412 lines
17 KiB
Python
412 lines
17 KiB
Python
from collections import namedtuple
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import numpy as np
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import torch
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import tqdm
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from PIL import Image
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import inspect
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import k_diffusion.sampling
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import ldm.models.diffusion.ddim
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import ldm.models.diffusion.plms
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from modules import prompt_parser, devices, processing
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
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samplers_k_diffusion = [
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('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
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('Euler', 'sample_euler', ['k_euler'], {}),
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('LMS', 'sample_lms', ['k_lms'], {}),
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('Heun', 'sample_heun', ['k_heun'], {}),
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('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
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('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
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('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
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('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
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]
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samplers_data_k_diffusion = [
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SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
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for label, funcname, aliases, options in samplers_k_diffusion
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if hasattr(k_diffusion.sampling, funcname)
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]
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all_samplers = [
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*samplers_data_k_diffusion,
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SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
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SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
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]
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samplers = []
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samplers_for_img2img = []
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def create_sampler_with_index(list_of_configs, index, model):
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config = list_of_configs[index]
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sampler = config.constructor(model)
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sampler.config = config
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return sampler
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def set_samplers():
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global samplers, samplers_for_img2img
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hidden = set(opts.hide_samplers)
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hidden_img2img = set(opts.hide_samplers + ['PLMS'])
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samplers = [x for x in all_samplers if x.name not in hidden]
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samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
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set_samplers()
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sampler_extra_params = {
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'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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}
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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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steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
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t_enc = p.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 sample_to_image(samples):
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x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, 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 store_latent(decoded):
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state.current_latent = decoded
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if 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.current_image = sample_to_image(decoded)
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def extended_tdqm(sequence, *args, desc=None, **kwargs):
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state.sampling_steps = len(sequence)
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state.sampling_step = 0
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seq = sequence if cmd_opts.disable_console_progressbars else tqdm.tqdm(sequence, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
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for x in seq:
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if state.interrupted or state.skipped:
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break
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yield x
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state.sampling_step += 1
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shared.total_tqdm.update()
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ldm.models.diffusion.ddim.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
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ldm.models.diffusion.plms.tqdm = lambda *args, desc=None, **kwargs: extended_tdqm(*args, desc=desc, **kwargs)
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class VanillaStableDiffusionSampler:
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def __init__(self, constructor, sd_model):
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self.sampler = constructor(sd_model)
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self.orig_p_sample_ddim = self.sampler.p_sample_ddim if hasattr(self.sampler, 'p_sample_ddim') else self.sampler.p_sample_plms
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self.mask = None
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self.nmask = None
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self.init_latent = None
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self.sampler_noises = None
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self.step = 0
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self.eta = None
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self.default_eta = 0.0
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self.config = None
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def number_of_needed_noises(self, p):
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return 0
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def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
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conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
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unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
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assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
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cond = tensor
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# for DDIM, shapes must match, we can't just process cond and uncond independently;
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# filling unconditional_conditioning with repeats of the last vector to match length is
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# not 100% correct but should work well enough
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if unconditional_conditioning.shape[1] < cond.shape[1]:
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last_vector = unconditional_conditioning[:, -1:]
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last_vector_repeated = last_vector.repeat([1, cond.shape[1] - unconditional_conditioning.shape[1], 1])
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unconditional_conditioning = torch.hstack([unconditional_conditioning, last_vector_repeated])
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elif unconditional_conditioning.shape[1] > cond.shape[1]:
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unconditional_conditioning = unconditional_conditioning[:, :cond.shape[1]]
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if self.mask is not None:
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img_orig = self.sampler.model.q_sample(self.init_latent, ts)
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x_dec = img_orig * self.mask + self.nmask * x_dec
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res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs)
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if self.mask is not None:
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store_latent(self.init_latent * self.mask + self.nmask * res[1])
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else:
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store_latent(res[1])
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self.step += 1
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return res
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def initialize(self, p):
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self.eta = p.eta if p.eta is not None else opts.eta_ddim
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for fieldname in ['p_sample_ddim', 'p_sample_plms']:
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if hasattr(self.sampler, fieldname):
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setattr(self.sampler, fieldname, self.p_sample_ddim_hook)
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self.mask = p.mask if hasattr(p, 'mask') else None
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self.nmask = p.nmask if hasattr(p, 'nmask') else None
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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self.initialize(p)
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# existing code fails with certain step counts, like 9
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try:
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self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
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except Exception:
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self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
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x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
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self.init_latent = x
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self.step = 0
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samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)
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return samples
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
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self.initialize(p)
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self.init_latent = None
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self.step = 0
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steps = steps or p.steps
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# existing code fails with certain step counts, like 9
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try:
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samples_ddim, _ = self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
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except Exception:
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samples_ddim, _ = self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)
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return samples_ddim
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class CFGDenoiser(torch.nn.Module):
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def __init__(self, model):
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super().__init__()
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self.inner_model = model
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self.mask = None
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self.nmask = None
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self.init_latent = None
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self.step = 0
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def forward(self, x, sigma, uncond, cond, cond_scale):
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conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
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uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
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batch_size = len(conds_list)
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repeats = [len(conds_list[i]) for i in range(batch_size)]
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x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
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sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
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if tensor.shape[1] == uncond.shape[1]:
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cond_in = torch.cat([tensor, uncond])
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if shared.batch_cond_uncond:
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x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
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else:
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x_out = torch.zeros_like(x_in)
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for batch_offset in range(0, x_out.shape[0], batch_size):
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a = batch_offset
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b = a + batch_size
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
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else:
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x_out = torch.zeros_like(x_in)
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batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size
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for batch_offset in range(0, tensor.shape[0], batch_size):
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a = batch_offset
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b = min(a + batch_size, tensor.shape[0])
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x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b])
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x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond)
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denoised_uncond = x_out[-uncond.shape[0]:]
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denoised = torch.clone(denoised_uncond)
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for i, conds in enumerate(conds_list):
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for cond_index, weight in conds:
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denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
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if self.mask is not None:
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denoised = self.init_latent * self.mask + self.nmask * denoised
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self.step += 1
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return denoised
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def extended_trange(sampler, count, *args, **kwargs):
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state.sampling_steps = count
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state.sampling_step = 0
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seq = range(count) if cmd_opts.disable_console_progressbars else tqdm.trange(count, *args, desc=state.job, file=shared.progress_print_out, **kwargs)
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for x in seq:
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if state.interrupted or state.skipped:
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break
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if sampler.stop_at is not None and x > sampler.stop_at:
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break
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yield x
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state.sampling_step += 1
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shared.total_tqdm.update()
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class TorchHijack:
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def __init__(self, kdiff_sampler):
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self.kdiff_sampler = kdiff_sampler
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def __getattr__(self, item):
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if item == 'randn_like':
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return self.kdiff_sampler.randn_like
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if hasattr(torch, item):
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return getattr(torch, item)
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raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, item))
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class KDiffusionSampler:
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def __init__(self, funcname, sd_model):
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self.model_wrap = k_diffusion.external.CompVisDenoiser(sd_model, quantize=shared.opts.enable_quantization)
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self.funcname = funcname
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self.func = getattr(k_diffusion.sampling, self.funcname)
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self.extra_params = sampler_extra_params.get(funcname, [])
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self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
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self.sampler_noises = None
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self.sampler_noise_index = 0
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self.stop_at = None
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self.eta = None
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self.default_eta = 1.0
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self.config = None
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def callback_state(self, d):
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store_latent(d["denoised"])
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def number_of_needed_noises(self, p):
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return p.steps
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def randn_like(self, x):
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noise = self.sampler_noises[self.sampler_noise_index] if self.sampler_noises is not None and self.sampler_noise_index < len(self.sampler_noises) else None
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if noise is not None and x.shape == noise.shape:
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res = noise
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else:
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res = torch.randn_like(x)
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self.sampler_noise_index += 1
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return res
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def initialize(self, 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.step = 0
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self.sampler_noise_index = 0
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self.eta = p.eta or opts.eta_ancestral
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if hasattr(k_diffusion.sampling, 'trange'):
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k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs)
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if self.sampler_noises is not None:
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k_diffusion.sampling.torch = TorchHijack(self)
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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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extra_params_kwargs['eta'] = self.eta
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return extra_params_kwargs
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
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sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
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else:
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sigmas = self.model_wrap.get_sigmas(steps)
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sigma_sched = sigmas[steps - t_enc - 1:]
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xi = x + noise * sigma_sched[0]
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extra_params_kwargs = self.initialize(p)
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if 'sigma_min' in inspect.signature(self.func).parameters:
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## last sigma is zero which is allowed by DPM Fast & Adaptive so taking value before last
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extra_params_kwargs['sigma_min'] = sigma_sched[-2]
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if 'sigma_max' in inspect.signature(self.func).parameters:
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extra_params_kwargs['sigma_max'] = sigma_sched[0]
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if 'n' in inspect.signature(self.func).parameters:
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extra_params_kwargs['n'] = len(sigma_sched) - 1
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if 'sigma_sched' in inspect.signature(self.func).parameters:
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extra_params_kwargs['sigma_sched'] = sigma_sched
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if 'sigmas' in inspect.signature(self.func).parameters:
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extra_params_kwargs['sigmas'] = sigma_sched
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self.model_wrap_cfg.init_latent = x
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return self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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def sample(self, p, x, conditioning, unconditional_conditioning, steps=None):
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steps = steps or p.steps
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if p.sampler_noise_scheduler_override:
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sigmas = p.sampler_noise_scheduler_override(steps)
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elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
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sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
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else:
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sigmas = self.model_wrap.get_sigmas(steps)
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x = x * sigmas[0]
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extra_params_kwargs = self.initialize(p)
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if 'sigma_min' in inspect.signature(self.func).parameters:
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extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
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extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
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if 'n' in inspect.signature(self.func).parameters:
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extra_params_kwargs['n'] = steps
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else:
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extra_params_kwargs['sigmas'] = sigmas
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samples = self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)
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return samples
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