import argparse import inspect from . import gaussian_diffusion as gd from .respace import SpacedDiffusion, space_timesteps from .text2im_model import ( SuperResText2ImModel, Text2ImModel, ) def model_and_diffusion_defaults(super_res=0): """ Defaults for image training. """ result= dict( image_size=64, num_channels=192, num_res_blocks=3, channel_mult="", num_heads=1, num_head_channels=64, num_heads_upsample=-1, attention_resolutions="32,16,8", dropout=0.1, text_ctx=128, xf_width=512, xf_layers=16, xf_heads=8, xf_final_ln=True, xf_padding=True, learn_sigma=True, ## sigma_small=False, ## diffusion_steps=1000, noise_schedule="squaredcos_cap_v2", timestep_respacing="", use_kl=False, ## predict_xstart=False, rescale_timesteps=True, rescale_learned_sigmas=True, use_fp16=False, ## use_scale_shift_norm=True, resblock_updown=True, cache_text_emb=False, inpaint=False, super_res=0, mode = '', ) if super_res: result.update( dict( image_size=256, num_res_blocks=2, noise_schedule="linear", super_res=super_res, )) return result def create_model_and_diffusion( image_size=64, num_channels=192, num_res_blocks=3, channel_mult="", num_heads=1, num_head_channels=64, num_heads_upsample=-1, attention_resolutions="32,16,8", dropout=0.1, text_ctx=128, xf_width=512, xf_layers=16, xf_heads=8, xf_final_ln=True, xf_padding=True, learn_sigma=False, ## sigma_small=False, ## diffusion_steps=1000, noise_schedule="squaredcos_cap_v2", timestep_respacing="", use_kl=False, ## predict_xstart=False, rescale_timesteps=True, rescale_learned_sigmas=True, use_fp16=False, ## use_scale_shift_norm=True, resblock_updown=True, cache_text_emb=False, inpaint=False, super_res=False, mode = '', ): model = create_model( image_size, num_channels, num_res_blocks, learn_sigma=learn_sigma, channel_mult=channel_mult, use_fp16=use_fp16, attention_resolutions=attention_resolutions, num_heads=num_heads, num_head_channels=num_head_channels, num_heads_upsample=num_heads_upsample, use_scale_shift_norm=use_scale_shift_norm, dropout=dropout, text_ctx=text_ctx, xf_width=xf_width, xf_layers=xf_layers, xf_heads=xf_heads, xf_final_ln=xf_final_ln, xf_padding=xf_padding, resblock_updown=resblock_updown, cache_text_emb=cache_text_emb, inpaint=inpaint, super_res=super_res, mode = mode ) diffusion = create_gaussian_diffusion( steps=diffusion_steps, learn_sigma=learn_sigma, sigma_small=sigma_small, noise_schedule=noise_schedule, use_kl=use_kl, predict_xstart=predict_xstart, rescale_timesteps=rescale_timesteps, rescale_learned_sigmas=rescale_learned_sigmas, timestep_respacing=timestep_respacing, ) return model, diffusion def create_model( image_size, num_channels, num_res_blocks, learn_sigma, channel_mult, use_fp16, attention_resolutions, num_heads, num_head_channels, num_heads_upsample, use_scale_shift_norm, dropout, text_ctx, xf_width, xf_layers, xf_heads, xf_final_ln, xf_padding, resblock_updown, cache_text_emb, inpaint, super_res, mode, ): if channel_mult == "": if image_size == 256: channel_mult = (1, 1, 2, 2, 4, 4) elif image_size == 128: channel_mult = (1, 1, 2, 3, 4) elif image_size == 64: channel_mult = (1, 2, 3, 4) else: raise ValueError(f"unsupported image size: {image_size}") else: channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(",")) assert 2 ** (len(channel_mult) + 2) == image_size attention_ds = [] for res in attention_resolutions.split(","): attention_ds.append(image_size // int(res)) if super_res: model_cls = SuperResText2ImModel else: model_cls = Text2ImModel n_class = 3 if mode == 'ade20k' or mode == 'coco': n_class = 3 elif mode == 'depth-normal' : n_class = 6 elif mode == 'coco-edge' or mode == 'flickr-edge': n_class = 1 return model_cls( text_ctx=text_ctx, xf_width=xf_width, xf_layers=xf_layers, xf_heads=xf_heads, xf_final_ln=xf_final_ln, model_channels=num_channels, out_channels=(3 if not learn_sigma else 6), num_res_blocks=num_res_blocks, attention_resolutions=tuple(attention_ds), dropout=dropout, channel_mult=channel_mult, use_fp16=use_fp16, num_heads=num_heads, num_heads_upsample=num_heads_upsample, num_head_channels=num_head_channels, use_scale_shift_norm=use_scale_shift_norm, resblock_updown=resblock_updown, in_channels=3, n_class = n_class, image_size = image_size, ) def create_gaussian_diffusion( *, steps=1000, learn_sigma=False, sigma_small=False, noise_schedule="linear", use_kl=False, predict_xstart=False, rescale_timesteps=False, rescale_learned_sigmas=False, timestep_respacing="", ): betas = gd.get_named_beta_schedule(noise_schedule, steps) if use_kl: loss_type = gd.LossType.RESCALED_KL elif rescale_learned_sigmas: loss_type = gd.LossType.RESCALED_MSE else: loss_type = gd.LossType.MSE if not timestep_respacing: timestep_respacing = [steps] return SpacedDiffusion( use_timesteps=space_timesteps(steps, timestep_respacing), betas=betas, model_mean_type=( gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X ), model_var_type=( ( gd.ModelVarType.FIXED_LARGE if not sigma_small else gd.ModelVarType.FIXED_SMALL ) if not learn_sigma else gd.ModelVarType.LEARNED_RANGE ), loss_type=loss_type, rescale_timesteps=rescale_timesteps, ) def add_dict_to_argparser(parser, default_dict): for k, v in default_dict.items(): v_type = type(v) if v is None: v_type = str elif isinstance(v, bool): v_type = str2bool parser.add_argument(f"--{k}", default=v, type=v_type) def args_to_dict(args, keys=None): if keys is None: keys=vars(args) return {k: getattr(args, k) for k in keys} def str2bool(v): """ https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse """ if isinstance(v, bool): return v if v.lower() in ("yes", "true", "t", "y", "1"): return True elif v.lower() in ("no", "false", "f", "n", "0"): return False else: raise argparse.ArgumentTypeError("boolean value expected")