mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 07:03:06 +03:00
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
from collections import namedtuple
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image
|
|
from modules import devices, processing, images, sd_vae_approx, sd_samplers, sd_vae_taesd
|
|
|
|
from modules.shared import opts, state
|
|
import modules.shared as shared
|
|
|
|
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 single_sample_to_image(sample, approximation=None):
|
|
|
|
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).unsqueeze(0))[0].detach()
|
|
elif approximation == 3:
|
|
x_sample = sample * 1.5
|
|
x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
|
|
else:
|
|
x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
|
|
|
|
if approximation != 3:
|
|
x_sample = (x_sample + 1.0) / 2.0
|
|
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 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 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
|
|
|
|
|
|
if opts.randn_source == "CPU":
|
|
import torchsde._brownian.brownian_interval
|
|
|
|
def torchsde_randn(size, dtype, device, seed):
|
|
generator = torch.Generator(devices.cpu).manual_seed(int(seed))
|
|
return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
|
|
|
|
torchsde._brownian.brownian_interval._randn = torchsde_randn
|