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from collections import namedtuple
import numpy as np
import torch
from PIL import Image
from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
from modules.shared import opts, state
SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
def setup_img2img_steps(p, steps=None):
if opts.img2img_fix_steps or steps is not None:
requested_steps = (steps or p.steps)
steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
t_enc = requested_steps - 1
else:
steps = p.steps
t_enc = int(min(p.denoising_strength, 0.999) * steps)
return steps, t_enc
approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3}
def samples_to_images_tensor(sample, approximation=None, model=None):
'''latents -> images [-1, 1]'''
if approximation is None:
approximation = approximation_indexes.get(opts.show_progress_type, 0)
if approximation == 2:
x_sample = sd_vae_approx.cheap_approximation(sample)
elif approximation == 1:
x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach()
elif approximation == 3:
x_sample = sample * 1.5
x_sample = sd_vae_taesd.decoder_model()(x_sample.to(devices.device, devices.dtype)).detach()
x_sample = x_sample * 2 - 1
else:
if model is None:
model = shared.sd_model
x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype))
return x_sample
def single_sample_to_image(sample, approximation=None):
x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5
x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
x_sample = x_sample.astype(np.uint8)
return Image.fromarray(x_sample)
def decode_first_stage(model, x):
x = x.to(devices.dtype_vae)
approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0)
return samples_to_images_tensor(x, approx_index, model)
def sample_to_image(samples, index=0, approximation=None):
return single_sample_to_image(samples[index], approximation)
def samples_to_image_grid(samples, approximation=None):
return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
def images_tensor_to_samples(image, approximation=None, model=None):
'''image[0, 1] -> latent'''
if approximation is None:
approximation = approximation_indexes.get(opts.sd_vae_encode_method, 0)
if approximation == 3:
image = image.to(devices.device, devices.dtype)
x_latent = sd_vae_taesd.encoder_model()(image)
else:
if model is None:
model = shared.sd_model
image = image.to(shared.device, dtype=devices.dtype_vae)
image = image * 2 - 1
x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
return x_latent
def store_latent(decoded):
state.current_latent = decoded
if opts.live_previews_enable and opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % opts.show_progress_every_n_steps == 0:
if not shared.parallel_processing_allowed:
shared.state.assign_current_image(sample_to_image(decoded))
def is_sampler_using_eta_noise_seed_delta(p):
"""returns whether sampler from config will use eta noise seed delta for image creation"""
sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
eta = p.eta
if eta is None and p.sampler is not None:
eta = p.sampler.eta
if eta is None and sampler_config is not None:
eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
if eta == 0:
return False
return sampler_config.options.get("uses_ensd", False)
class InterruptedException(BaseException):
pass
def replace_torchsde_browinan():
import torchsde._brownian.brownian_interval
def torchsde_randn(size, dtype, device, seed):
return devices.randn_local(seed, size).to(device=device, dtype=dtype)
torchsde._brownian.brownian_interval._randn = torchsde_randn
replace_torchsde_browinan()
def apply_refiner(sampler):
completed_ratio = sampler.step / sampler.steps
if completed_ratio <= shared.opts.sd_refiner_switch_at:
return False
if shared.sd_model.sd_checkpoint_info.title == shared.opts.sd_refiner_checkpoint:
return False
refiner_checkpoint_info = sd_models.get_closet_checkpoint_match(shared.opts.sd_refiner_checkpoint)
if refiner_checkpoint_info is None:
raise Exception(f'Could not find checkpoint with name {shared.opts.sd_refiner_checkpoint}')
sampler.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title
sampler.p.extra_generation_params['Refiner switch at'] = shared.opts.sd_refiner_switch_at
with sd_models.SkipWritingToConfig():
sd_models.reload_model_weights(info=refiner_checkpoint_info)
devices.torch_gc()
sampler.p.setup_conds()
sampler.update_inner_model()
return True
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