253 lines
9.6 KiB
Python
253 lines
9.6 KiB
Python
import os
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import gc
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import time
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import numpy as np
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import torch
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import torchvision
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from PIL import Image
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from einops import rearrange, repeat
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from omegaconf import OmegaConf
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import safetensors.torch
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.util import instantiate_from_config, ismap
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from modules import shared, sd_hijack
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cached_ldsr_model: torch.nn.Module = None
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# Create LDSR Class
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class LDSR:
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def load_model_from_config(self, half_attention):
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global cached_ldsr_model
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if shared.opts.ldsr_cached and cached_ldsr_model is not None:
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print("Loading model from cache")
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model: torch.nn.Module = cached_ldsr_model
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else:
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print(f"Loading model from {self.modelPath}")
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_, extension = os.path.splitext(self.modelPath)
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if extension.lower() == ".safetensors":
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pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
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else:
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pl_sd = torch.load(self.modelPath, map_location="cpu")
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sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
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config = OmegaConf.load(self.yamlPath)
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config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
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model: torch.nn.Module = instantiate_from_config(config.model)
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model.load_state_dict(sd, strict=False)
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model = model.to(shared.device)
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if half_attention:
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model = model.half()
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if shared.cmd_opts.opt_channelslast:
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model = model.to(memory_format=torch.channels_last)
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sd_hijack.model_hijack.hijack(model) # apply optimization
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model.eval()
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if shared.opts.ldsr_cached:
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cached_ldsr_model = model
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return {"model": model}
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def __init__(self, model_path, yaml_path):
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self.modelPath = model_path
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self.yamlPath = yaml_path
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@staticmethod
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def run(model, selected_path, custom_steps, eta):
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example = get_cond(selected_path)
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n_runs = 1
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guider = None
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ckwargs = None
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ddim_use_x0_pred = False
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temperature = 1.
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eta = eta
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custom_shape = None
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height, width = example["image"].shape[1:3]
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split_input = height >= 128 and width >= 128
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if split_input:
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ks = 128
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stride = 64
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vqf = 4 #
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model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
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"vqf": vqf,
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"patch_distributed_vq": True,
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"tie_braker": False,
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"clip_max_weight": 0.5,
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"clip_min_weight": 0.01,
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"clip_max_tie_weight": 0.5,
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"clip_min_tie_weight": 0.01}
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else:
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if hasattr(model, "split_input_params"):
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delattr(model, "split_input_params")
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x_t = None
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logs = None
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for _ in range(n_runs):
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if custom_shape is not None:
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x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
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x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
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logs = make_convolutional_sample(example, model,
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custom_steps=custom_steps,
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eta=eta, quantize_x0=False,
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custom_shape=custom_shape,
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temperature=temperature, noise_dropout=0.,
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corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
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ddim_use_x0_pred=ddim_use_x0_pred
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)
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return logs
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def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
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model = self.load_model_from_config(half_attention)
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# Run settings
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diffusion_steps = int(steps)
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eta = 1.0
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gc.collect()
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if torch.cuda.is_available:
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torch.cuda.empty_cache()
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im_og = image
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width_og, height_og = im_og.size
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# If we can adjust the max upscale size, then the 4 below should be our variable
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down_sample_rate = target_scale / 4
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wd = width_og * down_sample_rate
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hd = height_og * down_sample_rate
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width_downsampled_pre = int(np.ceil(wd))
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height_downsampled_pre = int(np.ceil(hd))
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if down_sample_rate != 1:
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print(
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f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
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im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
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else:
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print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
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# pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
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pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
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im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
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logs = self.run(model["model"], im_padded, diffusion_steps, eta)
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sample = logs["sample"]
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sample = sample.detach().cpu()
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sample = torch.clamp(sample, -1., 1.)
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sample = (sample + 1.) / 2. * 255
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sample = sample.numpy().astype(np.uint8)
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sample = np.transpose(sample, (0, 2, 3, 1))
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a = Image.fromarray(sample[0])
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# remove padding
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a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
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del model
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gc.collect()
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if torch.cuda.is_available:
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torch.cuda.empty_cache()
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return a
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def get_cond(selected_path):
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example = {}
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up_f = 4
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c = selected_path.convert('RGB')
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c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
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c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
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antialias=True)
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c_up = rearrange(c_up, '1 c h w -> 1 h w c')
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c = rearrange(c, '1 c h w -> 1 h w c')
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c = 2. * c - 1.
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c = c.to(shared.device)
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example["LR_image"] = c
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example["image"] = c_up
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return example
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@torch.no_grad()
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def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
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mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
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corrector_kwargs=None, x_t=None
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):
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ddim = DDIMSampler(model)
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bs = shape[0]
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shape = shape[1:]
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print(f"Sampling with eta = {eta}; steps: {steps}")
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samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
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normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
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mask=mask, x0=x0, temperature=temperature, verbose=False,
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score_corrector=score_corrector,
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corrector_kwargs=corrector_kwargs, x_t=x_t)
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return samples, intermediates
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@torch.no_grad()
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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,
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corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
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log = {}
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z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
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return_first_stage_outputs=True,
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force_c_encode=not (hasattr(model, 'split_input_params')
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and model.cond_stage_key == 'coordinates_bbox'),
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return_original_cond=True)
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if custom_shape is not None:
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z = torch.randn(custom_shape)
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print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
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z0 = None
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log["input"] = x
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log["reconstruction"] = xrec
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if ismap(xc):
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log["original_conditioning"] = model.to_rgb(xc)
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if hasattr(model, 'cond_stage_key'):
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log[model.cond_stage_key] = model.to_rgb(xc)
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else:
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log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
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if model.cond_stage_model:
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log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
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if model.cond_stage_key == 'class_label':
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log[model.cond_stage_key] = xc[model.cond_stage_key]
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with model.ema_scope("Plotting"):
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t0 = time.time()
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sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
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eta=eta,
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quantize_x0=quantize_x0, mask=None, x0=z0,
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temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
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x_t=x_T)
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t1 = time.time()
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if ddim_use_x0_pred:
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sample = intermediates['pred_x0'][-1]
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x_sample = model.decode_first_stage(sample)
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try:
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x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
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log["sample_noquant"] = x_sample_noquant
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log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
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except Exception:
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pass
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log["sample"] = x_sample
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log["time"] = t1 - t0
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return log
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