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Diffstat (limited to 'modules/ldsr_model_arch.py')
| -rw-r--r-- | modules/ldsr_model_arch.py | 230 |
1 files changed, 0 insertions, 230 deletions
diff --git a/modules/ldsr_model_arch.py b/modules/ldsr_model_arch.py deleted file mode 100644 index 90e0a2f0..00000000 --- a/modules/ldsr_model_arch.py +++ /dev/null @@ -1,230 +0,0 @@ -import gc -import time -import warnings - -import numpy as np -import torch -import torchvision -from PIL import Image -from einops import rearrange, repeat -from omegaconf import OmegaConf - -from ldm.models.diffusion.ddim import DDIMSampler -from ldm.util import instantiate_from_config, ismap - -warnings.filterwarnings("ignore", category=UserWarning) - - -# Create LDSR Class -class LDSR: - def load_model_from_config(self, half_attention): - print(f"Loading model from {self.modelPath}") - pl_sd = torch.load(self.modelPath, map_location="cpu") - sd = pl_sd["state_dict"] - config = OmegaConf.load(self.yamlPath) - model = instantiate_from_config(config.model) - model.load_state_dict(sd, strict=False) - model.cuda() - if half_attention: - model = model.half() - - model.eval() - return {"model": model} - - def __init__(self, model_path, yaml_path): - self.modelPath = model_path - self.yamlPath = yaml_path - - @staticmethod - def run(model, selected_path, custom_steps, eta): - example = get_cond(selected_path) - - n_runs = 1 - guider = None - ckwargs = None - ddim_use_x0_pred = False - temperature = 1. - eta = eta - custom_shape = None - - height, width = example["image"].shape[1:3] - split_input = height >= 128 and width >= 128 - - if split_input: - ks = 128 - stride = 64 - vqf = 4 # - model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride), - "vqf": vqf, - "patch_distributed_vq": True, - "tie_braker": False, - "clip_max_weight": 0.5, - "clip_min_weight": 0.01, - "clip_max_tie_weight": 0.5, - "clip_min_tie_weight": 0.01} - else: - if hasattr(model, "split_input_params"): - delattr(model, "split_input_params") - - x_t = None - logs = None - for n in range(n_runs): - if custom_shape is not None: - x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device) - x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0]) - - logs = make_convolutional_sample(example, model, - custom_steps=custom_steps, - eta=eta, quantize_x0=False, - custom_shape=custom_shape, - temperature=temperature, noise_dropout=0., - corrector=guider, corrector_kwargs=ckwargs, x_T=x_t, - ddim_use_x0_pred=ddim_use_x0_pred - ) - return logs - - def super_resolution(self, image, steps=100, target_scale=2, half_attention=False): - model = self.load_model_from_config(half_attention) - - # Run settings - diffusion_steps = int(steps) - eta = 1.0 - - down_sample_method = 'Lanczos' - - gc.collect() - torch.cuda.empty_cache() - - im_og = image - width_og, height_og = im_og.size - # If we can adjust the max upscale size, then the 4 below should be our variable - down_sample_rate = target_scale / 4 - wd = width_og * down_sample_rate - hd = height_og * down_sample_rate - width_downsampled_pre = int(np.ceil(wd)) - height_downsampled_pre = int(np.ceil(hd)) - - if down_sample_rate != 1: - print( - f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]') - im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS) - else: - print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)") - - # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts - pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size - im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge')) - - logs = self.run(model["model"], im_padded, diffusion_steps, eta) - - sample = logs["sample"] - sample = sample.detach().cpu() - sample = torch.clamp(sample, -1., 1.) - sample = (sample + 1.) / 2. * 255 - sample = sample.numpy().astype(np.uint8) - sample = np.transpose(sample, (0, 2, 3, 1)) - a = Image.fromarray(sample[0]) - - # remove padding - a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4)) - - del model - gc.collect() - torch.cuda.empty_cache() - return a - - -def get_cond(selected_path): - example = dict() - up_f = 4 - c = selected_path.convert('RGB') - c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0) - c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]], - antialias=True) - c_up = rearrange(c_up, '1 c h w -> 1 h w c') - c = rearrange(c, '1 c h w -> 1 h w c') - c = 2. * c - 1. - - c = c.to(torch.device("cuda")) - example["LR_image"] = c - example["image"] = c_up - - return example - - -@torch.no_grad() -def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None, - mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None, - corrector_kwargs=None, x_t=None - ): - ddim = DDIMSampler(model) - bs = shape[0] - shape = shape[1:] - print(f"Sampling with eta = {eta}; steps: {steps}") - samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback, - normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta, - mask=mask, x0=x0, temperature=temperature, verbose=False, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, x_t=x_t) - - return samples, intermediates - - -@torch.no_grad() -def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None, - corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False): - log = dict() - - z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key, - return_first_stage_outputs=True, - force_c_encode=not (hasattr(model, 'split_input_params') - and model.cond_stage_key == 'coordinates_bbox'), - return_original_cond=True) - - if custom_shape is not None: - z = torch.randn(custom_shape) - print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}") - - z0 = None - - log["input"] = x - log["reconstruction"] = xrec - - if ismap(xc): - log["original_conditioning"] = model.to_rgb(xc) - if hasattr(model, 'cond_stage_key'): - log[model.cond_stage_key] = model.to_rgb(xc) - - else: - log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x) - if model.cond_stage_model: - log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x) - if model.cond_stage_key == 'class_label': - log[model.cond_stage_key] = xc[model.cond_stage_key] - - with model.ema_scope("Plotting"): - t0 = time.time() - - sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape, - eta=eta, - quantize_x0=quantize_x0, mask=None, x0=z0, - temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs, - x_t=x_T) - t1 = time.time() - - if ddim_use_x0_pred: - sample = intermediates['pred_x0'][-1] - - x_sample = model.decode_first_stage(sample) - - try: - x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True) - log["sample_noquant"] = x_sample_noquant - log["sample_diff"] = torch.abs(x_sample_noquant - x_sample) - except: - pass - - log["sample"] = x_sample - log["time"] = t1 - t0 - - return log |