424 lines
18 KiB
Python
424 lines
18 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import torch
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import torch.nn.functional as F
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import copy
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import math
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import random
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from contextlib import nullcontext
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from einops import rearrange
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from scepter.modules.model.network.ldm import LatentDiffusion
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from scepter.modules.model.registry import MODELS, DIFFUSIONS, BACKBONES, LOSSES, TOKENIZERS, EMBEDDERS
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from scepter.modules.model.utils.basic_utils import check_list_of_list, to_device, pack_imagelist_into_tensor, \
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limit_batch_data, unpack_tensor_into_imagelist, count_params, disabled_train
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from scepter.modules.utils.config import dict_to_yaml
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from scepter.modules.utils.distribute import we
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@MODELS.register_class()
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class LatentDiffusionACEPlus(LatentDiffusion):
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para_dict = LatentDiffusion.para_dict
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def __init__(self, cfg, logger=None):
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super().__init__(cfg, logger=logger)
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self.guide_scale = cfg.get('GUIDE_SCALE', 1.0)
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def init_params(self):
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self.parameterization = self.cfg.get('PARAMETERIZATION', 'rf')
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assert self.parameterization in [
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'eps', 'x0', 'v', 'rf'
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], 'currently only supporting "eps" and "x0" and "v" and "rf"'
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diffusion_cfg = self.cfg.get("DIFFUSION", None)
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assert diffusion_cfg is not None
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if self.cfg.have("WORK_DIR"):
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diffusion_cfg.WORK_DIR = self.cfg.WORK_DIR
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self.diffusion = DIFFUSIONS.build(diffusion_cfg, logger=self.logger)
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self.pretrained_model = self.cfg.get('PRETRAINED_MODEL', None)
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self.ignore_keys = self.cfg.get('IGNORE_KEYS', [])
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self.model_config = self.cfg.DIFFUSION_MODEL
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self.first_stage_config = self.cfg.FIRST_STAGE_MODEL
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self.cond_stage_config = self.cfg.COND_STAGE_MODEL
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self.tokenizer_config = self.cfg.get('TOKENIZER', None)
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self.loss_config = self.cfg.get('LOSS', None)
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self.scale_factor = self.cfg.get('SCALE_FACTOR', 0.18215)
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self.size_factor = self.cfg.get('SIZE_FACTOR', 16)
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self.default_n_prompt = self.cfg.get('DEFAULT_N_PROMPT', '')
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self.default_n_prompt = '' if self.default_n_prompt is None else self.default_n_prompt
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self.p_zero = self.cfg.get('P_ZERO', 0.0)
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self.train_n_prompt = self.cfg.get('TRAIN_N_PROMPT', '')
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if self.default_n_prompt is None:
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self.default_n_prompt = ''
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if self.train_n_prompt is None:
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self.train_n_prompt = ''
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self.use_ema = self.cfg.get('USE_EMA', False)
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self.model_ema_config = self.cfg.get('DIFFUSION_MODEL_EMA', None)
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def construct_network(self):
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# embedding_context = torch.device("meta") if self.model_config.get("PRETRAINED_MODEL", None) else nullcontext()
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# with embedding_context:
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self.model = BACKBONES.build(self.model_config, logger=self.logger).to(torch.bfloat16)
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self.logger.info('all parameters:{}'.format(count_params(self.model)))
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if self.use_ema:
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if self.model_ema_config:
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self.model_ema = BACKBONES.build(self.model_ema_config,
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logger=self.logger)
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else:
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self.model_ema = copy.deepcopy(self.model)
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self.model_ema = self.model_ema.eval()
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for param in self.model_ema.parameters():
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param.requires_grad = False
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if self.loss_config:
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self.loss = LOSSES.build(self.loss_config, logger=self.logger)
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if self.tokenizer_config is not None:
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self.tokenizer = TOKENIZERS.build(self.tokenizer_config,
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logger=self.logger)
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if self.first_stage_config:
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self.first_stage_model = MODELS.build(self.first_stage_config,
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logger=self.logger)
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self.first_stage_model = self.first_stage_model.eval()
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self.first_stage_model.train = disabled_train
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for param in self.first_stage_model.parameters():
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param.requires_grad = False
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else:
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self.first_stage_model = None
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if self.tokenizer_config is not None:
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self.cond_stage_config.KWARGS = {
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'vocab_size': self.tokenizer.vocab_size
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}
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if self.cond_stage_config == '__is_unconditional__':
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print(
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f'Training {self.__class__.__name__} as an unconditional model.'
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)
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self.cond_stage_model = None
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else:
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model = EMBEDDERS.build(self.cond_stage_config, logger=self.logger)
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self.cond_stage_model = model.eval().requires_grad_(False)
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self.cond_stage_model.train = disabled_train
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@torch.no_grad()
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def encode_first_stage(self, x, **kwargs):
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def run_one_image(u):
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zu = self.first_stage_model.encode(u)
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if isinstance(zu, (tuple, list)):
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zu = zu[0]
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return zu
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z = [run_one_image(u.unsqueeze(0) if u.dim() == 3 else u) for u in x]
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return z
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@torch.no_grad()
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def decode_first_stage(self, z):
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return [self.first_stage_model.decode(zu) for zu in z]
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def noise_sample(self, num_samples, h, w, seed, dtype=torch.bfloat16):
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noise = torch.randn(
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num_samples,
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16,
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# allow for packing
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2 * math.ceil(h / 16),
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2 * math.ceil(w / 16),
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device=we.device_id,
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dtype=dtype,
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generator=torch.Generator(device=we.device_id).manual_seed(seed),
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)
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return noise
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def resize_func(self, x, size):
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if x is None: return x
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return F.interpolate(x.unsqueeze(0), size = size, mode='nearest-exact')
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def parse_ref_and_edit(self, src_image,
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src_image_mask,
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text_embedding,
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#text_mask,
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edit_id):
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edit_image = []
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edit_mask = []
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ref_image = []
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ref_mask = []
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ref_context = []
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ref_y = []
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ref_id = []
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txt = []
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txt_y = []
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for sample_id, (one_src, one_src_mask,
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one_text_embedding,
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one_text_y,
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# one_text_mask,
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one_edit_id) in enumerate(zip(src_image,
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src_image_mask,
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text_embedding["context"],
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text_embedding["y"],
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#text_mask,
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edit_id)
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):
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ref_id.append([i for i in range(len(one_src))])
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if hasattr(self, "ref_cond_stage_model") and self.ref_cond_stage_model:
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ref_image.append(self.ref_cond_stage_model.encode_list([((i + 1.0) / 2.0 * 255).type(torch.uint8) for i in one_src]))
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else:
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ref_image.append(one_src)
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ref_mask.append(one_src_mask)
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# process edit image & edit image mask
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current_edit_image = to_device([one_src[i] for i in one_edit_id], strict=False)
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current_edit_image = [v.squeeze(0) for v in self.encode_first_stage(current_edit_image)]
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current_edit_image_mask = to_device([one_src_mask[i] for i in one_edit_id], strict=False)
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current_edit_image_mask = [self.reshape_func(m).squeeze(0) for m in current_edit_image_mask]
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edit_image.append(current_edit_image)
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edit_mask.append(current_edit_image_mask)
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ref_context.append(one_text_embedding[:len(ref_id[-1])])
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ref_y.append(one_text_y[:len(ref_id[-1])])
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if not sum(len(src_) for src_ in src_image) > 0:
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ref_image = None
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ref_context = None
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ref_y = None
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for sample_id, (one_text_embedding, one_text_y) in enumerate(zip(text_embedding["context"],
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text_embedding["y"])):
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txt.append(one_text_embedding[-1].squeeze(0))
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txt_y.append(one_text_y[-1])
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return {
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"edit": edit_image,
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"edit_mask": edit_mask,
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"edit_id": edit_id,
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"ref_context": ref_context,
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"ref_y": ref_y,
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"context": txt,
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"y": txt_y,
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"ref_x": ref_image,
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"ref_mask": ref_mask,
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"ref_id": ref_id
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}
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def reshape_func(self, mask):
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mask = mask.to(torch.bfloat16)
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mask = mask.view((-1, mask.shape[-2], mask.shape[-1]))
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mask = rearrange(
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mask,
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"c (h ph) (w pw) -> c (ph pw) h w",
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ph=8,
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pw=8,
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)
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return mask
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def forward_train(self,
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src_image_list =[],
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src_mask_list =[],
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edit_id=[],
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image=None,
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image_mask=None,
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noise=None,
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prompt=[],
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**kwargs):
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'''
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Args:
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src_image: list of list of src_image
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src_image_mask: list of list of src_image_mask
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image: target image
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image_mask: target image mask
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noise: default is None, generate automaticly
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ref_prompt: list of list of text
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prompt: list of text
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**kwargs:
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Returns:
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'''
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assert check_list_of_list(src_image_list) and check_list_of_list(src_mask_list)
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assert self.cond_stage_model is not None
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gc_seg = kwargs.pop("gc_seg", [])
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gc_seg = int(gc_seg[0]) if len(gc_seg) > 0 else 0
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align = kwargs.pop("align", [])
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prompt_ = [[pp] if isinstance(pp, str) else pp for pp in prompt]
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if len(align) < 1: align = [0] * len(prompt_)
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context = getattr(self.cond_stage_model, 'encode_list_of_list')(prompt_)
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guide_scale = self.guide_scale
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if guide_scale is not None:
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guide_scale = torch.full((len(prompt_),), guide_scale, device=we.device_id)
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else:
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guide_scale = None
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# image and image_mask
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# print("is list of list", check_list_of_list(image))
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if check_list_of_list(image):
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image = [to_device(ix) for ix in image]
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x_start = [self.encode_first_stage(ix, **kwargs) for ix in image]
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noise = [[torch.randn_like(ii) for ii in ix] for ix in x_start]
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x_start = [torch.cat(ix, dim=-1) for ix in x_start]
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noise = [torch.cat(ix, dim=-1) for ix in noise]
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noise, _ = pack_imagelist_into_tensor(noise)
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image_mask = [to_device(im, strict=False) for im in image_mask]
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x_mask = [[self.reshape_func(i).squeeze(0) for i in im] if im is not None else [None] * len(ix) for ix, im in zip(image, image_mask)]
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x_mask = [torch.cat(im, dim=-1) for im in x_mask]
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else:
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image = to_device(image)
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x_start = self.encode_first_stage(image, **kwargs)
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image_mask = to_device(image_mask, strict=False)
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x_mask = [self.reshape_func(i).squeeze(0) for i in image_mask] if image_mask is not None else [None] * len(
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image)
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loss_mask, _ = pack_imagelist_into_tensor(
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tuple(torch.ones_like(ix, dtype=torch.bool, device=ix.device) for ix in x_start))
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x_start, x_shapes = pack_imagelist_into_tensor(x_start)
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context['x_shapes'] = x_shapes
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context['align'] = align
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# process image mask
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context['x_mask'] = x_mask
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ref_edit_context = self.parse_ref_and_edit(src_image_list, src_mask_list, context, edit_id)
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context.update(ref_edit_context)
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teacher_context = copy.deepcopy(context)
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teacher_context["context"] = torch.cat(teacher_context["context"], dim=0)
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teacher_context["y"] = torch.cat(teacher_context["y"], dim=0)
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loss = self.diffusion.loss(x_0=x_start,
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model=self.model,
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model_kwargs={"cond": context,
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"gc_seg": gc_seg,
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"guidance": guide_scale},
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noise=noise,
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reduction='none',
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**kwargs)
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loss = loss[loss_mask].mean()
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ret = {'loss': loss, 'probe_data': {'prompt': prompt}}
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return ret
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@torch.no_grad()
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def forward_test(self,
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src_image_list=[],
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src_mask_list=[],
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edit_id=[],
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image=None,
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image_mask=None,
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prompt=[],
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sampler='flow_euler',
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sample_steps=20,
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seed=2023,
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guide_scale=3.5,
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guide_rescale=0.0,
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show_process=False,
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log_num=-1,
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**kwargs):
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outputs = self.forward_editing(
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src_image_list=src_image_list,
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src_mask_list=src_mask_list,
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edit_id=edit_id,
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image=image,
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image_mask=image_mask,
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prompt=prompt,
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sampler=sampler,
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sample_steps=sample_steps,
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seed=seed,
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guide_scale=guide_scale,
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guide_rescale=guide_rescale,
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show_process=show_process,
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log_num=log_num,
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**kwargs
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)
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return outputs
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@torch.no_grad()
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def forward_editing(self,
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src_image_list=[],
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src_mask_list=[],
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edit_id=[],
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image=None,
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image_mask=None,
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prompt=[],
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sampler='flow_euler',
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sample_steps=20,
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seed=2023,
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guide_scale=3.5,
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log_num=-1,
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**kwargs
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):
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# gc_seg is unused
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prompt, image, image_mask, src_image, src_image_mask, edit_id = limit_batch_data(
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[prompt, image, image_mask, src_image_list, src_mask_list, edit_id], log_num)
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assert check_list_of_list(src_image) and check_list_of_list(src_image_mask)
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assert self.cond_stage_model is not None
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align = kwargs.pop("align", [])
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prompt_ = [[pp] if isinstance(pp, str) else pp for pp in prompt]
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if len(align) < 1: align = [0] * len(prompt_)
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context = getattr(self.cond_stage_model, 'encode_list_of_list')(prompt_)
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guide_scale = guide_scale or self.guide_scale
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if guide_scale is not None:
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guide_scale = torch.full((len(prompt),), guide_scale, device=we.device_id)
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else:
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guide_scale = None
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# image and image_mask
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seed = seed if seed >= 0 else random.randint(0, 2 ** 32 - 1)
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if image is not None:
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if check_list_of_list(image):
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image = [torch.cat(ix, dim=-1) for ix in image]
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image_mask = [torch.cat(im, dim=-1) for im in image_mask]
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noise = [self.noise_sample(1, ix.shape[1], ix.shape[2], seed) for ix in image]
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else:
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height, width = kwargs.pop("height"), kwargs.pop("width")
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noise = [self.noise_sample(1, height, width, seed) for _ in prompt]
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noise, x_shapes = pack_imagelist_into_tensor(noise)
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context['x_shapes'] = x_shapes
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context['align'] = align
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# process image mask
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image_mask = to_device(image_mask, strict=False)
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x_mask = [self.reshape_func(i).squeeze(0) for i in image_mask]
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context['x_mask'] = x_mask
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ref_edit_context = self.parse_ref_and_edit(src_image, src_image_mask, context, edit_id)
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context.update(ref_edit_context)
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# UNet use input n_prompt
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# model = self.model_ema if self.use_ema and self.eval_ema else self.model
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# import pdb;pdb.set_trace()
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model = self.model
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embedding_context = model.no_sync if isinstance(model, torch.distributed.fsdp.FullyShardedDataParallel) \
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else nullcontext
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with embedding_context():
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samples = self.diffusion.sample(
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noise=noise,
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sampler=sampler,
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model=self.model,
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model_kwargs={"cond": context, "guidance": guide_scale, "gc_seg": -1
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},
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steps=sample_steps,
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show_progress=True,
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guide_scale=guide_scale,
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return_intermediate=None,
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**kwargs).float()
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samples = unpack_tensor_into_imagelist(samples, x_shapes)
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with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
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x_samples = self.decode_first_stage(samples)
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outputs = list()
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for i in range(len(prompt)):
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rec_img = torch.clamp((x_samples[i].float() + 1.0) / 2.0, min=0.0, max=1.0)
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rec_img = rec_img.squeeze(0)
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edit_imgs, edit_img_masks = [], []
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if src_image is not None and src_image[i] is not None:
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if src_image_mask[i] is None:
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src_image_mask[i] = [None] * len(src_image[i])
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for edit_img, edit_mask in zip(src_image[i], src_image_mask[i]):
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edit_img = torch.clamp((edit_img.float() + 1.0) / 2.0, min=0.0, max=1.0)
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edit_imgs.append(edit_img.squeeze(0))
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if edit_mask is None:
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edit_mask = torch.ones_like(edit_img[[0], :, :])
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edit_img_masks.append(edit_mask)
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one_tup = {
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'reconstruct_image': rec_img,
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'instruction': prompt[i],
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'edit_image': edit_imgs if len(edit_imgs) > 0 else None,
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'edit_mask': edit_img_masks if len(edit_imgs) > 0 else None
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}
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if image is not None:
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if image_mask is None:
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image_mask = [None] * len(image)
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ori_img = torch.clamp((image[i] + 1.0) / 2.0, min=0.0, max=1.0)
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one_tup['target_image'] = ori_img.squeeze(0)
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one_tup['target_mask'] = image_mask[i] if image_mask[i] is not None else torch.ones_like(
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ori_img[[0], :, :])
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outputs.append(one_tup)
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return outputs
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@staticmethod
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def get_config_template():
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return dict_to_yaml('MODEL',
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__class__.__name__,
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LatentDiffusionACEPlus.para_dict,
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set_name=True)
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