552 lines
23 KiB
Python
552 lines
23 KiB
Python
from collections import namedtuple, deque
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import numpy as np
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from math import floor
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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 torchsde._brownian.brownian_interval
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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, images, sd_vae_approx
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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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from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
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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', 'k_euler_ancestral'], {}),
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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'], {'discard_next_to_last_sigma': True}),
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True}),
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('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {}),
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('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
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('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {}),
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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', 'discard_next_to_last_sigma': True}),
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('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True}),
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('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras'}),
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('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
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('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_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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all_samplers_map = {x.name: x for x in all_samplers}
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samplers = []
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samplers_for_img2img = []
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samplers_map = {}
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def create_sampler(name, model):
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if name is not None:
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config = all_samplers_map.get(name, None)
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else:
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config = all_samplers[0]
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assert config is not None, f'bad sampler name: {name}'
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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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samplers_map.clear()
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for sampler in all_samplers:
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samplers_map[sampler.name.lower()] = sampler.name
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for alias in sampler.aliases:
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samplers_map[alias.lower()] = sampler.name
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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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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}
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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(opts.show_progress_type, 0)
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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).unsqueeze(0))[0].detach()
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else:
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x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[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 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 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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class InterruptedException(BaseException):
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pass
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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.is_plms = hasattr(self.sampler, 'p_sample_plms')
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self.orig_p_sample_ddim = self.sampler.p_sample_plms if self.is_plms else self.sampler.p_sample_ddim
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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.stop_at = None
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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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self.last_latent = None
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self.conditioning_key = sd_model.model.conditioning_key
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def number_of_needed_noises(self, p):
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return 0
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def launch_sampling(self, steps, func):
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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 InterruptedException:
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return self.last_latent
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def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
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if state.interrupted or state.skipped:
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raise InterruptedException
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if self.stop_at is not None and self.step > self.stop_at:
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raise InterruptedException
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# Have to unwrap the inpainting conditioning here to perform pre-processing
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image_conditioning = None
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if isinstance(cond, dict):
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image_conditioning = cond["c_concat"][0]
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cond = cond["c_crossattn"][0]
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unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
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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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# Wrap the image conditioning back up since the DDIM code can accept the dict directly.
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# Note that they need to be lists because it just concatenates them later.
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if image_conditioning is not None:
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cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
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unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
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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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self.last_latent = self.init_latent * self.mask + self.nmask * res[1]
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else:
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self.last_latent = res[1]
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store_latent(self.last_latent)
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self.step += 1
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state.sampling_step = self.step
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shared.total_tqdm.update()
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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 adjust_steps_if_invalid(self, p, num_steps):
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if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
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valid_step = 999 / (1000 // num_steps)
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if valid_step == floor(valid_step):
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return int(valid_step) + 1
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return num_steps
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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steps, t_enc = setup_img2img_steps(p, steps)
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steps = self.adjust_steps_if_invalid(p, steps)
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self.initialize(p)
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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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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.last_latent = x
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self.step = 0
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# Wrap the conditioning models with additional image conditioning for inpainting model
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if image_conditioning is not None:
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conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
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unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
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samples = self.launch_sampling(t_enc + 1, lambda: 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, image_conditioning=None):
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self.initialize(p)
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self.init_latent = None
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self.last_latent = x
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self.step = 0
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steps = self.adjust_steps_if_invalid(p, steps or p.steps)
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# Wrap the conditioning models with additional image conditioning for inpainting model
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# dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape
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if image_conditioning is not None:
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conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]}
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unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]}
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samples_ddim = self.launch_sampling(steps, lambda: 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)[0])
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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 combine_denoised(self, x_out, conds_list, uncond, cond_scale):
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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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return denoised
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def forward(self, x, sigma, uncond, cond, cond_scale, image_cond):
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if state.interrupted or state.skipped:
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raise InterruptedException
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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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image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond])
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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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denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)
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cfg_denoiser_callback(denoiser_params)
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x_in = denoiser_params.x
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image_cond_in = denoiser_params.image_cond
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sigma_in = denoiser_params.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={"c_crossattn": [cond_in], "c_concat": [image_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={"c_crossattn": [cond_in[a:b]], "c_concat": [image_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={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[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={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]})
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devices.test_for_nans(x_out, "unet")
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if opts.live_preview_content == "Prompt":
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store_latent(x_out[0:uncond.shape[0]])
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elif opts.live_preview_content == "Negative prompt":
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store_latent(x_out[-uncond.shape[0]:])
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denoised = self.combine_denoised(x_out, conds_list, uncond, 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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class TorchHijack:
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def __init__(self, sampler_noises):
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# Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
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# implementation.
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self.sampler_noises = deque(sampler_noises)
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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("'{}' object has no attribute '{}'".format(type(self).__name__, item))
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def randn_like(self, x):
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if self.sampler_noises:
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noise = self.sampler_noises.popleft()
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if noise.shape == x.shape:
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return noise
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if x.device.type == 'mps':
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return torch.randn_like(x, device=devices.cpu).to(x.device)
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else:
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return torch.randn_like(x)
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# MPS fix for randn in torchsde
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def torchsde_randn(size, dtype, device, seed):
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if device.type == 'mps':
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generator = torch.Generator(devices.cpu).manual_seed(int(seed))
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return torch.randn(size, dtype=dtype, device=devices.cpu, generator=generator).to(device)
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else:
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generator = torch.Generator(device).manual_seed(int(seed))
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return torch.randn(size, dtype=dtype, device=device, generator=generator)
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torchsde._brownian.brownian_interval._randn = torchsde_randn
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class KDiffusionSampler:
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def __init__(self, funcname, sd_model):
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denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
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self.model_wrap = denoiser(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.stop_at = None
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|
self.eta = None
|
|
self.default_eta = 1.0
|
|
self.config = None
|
|
self.last_latent = None
|
|
|
|
self.conditioning_key = sd_model.model.conditioning_key
|
|
|
|
def callback_state(self, d):
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|
step = d['i']
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|
latent = d["denoised"]
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|
if opts.live_preview_content == "Combined":
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|
store_latent(latent)
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|
self.last_latent = latent
|
|
|
|
if self.stop_at is not None and step > self.stop_at:
|
|
raise InterruptedException
|
|
|
|
state.sampling_step = step
|
|
shared.total_tqdm.update()
|
|
|
|
def launch_sampling(self, steps, func):
|
|
state.sampling_steps = steps
|
|
state.sampling_step = 0
|
|
|
|
try:
|
|
return func()
|
|
except InterruptedException:
|
|
return self.last_latent
|
|
|
|
def number_of_needed_noises(self, p):
|
|
return p.steps
|
|
|
|
def initialize(self, p):
|
|
self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
|
|
self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
|
|
self.model_wrap_cfg.step = 0
|
|
self.eta = p.eta or opts.eta_ancestral
|
|
|
|
k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
|
|
|
|
extra_params_kwargs = {}
|
|
for param_name in self.extra_params:
|
|
if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs[param_name] = getattr(p, param_name)
|
|
|
|
if 'eta' in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs['eta'] = self.eta
|
|
|
|
return extra_params_kwargs
|
|
|
|
def get_sigmas(self, p, steps):
|
|
discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
|
|
if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
|
|
discard_next_to_last_sigma = True
|
|
p.extra_generation_params["Discard penultimate sigma"] = True
|
|
|
|
steps += 1 if discard_next_to_last_sigma else 0
|
|
|
|
if p.sampler_noise_scheduler_override:
|
|
sigmas = p.sampler_noise_scheduler_override(steps)
|
|
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
|
|
sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
|
|
|
|
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
|
|
else:
|
|
sigmas = self.model_wrap.get_sigmas(steps)
|
|
|
|
if discard_next_to_last_sigma:
|
|
sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
|
|
|
|
return sigmas
|
|
|
|
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
|
|
steps, t_enc = setup_img2img_steps(p, steps)
|
|
|
|
sigmas = self.get_sigmas(p, steps)
|
|
|
|
sigma_sched = sigmas[steps - t_enc - 1:]
|
|
xi = x + noise * sigma_sched[0]
|
|
|
|
extra_params_kwargs = self.initialize(p)
|
|
if 'sigma_min' in inspect.signature(self.func).parameters:
|
|
## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
|
|
extra_params_kwargs['sigma_min'] = sigma_sched[-2]
|
|
if 'sigma_max' in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs['sigma_max'] = sigma_sched[0]
|
|
if 'n' in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs['n'] = len(sigma_sched) - 1
|
|
if 'sigma_sched' in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs['sigma_sched'] = sigma_sched
|
|
if 'sigmas' in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs['sigmas'] = sigma_sched
|
|
|
|
self.model_wrap_cfg.init_latent = x
|
|
self.last_latent = x
|
|
|
|
samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args={
|
|
'cond': conditioning,
|
|
'image_cond': image_conditioning,
|
|
'uncond': unconditional_conditioning,
|
|
'cond_scale': p.cfg_scale
|
|
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
|
|
|
return samples
|
|
|
|
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
|
|
steps = steps or p.steps
|
|
|
|
sigmas = self.get_sigmas(p, steps)
|
|
|
|
x = x * sigmas[0]
|
|
|
|
extra_params_kwargs = self.initialize(p)
|
|
if 'sigma_min' in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
|
|
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
|
|
if 'n' in inspect.signature(self.func).parameters:
|
|
extra_params_kwargs['n'] = steps
|
|
else:
|
|
extra_params_kwargs['sigmas'] = sigmas
|
|
|
|
self.last_latent = x
|
|
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
|
|
'cond': conditioning,
|
|
'image_cond': image_conditioning,
|
|
'uncond': unconditional_conditioning,
|
|
'cond_scale': p.cfg_scale
|
|
}, disable=False, callback=self.callback_state, **extra_params_kwargs))
|
|
|
|
return samples
|
|
|