "Inverting...") if prog_bar else reversed(timesteps)
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for t in op:
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idx = t_to_idx[int(t)]
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# 1. predict noise residual
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if not eta_is_zero:
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xt = xts[idx][None]
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with torch.no_grad():
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out = model.unet.forward(xt, timestep = t, encoder_hidden_states = uncond_embedding)
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if not prompt=="":
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cond_out = model.unet.forward(xt, timestep=t, encoder_hidden_states = text_embeddings)
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if not prompt=="":
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## classifier free guidance
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noise_pred = out.sample + cfg_scale * (cond_out.sample - out.sample)
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else:
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noise_pred = out.sample
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if eta_is_zero:
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# 2. compute more noisy image and set x_t -> x_t+1
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xt = forward_step(model, noise_pred, t, xt)
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else:
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xtm1 = xts[idx+1][None]
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# pred of x0
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pred_original_sample = (xt - (1-alpha_bar[t]) ** 0.5 * noise_pred ) / alpha_bar[t] ** 0.5
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# direction to xt
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prev_timestep = t - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
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alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
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variance = get_variance(model, t)
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pred_sample_direction = (1 - alpha_prod_t_prev - etas[idx] * variance ) ** (0.5) * noise_pred
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mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
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z = (xtm1 - mu_xt ) / ( etas[idx] * variance ** 0.5 )
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zs[idx] = z
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# correction to avoid error accumulation
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xtm1 = mu_xt + ( etas[idx] * variance ** 0.5 )*z
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xts[idx+1] = xtm1
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if not zs is None:
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zs[-1] = torch.zeros_like(zs[-1])
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return xt, zs, xts
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def reverse_step(model, model_output, timestep, sample, eta = 0, variance_noise=None):
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# 1. get previous step value (=t-1)
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prev_timestep = timestep - model.scheduler.config.num_train_timesteps // model.scheduler.num_inference_steps
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# 2. compute alphas, betas
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alpha_prod_t = model.scheduler.alphas_cumprod[timestep]
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alpha_prod_t_prev = model.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else model.scheduler.final_alpha_cumprod
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beta_prod_t = 1 - alpha_prod_t
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# 3. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
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# 5. compute variance: "sigma_t(η)" -> see formula (16)
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# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
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# variance = self.scheduler._get_variance(timestep, prev_timestep)
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variance = get_variance(model, timestep) #, prev_timestep)
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std_dev_t = eta * variance ** (0.5)
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# Take care of asymetric reverse process (asyrp)
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model_output_direction = model_output
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# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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# pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
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pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * model_output_direction
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# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
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# 8. Add noice if eta > 0
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if eta > 0:
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if variance_noise is None:
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variance_noise = torch.randn(model_output.shape, device=model.device, dtype =torch.float16)
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sigma_z = eta * variance ** (0.5) * variance_noise
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prev_sample = prev_sample + sigma_z
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return prev_sample
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def inversion_reverse_process(model,
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xT,
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etas = 0,
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prompts = "",
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cfg_scales = None,
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prog_bar = False,
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zs = None,
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controller=None,
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asyrp = False):
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batch_size = len(prompts)
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cfg_scales_tensor = torch.Tensor(cfg_scales).view(-1,1,1,1).to(model.device, dtype=torch.float16)
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text_embeddings = encode_text(model, prompts)
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uncond_embedding = encode_text(model, [""] * batch_size)
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if etas is None: etas = 0
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if type(etas) in [int, float]: etas = [etas]*model.scheduler.num_inference_steps
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assert len(etas) == model.scheduler.num_inference_steps
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timesteps = model.scheduler.timesteps.to(model.device)
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xt = xT.expand(batch_size, -1, -1, -1)
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op = tqdm(timesteps[-zs.shape[0]:]) if prog_bar else timesteps[-zs.shape[0]:]
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t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
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for t in op:
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idx = t_to_idx[int(t)]
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## Unconditional embedding
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with torch.no_grad():
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uncond_out = model.unet.forward(xt, timestep = t,
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encoder_hidden_states = uncond_embedding)
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## Conditional embedding
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if prompts:
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with torch.no_grad():
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cond_out = model.unet.forward(xt, timestep = t,
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encoder_hidden_states = text_embeddings)
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z = zs[idx] if not zs is None else None
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z = z.expand(batch_size, -1, -1, -1)
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if prompts:
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## classifier free guidance
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noise_pred = uncond_out.sample + cfg_scales_tensor * (cond_out.sample - uncond_out.sample)
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else:
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noise_pred = uncond_out.sample
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# 2. compute less noisy image and set x_t -> x_t-1
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xt = reverse_step(model, noise_pred, t, xt, eta = etas[idx], variance_noise = z)
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if controller is not None:
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xt = controller.step_callback(xt)
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return xt, zs
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