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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).to(device)
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'].to(device)
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
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