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| import os | |
| from typing import Tuple | |
| #from . import dist_util | |
| import PIL | |
| import numpy as np | |
| import torch as th | |
| from .script_util import ( | |
| create_gaussian_diffusion, | |
| create_model_and_diffusion, | |
| model_and_diffusion_defaults, | |
| ) | |
| # Sample from the base model. | |
| #@th.inference_mode() | |
| def sample( | |
| glide_model, | |
| glide_options, | |
| side_x, | |
| side_y, | |
| prompt, | |
| batch_size=1, | |
| guidance_scale=4, | |
| device="cpu", | |
| prediction_respacing="100", | |
| upsample_enabled=False, | |
| upsample_temp=0.997, | |
| mode = '', | |
| ): | |
| eval_diffusion = create_gaussian_diffusion( | |
| steps=glide_options["diffusion_steps"], | |
| learn_sigma=glide_options["learn_sigma"], | |
| noise_schedule=glide_options["noise_schedule"], | |
| predict_xstart=glide_options["predict_xstart"], | |
| rescale_timesteps=glide_options["rescale_timesteps"], | |
| rescale_learned_sigmas=glide_options["rescale_learned_sigmas"], | |
| timestep_respacing=prediction_respacing | |
| ) | |
| # Create the classifier-free guidance tokens (empty) | |
| full_batch_size = batch_size * 2 | |
| cond_ref = prompt['ref'] | |
| uncond_ref = th.ones_like(cond_ref) | |
| model_kwargs = {} | |
| model_kwargs['ref'] = th.cat([cond_ref, uncond_ref], 0).cuda() | |
| def cfg_model_fn(x_t, ts, **kwargs): | |
| half = x_t[: len(x_t) // 2] | |
| combined = th.cat([half, half], dim=0) | |
| model_out = glide_model(combined, ts, **kwargs) | |
| eps, rest = model_out[:, :3], model_out[:, 3:] | |
| cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0) | |
| half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps) | |
| eps = th.cat([half_eps, half_eps], dim=0) | |
| return th.cat([eps, rest], dim=1) | |
| if upsample_enabled: | |
| model_kwargs['low_res'] = prompt['low_res'].cuda() | |
| noise = th.randn((batch_size, 3, side_y, side_x), device=device) * upsample_temp | |
| model_fn = glide_model # just use the base model, no need for CFG. | |
| model_kwargs['ref'] = model_kwargs['ref'][:batch_size] | |
| samples = eval_diffusion.p_sample_loop( | |
| model_fn, | |
| (batch_size, 3, side_y, side_x), # only thing that's changed | |
| noise=noise, | |
| device=device, | |
| clip_denoised=True, | |
| progress=False, | |
| model_kwargs=model_kwargs, | |
| cond_fn=None, | |
| )[:batch_size] | |
| else: | |
| model_fn = cfg_model_fn # so we use CFG for the base model. | |
| noise = th.randn((batch_size, 3, side_y, side_x), device=device) | |
| noise = th.cat([noise, noise], 0) | |
| samples = eval_diffusion.p_sample_loop( | |
| model_fn, | |
| (full_batch_size, 3, side_y, side_x), # only thing that's changed | |
| noise=noise, | |
| device=device, | |
| clip_denoised=True, | |
| progress=False, | |
| model_kwargs=model_kwargs, | |
| cond_fn=None, | |
| )[:batch_size] | |
| return samples | |