mirror of https://github.com/vladmandic/automatic
106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
from collections import namedtuple
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import numpy as np
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import torch
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from PIL import Image
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from modules import devices, processing, images, sd_vae_approx, sd_samplers
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import modules.shared as shared
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import modules.taesd.sd_vae_taesd as sd_vae_taesd
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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approximation_indexes = {"Full VAE": 0, "Approximate NN": 1, "Approximate simple": 2, "TAESD": 3}
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def setup_img2img_steps(p, steps=None):
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if shared.opts.img2img_fix_steps or steps is not None:
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requested_steps = (steps or p.steps)
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steps = int(requested_steps / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
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t_enc = requested_steps - 1
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else:
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steps = p.steps
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t_enc = int(min(p.denoising_strength, 0.999) * steps)
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return steps, t_enc
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def single_sample_to_image(sample, approximation=None):
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if approximation is None:
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approximation = approximation_indexes.get(shared.opts.show_progress_type, 0)
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if approximation == 0:
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x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] * 0.5 + 0.5
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elif approximation == 1:
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x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach() * 0.5 + 0.5
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if shared.sd_model_type == "sdxl":
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x_sample = x_sample[[2,1,0],:,:] # BGR to RGB
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elif approximation == 2:
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x_sample = sd_vae_approx.cheap_approximation(sample) * 0.5 + 0.5
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if shared.sd_model_type == "sdxl":
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x_sample = x_sample[[2,1,0],:,:] # BGR to RGB
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elif approximation == 3:
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# x_sample = sample * 1.5
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# x_sample = sd_vae_taesd.model()(x_sample.to(devices.device, devices.dtype).unsqueeze(0))[0].detach()
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x_sample = sd_vae_taesd.decode(sample)
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else:
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shared.log.warning(f"Unknown image decode type: {approximation}")
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return Image.new(mode="RGB", size=(512, 512))
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x_sample = torch.clamp(255 * x_sample, min=0.0, max=255).cpu()
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x_sample = np.moveaxis(x_sample.numpy(), 0, 2).astype(np.uint8)
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return Image.fromarray(x_sample)
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def sample_to_image(samples, index=0, approximation=None):
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return single_sample_to_image(samples[index], approximation)
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def samples_to_image_grid(samples, approximation=None):
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return images.image_grid([single_sample_to_image(sample, approximation) for sample in samples])
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def images_tensor_to_samples(image, approximation=None, model=None):
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'''image[0, 1] -> latent'''
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if approximation is None:
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approximation = approximation_indexes.get(shared.opts.show_progress_type, 0)
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if approximation == 3:
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image = image.to(devices.device, devices.dtype)
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x_latent = sd_vae_taesd.encode(image)
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else:
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if model is None:
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model = shared.sd_model
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model.first_stage_model.to(devices.dtype_vae)
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image = image.to(shared.device, dtype=devices.dtype_vae)
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image = image * 2 - 1
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if len(image) > 1:
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x_latent = torch.stack([
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model.get_first_stage_encoding(model.encode_first_stage(torch.unsqueeze(img, 0)))[0]
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for img in image
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])
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else:
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x_latent = model.get_first_stage_encoding(model.encode_first_stage(image))
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return x_latent
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def store_latent(decoded):
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shared.state.current_latent = decoded
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if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps > 0 and shared.state.sampling_step % shared.opts.show_progress_every_n_steps == 0:
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if not shared.parallel_processing_allowed:
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image = sample_to_image(decoded)
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shared.state.assign_current_image(image)
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def is_sampler_using_eta_noise_seed_delta(p):
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"""returns whether sampler from config will use eta noise seed delta for image creation"""
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sampler_config = sd_samplers.find_sampler_config(p.sampler_name)
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eta = p.eta
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if not hasattr(p.sampler, "eta"):
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return False
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if eta is None and p.sampler is not None:
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eta = p.sampler.eta
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if eta is None and sampler_config is not None:
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eta = 0 if sampler_config.options.get("default_eta_is_0", False) else 1.0
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if eta == 0:
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return False
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return sampler_config.options.get("uses_ensd", False)
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class InterruptedException(BaseException):
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pass
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