160 lines
7.4 KiB
Python
160 lines
7.4 KiB
Python
import math
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import ldm.models.diffusion.ddim
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import ldm.models.diffusion.plms
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import numpy as np
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import torch
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from modules.shared import state
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from modules import sd_samplers_common, prompt_parser, shared
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samplers_data_compvis = [
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sd_samplers_common.SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
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sd_samplers_common.SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
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]
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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.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 sd_samplers_common.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 sd_samplers_common.InterruptedException
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if self.stop_at is not None and self.step > self.stop_at:
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raise sd_samplers_common.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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sd_samplers_common.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 shared.opts.eta_ddim
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if self.eta != 0.0:
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p.extra_generation_params["Eta DDIM"] = self.eta
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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 == math.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 = sd_samplers_common.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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