270 lines
9.9 KiB
Python
270 lines
9.9 KiB
Python
from torchvision.datasets.utils import download_url
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from ldm.util import instantiate_from_config
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import torch
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import os
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# todo ?
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from google.colab import files
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from IPython.display import Image as ipyimg
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import ipywidgets as widgets
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from PIL import Image
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from numpy import asarray
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from einops import rearrange, repeat
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import torch, torchvision
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from ldm.models.diffusion.ddim import DDIMSampler
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from ldm.util import ismap
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import time
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from omegaconf import OmegaConf
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def download_models(mode):
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if mode == "superresolution":
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# this is the small bsr light model
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url_conf = 'https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1'
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url_ckpt = 'https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1'
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path_conf = 'logs/diffusion/superresolution_bsr/configs/project.yaml'
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path_ckpt = 'logs/diffusion/superresolution_bsr/checkpoints/last.ckpt'
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download_url(url_conf, path_conf)
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download_url(url_ckpt, path_ckpt)
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path_conf = path_conf + '/?dl=1' # fix it
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path_ckpt = path_ckpt + '/?dl=1' # fix it
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return path_conf, path_ckpt
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else:
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raise NotImplementedError
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def load_model_from_config(config, ckpt):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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global_step = pl_sd["global_step"]
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sd = pl_sd["state_dict"]
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model = instantiate_from_config(config.model)
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m, u = model.load_state_dict(sd, strict=False)
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model.cuda()
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model.eval()
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return {"model": model}, global_step
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def get_model(mode):
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path_conf, path_ckpt = download_models(mode)
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config = OmegaConf.load(path_conf)
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model, step = load_model_from_config(config, path_ckpt)
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return model
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def get_custom_cond(mode):
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dest = "data/example_conditioning"
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if mode == "superresolution":
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uploaded_img = files.upload()
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filename = next(iter(uploaded_img))
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name, filetype = filename.split(".") # todo assumes just one dot in name !
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os.rename(f"{filename}", f"{dest}/{mode}/custom_{name}.{filetype}")
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elif mode == "text_conditional":
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w = widgets.Text(value='A cake with cream!', disabled=True)
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display(w)
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with open(f"{dest}/{mode}/custom_{w.value[:20]}.txt", 'w') as f:
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f.write(w.value)
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elif mode == "class_conditional":
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w = widgets.IntSlider(min=0, max=1000)
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display(w)
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with open(f"{dest}/{mode}/custom.txt", 'w') as f:
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f.write(w.value)
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else:
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raise NotImplementedError(f"cond not implemented for mode{mode}")
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def get_cond_options(mode):
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path = "data/example_conditioning"
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path = os.path.join(path, mode)
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onlyfiles = [f for f in sorted(os.listdir(path))]
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return path, onlyfiles
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def select_cond_path(mode):
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path = "data/example_conditioning" # todo
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path = os.path.join(path, mode)
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onlyfiles = [f for f in sorted(os.listdir(path))]
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selected = widgets.RadioButtons(
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options=onlyfiles,
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description='Select conditioning:',
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disabled=False
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)
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display(selected)
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selected_path = os.path.join(path, selected.value)
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return selected_path
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def get_cond(mode, selected_path):
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example = dict()
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if mode == "superresolution":
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up_f = 4
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visualize_cond_img(selected_path)
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c = Image.open(selected_path)
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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]], 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(torch.device("cuda"))
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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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def visualize_cond_img(path):
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display(ipyimg(filename=path))
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def run(model, selected_path, task, custom_steps, resize_enabled=False, classifier_ckpt=None, global_step=None):
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example = get_cond(task, selected_path)
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save_intermediate_vid = False
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n_runs = 1
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masked = False
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guider = None
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ckwargs = None
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mode = 'ddim'
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ddim_use_x0_pred = False
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temperature = 1.
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eta = 1.
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make_progrow = True
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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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invert_mask = False
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x_T = None
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for n 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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mode=mode, custom_steps=custom_steps,
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eta=eta, swap_mode=False , masked=masked,
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invert_mask=invert_mask, quantize_x0=False,
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custom_schedule=None, decode_interval=10,
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resize_enabled=resize_enabled, 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, save_intermediate_vid=save_intermediate_vid,
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make_progrow=make_progrow,ddim_use_x0_pred=ddim_use_x0_pred
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)
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return logs
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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, img_callback=None,
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temperature=1., noise_dropout=0., score_corrector=None,
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corrector_kwargs=None, x_T=None, log_every_t=None
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):
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ddim = DDIMSampler(model)
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bs = shape[0] # dont know where this comes from but wayne
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shape = shape[1:] # cut batch dim
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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, mode="vanilla", custom_steps=None, eta=1.0, swap_mode=False, masked=False,
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invert_mask=True, quantize_x0=False, custom_schedule=None, decode_interval=1000,
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resize_enabled=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
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corrector_kwargs=None, x_T=None, save_intermediate_vid=False, make_progrow=True,ddim_use_x0_pred=False):
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log = dict()
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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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log_every_t = 1 if save_intermediate_vid else None
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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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img_cb = None
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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, img_callback=img_cb, mask=None, x0=z0,
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temperature=temperature, noise_dropout=noise_dropout,
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score_corrector=corrector, corrector_kwargs=corrector_kwargs,
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x_T=x_T, log_every_t=log_every_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:
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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 |