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A10G
| """SAMPLING ONLY.""" | |
| # CrossAttn precision handling | |
| import os | |
| import einops | |
| import numpy as np | |
| import torch | |
| from tqdm import tqdm | |
| from ControlNet.ldm.modules.diffusionmodules.util import ( | |
| extract_into_tensor, make_ddim_sampling_parameters, make_ddim_timesteps, | |
| noise_like) | |
| _ATTN_PRECISION = os.environ.get('ATTN_PRECISION', 'fp32') | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| def register_attention_control(model, controller=None): | |
| def ca_forward(self, place_in_unet): | |
| def forward(x, context=None, mask=None): | |
| h = self.heads | |
| q = self.to_q(x) | |
| is_cross = context is not None | |
| context = context if is_cross else x | |
| context = controller(context, is_cross, place_in_unet) | |
| k = self.to_k(context) | |
| v = self.to_v(context) | |
| q, k, v = map( | |
| lambda t: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h), | |
| (q, k, v)) | |
| # force cast to fp32 to avoid overflowing | |
| if _ATTN_PRECISION == 'fp32': | |
| with torch.autocast(enabled=False, device_type=device): | |
| q, k = q.float(), k.float() | |
| sim = torch.einsum('b i d, b j d -> b i j', q, | |
| k) * self.scale | |
| else: | |
| sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale | |
| del q, k | |
| if mask is not None: | |
| mask = einops.rearrange(mask, 'b ... -> b (...)') | |
| max_neg_value = -torch.finfo(sim.dtype).max | |
| mask = einops.repeat(mask, 'b j -> (b h) () j', h=h) | |
| sim.masked_fill_(~mask, max_neg_value) | |
| # attention, what we cannot get enough of | |
| sim = sim.softmax(dim=-1) | |
| out = torch.einsum('b i j, b j d -> b i d', sim, v) | |
| out = einops.rearrange(out, '(b h) n d -> b n (h d)', h=h) | |
| return self.to_out(out) | |
| return forward | |
| class DummyController: | |
| def __call__(self, *args): | |
| return args[0] | |
| def __init__(self): | |
| self.cur_step = 0 | |
| if controller is None: | |
| controller = DummyController() | |
| def register_recr(net_, place_in_unet): | |
| if net_.__class__.__name__ == 'CrossAttention': | |
| net_.forward = ca_forward(net_, place_in_unet) | |
| elif hasattr(net_, 'children'): | |
| for net__ in net_.children(): | |
| register_recr(net__, place_in_unet) | |
| sub_nets = model.named_children() | |
| for net in sub_nets: | |
| if 'input_blocks' in net[0]: | |
| register_recr(net[1], 'down') | |
| elif 'output_blocks' in net[0]: | |
| register_recr(net[1], 'up') | |
| elif 'middle_block' in net[0]: | |
| register_recr(net[1], 'mid') | |
| class DDIMVSampler(object): | |
| def __init__(self, model, schedule='linear', **kwargs): | |
| super().__init__() | |
| self.model = model | |
| self.ddpm_num_timesteps = model.num_timesteps | |
| self.schedule = schedule | |
| def register_buffer(self, name, attr): | |
| if type(attr) == torch.Tensor: | |
| if attr.device != torch.device(device): | |
| attr = attr.to(torch.device(device)) | |
| 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.ddpm_num_timesteps, | |
| verbose=verbose) | |
| alphas_cumprod = self.model.alphas_cumprod | |
| assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, \ | |
| 'alphas have to be defined for each timestep' | |
| def to_torch(x): | |
| return x.clone().detach().to(torch.float32).to(self.model.device) | |
| self.register_buffer('betas', to_torch(self.model.betas)) | |
| self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) | |
| self.register_buffer('alphas_cumprod_prev', | |
| to_torch(self.model.alphas_cumprod_prev)) | |
| # calculations for diffusion q(x_t | x_{t-1}) and others | |
| self.register_buffer('sqrt_alphas_cumprod', | |
| to_torch(np.sqrt(alphas_cumprod.cpu()))) | |
| self.register_buffer('sqrt_one_minus_alphas_cumprod', | |
| to_torch(np.sqrt(1. - alphas_cumprod.cpu()))) | |
| self.register_buffer('log_one_minus_alphas_cumprod', | |
| to_torch(np.log(1. - alphas_cumprod.cpu()))) | |
| self.register_buffer('sqrt_recip_alphas_cumprod', | |
| to_torch(np.sqrt(1. / alphas_cumprod.cpu()))) | |
| self.register_buffer('sqrt_recipm1_alphas_cumprod', | |
| to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1))) | |
| # ddim sampling parameters | |
| ddim_sigmas, ddim_alphas, ddim_alphas_prev = \ | |
| make_ddim_sampling_parameters( | |
| alphacums=alphas_cumprod.cpu(), | |
| ddim_timesteps=self.ddim_timesteps, | |
| eta=ddim_eta, | |
| verbose=verbose) | |
| self.register_buffer('ddim_sigmas', ddim_sigmas) | |
| self.register_buffer('ddim_alphas', ddim_alphas) | |
| self.register_buffer('ddim_alphas_prev', ddim_alphas_prev) | |
| self.register_buffer('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_buffer('ddim_sigmas_for_original_num_steps', | |
| sigmas_for_original_sampling_steps) | |
| def sample(self, | |
| S, | |
| batch_size, | |
| shape, | |
| conditioning=None, | |
| callback=None, | |
| img_callback=None, | |
| quantize_x0=False, | |
| eta=0., | |
| mask=None, | |
| x0=None, | |
| xtrg=None, | |
| noise_rescale=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, | |
| dynamic_threshold=None, | |
| ucg_schedule=None, | |
| controller=None, | |
| strength=0.0, | |
| **kwargs): | |
| if conditioning is not None: | |
| if isinstance(conditioning, dict): | |
| ctmp = conditioning[list(conditioning.keys())[0]] | |
| while isinstance(ctmp, list): | |
| ctmp = ctmp[0] | |
| cbs = ctmp.shape[0] | |
| if cbs != batch_size: | |
| print(f'Warning: Got {cbs} conditionings' | |
| f'but batch-size is {batch_size}') | |
| elif isinstance(conditioning, list): | |
| for ctmp in conditioning: | |
| if ctmp.shape[0] != batch_size: | |
| print(f'Warning: Got {cbs} conditionings' | |
| f'but batch-size is {batch_size}') | |
| else: | |
| if conditioning.shape[0] != batch_size: | |
| print(f'Warning: Got {conditioning.shape[0]}' | |
| f'conditionings but batch-size is {batch_size}') | |
| self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) | |
| # sampling | |
| C, H, W = shape | |
| size = (batch_size, C, H, W) | |
| print(f'Data shape for DDIM sampling is {size}, eta {eta}') | |
| samples, intermediates = self.ddim_sampling( | |
| conditioning, | |
| size, | |
| callback=callback, | |
| img_callback=img_callback, | |
| quantize_denoised=quantize_x0, | |
| mask=mask, | |
| x0=x0, | |
| xtrg=xtrg, | |
| noise_rescale=noise_rescale, | |
| 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, | |
| dynamic_threshold=dynamic_threshold, | |
| ucg_schedule=ucg_schedule, | |
| controller=controller, | |
| strength=strength, | |
| ) | |
| return samples, intermediates | |
| def ddim_sampling(self, | |
| cond, | |
| shape, | |
| x_T=None, | |
| ddim_use_original_steps=False, | |
| callback=None, | |
| timesteps=None, | |
| quantize_denoised=False, | |
| mask=None, | |
| x0=None, | |
| xtrg=None, | |
| noise_rescale=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, | |
| dynamic_threshold=None, | |
| ucg_schedule=None, | |
| controller=None, | |
| strength=0.0): | |
| if strength == 1 and x0 is not None: | |
| return x0, None | |
| register_attention_control(self.model.model.diffusion_model, | |
| controller) | |
| device = self.model.betas.device | |
| b = shape[0] | |
| if x_T is None: | |
| img = torch.randn(shape, device=device) | |
| else: | |
| img = x_T | |
| if timesteps is None: | |
| timesteps = self.ddpm_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] | |
| intermediates = {'x_inter': [img], 'pred_x0': [img]} | |
| time_range = 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 DDIM Sampling with {total_steps} timesteps') | |
| iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps) | |
| if controller is not None: | |
| controller.set_total_step(total_steps) | |
| if mask is None: | |
| mask = [None] * total_steps | |
| dir_xt = 0 | |
| for i, step in enumerate(iterator): | |
| if controller is not None: | |
| controller.set_step(i) | |
| index = total_steps - i - 1 | |
| ts = torch.full((b, ), step, device=device, dtype=torch.long) | |
| if strength >= 0 and i == int( | |
| total_steps * strength) and x0 is not None: | |
| img = self.model.q_sample(x0, ts) | |
| if mask is not None and xtrg is not None: | |
| # TODO: deterministic forward pass? | |
| if type(mask) == list: | |
| weight = mask[i] | |
| else: | |
| weight = mask | |
| if weight is not None: | |
| rescale = torch.maximum(1. - weight, (1 - weight**2)**0.5 * | |
| controller.inner_strength) | |
| if noise_rescale is not None: | |
| rescale = (1. - weight) * ( | |
| 1 - noise_rescale) + rescale * noise_rescale | |
| img_ref = self.model.q_sample(xtrg, ts) | |
| img = img_ref * weight + (1. - weight) * ( | |
| img - dir_xt) + rescale * dir_xt | |
| if ucg_schedule is not None: | |
| assert len(ucg_schedule) == len(time_range) | |
| unconditional_guidance_scale = ucg_schedule[i] | |
| outs = self.p_sample_ddim( | |
| 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, | |
| dynamic_threshold=dynamic_threshold, | |
| controller=controller, | |
| return_dir=True) | |
| img, pred_x0, dir_xt = outs | |
| if callback: | |
| callback(i) | |
| if img_callback: | |
| img_callback(pred_x0, i) | |
| if index % log_every_t == 0 or index == total_steps - 1: | |
| intermediates['x_inter'].append(img) | |
| intermediates['pred_x0'].append(pred_x0) | |
| return img, intermediates | |
| 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, | |
| dynamic_threshold=None, | |
| controller=None, | |
| return_dir=False): | |
| b, *_, device = *x.shape, x.device | |
| if unconditional_conditioning is None or \ | |
| unconditional_guidance_scale == 1.: | |
| model_output = self.model.apply_model(x, t, c) | |
| else: | |
| model_t = self.model.apply_model(x, t, c) | |
| model_uncond = self.model.apply_model(x, t, | |
| unconditional_conditioning) | |
| model_output = model_uncond + unconditional_guidance_scale * ( | |
| model_t - model_uncond) | |
| if self.model.parameterization == 'v': | |
| e_t = self.model.predict_eps_from_z_and_v(x, t, model_output) | |
| else: | |
| e_t = model_output | |
| if score_corrector is not None: | |
| assert self.model.parameterization == 'eps', 'not implemented' | |
| e_t = score_corrector.modify_score(self.model, e_t, x, t, c, | |
| **corrector_kwargs) | |
| if use_original_steps: | |
| alphas = self.model.alphas_cumprod | |
| alphas_prev = self.model.alphas_cumprod_prev | |
| sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod | |
| sigmas = self.model.ddim_sigmas_for_original_num_steps | |
| else: | |
| alphas = self.ddim_alphas | |
| alphas_prev = self.ddim_alphas_prev | |
| sqrt_one_minus_alphas = self.ddim_sqrt_one_minus_alphas | |
| sigmas = 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 | |
| if self.model.parameterization != 'v': | |
| pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() | |
| else: | |
| pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output) | |
| if quantize_denoised: | |
| pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) | |
| if dynamic_threshold is not None: | |
| raise NotImplementedError() | |
| ''' | |
| if mask is not None and xtrg is not None: | |
| pred_x0 = xtrg * mask + (1. - mask) * pred_x0 | |
| ''' | |
| if controller is not None: | |
| pred_x0 = controller.update_x0(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 | |
| if return_dir: | |
| return x_prev, pred_x0, dir_xt | |
| return x_prev, pred_x0 | |
| def encode(self, | |
| x0, | |
| c, | |
| t_enc, | |
| use_original_steps=False, | |
| return_intermediates=None, | |
| unconditional_guidance_scale=1.0, | |
| unconditional_conditioning=None, | |
| callback=None): | |
| timesteps = np.arange(self.ddpm_num_timesteps | |
| ) if use_original_steps else self.ddim_timesteps | |
| num_reference_steps = timesteps.shape[0] | |
| assert t_enc <= num_reference_steps | |
| num_steps = t_enc | |
| if use_original_steps: | |
| alphas_next = self.alphas_cumprod[:num_steps] | |
| alphas = self.alphas_cumprod_prev[:num_steps] | |
| else: | |
| alphas_next = self.ddim_alphas[:num_steps] | |
| alphas = torch.tensor(self.ddim_alphas_prev[:num_steps]) | |
| x_next = x0 | |
| intermediates = [] | |
| inter_steps = [] | |
| for i in tqdm(range(num_steps), desc='Encoding Image'): | |
| t = torch.full((x0.shape[0], ), | |
| timesteps[i], | |
| device=self.model.device, | |
| dtype=torch.long) | |
| if unconditional_guidance_scale == 1.: | |
| noise_pred = self.model.apply_model(x_next, t, c) | |
| else: | |
| assert unconditional_conditioning is not None | |
| e_t_uncond, noise_pred = torch.chunk( | |
| self.model.apply_model( | |
| torch.cat((x_next, x_next)), torch.cat((t, t)), | |
| torch.cat((unconditional_conditioning, c))), 2) | |
| noise_pred = e_t_uncond + unconditional_guidance_scale * ( | |
| noise_pred - e_t_uncond) | |
| xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next | |
| weighted_noise_pred = alphas_next[i].sqrt() * ( | |
| (1 / alphas_next[i] - 1).sqrt() - | |
| (1 / alphas[i] - 1).sqrt()) * noise_pred | |
| x_next = xt_weighted + weighted_noise_pred | |
| if return_intermediates and i % (num_steps // return_intermediates | |
| ) == 0 and i < num_steps - 1: | |
| intermediates.append(x_next) | |
| inter_steps.append(i) | |
| elif return_intermediates and i >= num_steps - 2: | |
| intermediates.append(x_next) | |
| inter_steps.append(i) | |
| if callback: | |
| callback(i) | |
| out = {'x_encoded': x_next, 'intermediate_steps': inter_steps} | |
| if return_intermediates: | |
| out.update({'intermediates': intermediates}) | |
| return x_next, out | |
| def stochastic_encode(self, x0, t, use_original_steps=False, noise=None): | |
| # fast, but does not allow for exact reconstruction | |
| # t serves as an index to gather the correct alphas | |
| 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: | |
| noise = torch.randn_like(x0) | |
| if t >= len(sqrt_alphas_cumprod): | |
| return noise | |
| return ( | |
| extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 + | |
| extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * | |
| noise) | |
| def decode(self, | |
| x_latent, | |
| cond, | |
| t_start, | |
| unconditional_guidance_scale=1.0, | |
| unconditional_conditioning=None, | |
| use_original_steps=False, | |
| callback=None): | |
| 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 | |
| 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) | |
| 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 callback: | |
| callback(i) | |
| return x_dec | |
| def calc_mean_std(feat, eps=1e-5): | |
| # eps is a small value added to the variance to avoid divide-by-zero. | |
| size = feat.size() | |
| assert (len(size) == 4) | |
| N, C = size[:2] | |
| feat_var = feat.view(N, C, -1).var(dim=2) + eps | |
| feat_std = feat_var.sqrt().view(N, C, 1, 1) | |
| feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) | |
| return feat_mean, feat_std | |
| def adaptive_instance_normalization(content_feat, style_feat): | |
| assert (content_feat.size()[:2] == style_feat.size()[:2]) | |
| size = content_feat.size() | |
| style_mean, style_std = calc_mean_std(style_feat) | |
| content_mean, content_std = calc_mean_std(content_feat) | |
| normalized_feat = (content_feat - | |
| content_mean.expand(size)) / content_std.expand(size) | |
| return normalized_feat * style_std.expand(size) + style_mean.expand(size) | |