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import inspect |
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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, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models |
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from modules.shared import opts, state |
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import k_diffusion.sampling |
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SamplerDataTuple = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) |
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class SamplerData(SamplerDataTuple): |
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def total_steps(self, steps): |
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if self.options.get("second_order", False): |
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steps = steps * 2 |
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return steps |
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def setup_img2img_steps(p, steps=None): |
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if 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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approximation_indexes = {"Full": 0, "Approx NN": 1, "Approx cheap": 2, "TAESD": 3} |
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def samples_to_images_tensor(sample, approximation=None, model=None): |
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"""Transforms 4-channel latent space images into 3-channel RGB image tensors, with values in range [-1, 1].""" |
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if approximation is None or (shared.state.interrupted and opts.live_preview_fast_interrupt): |
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approximation = approximation_indexes.get(opts.show_progress_type, 0) |
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from modules import lowvram |
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if approximation == 0 and lowvram.is_enabled(shared.sd_model) and not shared.opts.live_preview_allow_lowvram_full: |
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approximation = 1 |
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if approximation == 2: |
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x_sample = sd_vae_approx.cheap_approximation(sample) |
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elif approximation == 1: |
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x_sample = sd_vae_approx.model()(sample.to(devices.device, devices.dtype)).detach() |
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elif approximation == 3: |
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x_sample = sd_vae_taesd.decoder_model()(sample.to(devices.device, devices.dtype)).detach() |
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x_sample = x_sample * 2 - 1 |
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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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with devices.without_autocast(): |
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x_sample = model.decode_first_stage(sample.to(model.first_stage_model.dtype)) |
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return x_sample |
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def single_sample_to_image(sample, approximation=None): |
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x_sample = samples_to_images_tensor(sample.unsqueeze(0), approximation)[0] * 0.5 + 0.5 |
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x_sample = torch.clamp(x_sample, min=0.0, max=1.0) |
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) |
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x_sample = x_sample.astype(np.uint8) |
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return Image.fromarray(x_sample) |
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def decode_first_stage(model, x): |
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x = x.to(devices.dtype_vae) |
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approx_index = approximation_indexes.get(opts.sd_vae_decode_method, 0) |
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return samples_to_images_tensor(x, approx_index, model) |
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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(opts.sd_vae_encode_method, 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.encoder_model()(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( |
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model.encode_first_stage(torch.unsqueeze(img, 0)) |
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)[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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state.current_latent = decoded |
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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: |
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if not shared.parallel_processing_allowed: |
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shared.state.assign_current_image(sample_to_image(decoded)) |
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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 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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def replace_torchsde_browinan(): |
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import torchsde._brownian.brownian_interval |
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def torchsde_randn(size, dtype, device, seed): |
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return devices.randn_local(seed, size).to(device=device, dtype=dtype) |
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torchsde._brownian.brownian_interval._randn = torchsde_randn |
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replace_torchsde_browinan() |
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def apply_refiner(cfg_denoiser): |
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completed_ratio = cfg_denoiser.step / cfg_denoiser.total_steps |
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refiner_switch_at = cfg_denoiser.p.refiner_switch_at |
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refiner_checkpoint_info = cfg_denoiser.p.refiner_checkpoint_info |
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if refiner_switch_at is not None and completed_ratio < refiner_switch_at: |
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return False |
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if refiner_checkpoint_info is None or shared.sd_model.sd_checkpoint_info == refiner_checkpoint_info: |
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return False |
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if getattr(cfg_denoiser.p, "enable_hr", False): |
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is_second_pass = cfg_denoiser.p.is_hr_pass |
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if opts.hires_fix_refiner_pass == "first pass" and is_second_pass: |
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return False |
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if opts.hires_fix_refiner_pass == "second pass" and not is_second_pass: |
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return False |
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if opts.hires_fix_refiner_pass != "second pass": |
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cfg_denoiser.p.extra_generation_params['Hires refiner'] = opts.hires_fix_refiner_pass |
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cfg_denoiser.p.extra_generation_params['Refiner'] = refiner_checkpoint_info.short_title |
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cfg_denoiser.p.extra_generation_params['Refiner switch at'] = refiner_switch_at |
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with sd_models.SkipWritingToConfig(): |
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sd_models.reload_model_weights(info=refiner_checkpoint_info) |
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devices.torch_gc() |
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cfg_denoiser.p.setup_conds() |
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cfg_denoiser.update_inner_model() |
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return True |
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class TorchHijack: |
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"""This is here to replace torch.randn_like of k-diffusion. |
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k-diffusion has random_sampler argument for most samplers, but not for all, so |
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this is needed to properly replace every use of torch.randn_like. |
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We need to replace to make images generated in batches to be same as images generated individually.""" |
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def __init__(self, p): |
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self.rng = p.rng |
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def __getattr__(self, item): |
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if item == 'randn_like': |
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return self.randn_like |
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if hasattr(torch, item): |
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return getattr(torch, item) |
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raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'") |
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def randn_like(self, x): |
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return self.rng.next() |
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class Sampler: |
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def __init__(self, funcname): |
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self.funcname = funcname |
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self.func = funcname |
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self.extra_params = [] |
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self.sampler_noises = None |
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self.stop_at = None |
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self.eta = None |
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self.config: SamplerData = None |
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self.last_latent = None |
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self.s_min_uncond = None |
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self.s_churn = 0.0 |
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self.s_tmin = 0.0 |
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self.s_tmax = float('inf') |
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self.s_noise = 1.0 |
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self.eta_option_field = 'eta_ancestral' |
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self.eta_infotext_field = 'Eta' |
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self.eta_default = 1.0 |
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self.conditioning_key = shared.sd_model.model.conditioning_key |
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self.p = None |
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self.model_wrap_cfg = None |
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self.sampler_extra_args = None |
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self.options = {} |
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def callback_state(self, d): |
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step = d['i'] |
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if self.stop_at is not None and step > self.stop_at: |
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raise InterruptedException |
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state.sampling_step = step |
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shared.total_tqdm.update() |
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def launch_sampling(self, steps, func): |
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self.model_wrap_cfg.steps = steps |
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self.model_wrap_cfg.total_steps = self.config.total_steps(steps) |
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state.sampling_steps = steps |
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state.sampling_step = 0 |
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try: |
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return func() |
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except RecursionError: |
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print( |
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'Encountered RecursionError during sampling, returning last latent. ' |
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'rho >5 with a polyexponential scheduler may cause this error. ' |
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'You should try to use a smaller rho value instead.' |
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) |
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return self.last_latent |
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except InterruptedException: |
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return self.last_latent |
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def number_of_needed_noises(self, p): |
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return p.steps |
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def initialize(self, p) -> dict: |
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self.p = p |
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self.model_wrap_cfg.p = p |
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self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None |
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self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None |
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self.model_wrap_cfg.step = 0 |
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self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None) |
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self.eta = p.eta if p.eta is not None else getattr(opts, self.eta_option_field, 0.0) |
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self.s_min_uncond = getattr(p, 's_min_uncond', 0.0) |
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k_diffusion.sampling.torch = TorchHijack(p) |
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extra_params_kwargs = {} |
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for param_name in self.extra_params: |
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if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters: |
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extra_params_kwargs[param_name] = getattr(p, param_name) |
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if 'eta' in inspect.signature(self.func).parameters: |
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if self.eta != self.eta_default: |
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p.extra_generation_params[self.eta_infotext_field] = self.eta |
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extra_params_kwargs['eta'] = self.eta |
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if len(self.extra_params) > 0: |
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s_churn = getattr(opts, 's_churn', p.s_churn) |
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s_tmin = getattr(opts, 's_tmin', p.s_tmin) |
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s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax |
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s_noise = getattr(opts, 's_noise', p.s_noise) |
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if 's_churn' in extra_params_kwargs and s_churn != self.s_churn: |
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extra_params_kwargs['s_churn'] = s_churn |
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p.s_churn = s_churn |
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p.extra_generation_params['Sigma churn'] = s_churn |
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if 's_tmin' in extra_params_kwargs and s_tmin != self.s_tmin: |
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extra_params_kwargs['s_tmin'] = s_tmin |
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p.s_tmin = s_tmin |
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p.extra_generation_params['Sigma tmin'] = s_tmin |
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if 's_tmax' in extra_params_kwargs and s_tmax != self.s_tmax: |
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extra_params_kwargs['s_tmax'] = s_tmax |
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p.s_tmax = s_tmax |
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p.extra_generation_params['Sigma tmax'] = s_tmax |
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if 's_noise' in extra_params_kwargs and s_noise != self.s_noise: |
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extra_params_kwargs['s_noise'] = s_noise |
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p.s_noise = s_noise |
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p.extra_generation_params['Sigma noise'] = s_noise |
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return extra_params_kwargs |
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def create_noise_sampler(self, x, sigmas, p): |
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"""For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes""" |
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if shared.opts.no_dpmpp_sde_batch_determinism: |
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return None |
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from k_diffusion.sampling import BrownianTreeNoiseSampler |
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() |
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current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size] |
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return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds) |
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): |
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raise NotImplementedError() |
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): |
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raise NotImplementedError() |
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