mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-27 07:39:53 +08:00
169 lines
6.5 KiB
Python
169 lines
6.5 KiB
Python
import torch
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import inspect
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import sys
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from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl
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from modules.sd_samplers_cfg_denoiser import CFGDenoiser
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from modules.script_callbacks import ExtraNoiseParams, extra_noise_callback
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from modules.shared import opts
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import modules.shared as shared
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samplers_timesteps = [
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('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
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('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
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('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}),
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]
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samplers_data_timesteps = [
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sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options)
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for label, funcname, aliases, options in samplers_timesteps
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]
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class CompVisTimestepsDenoiser(torch.nn.Module):
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def __init__(self, model, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.inner_model = model
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def forward(self, input, timesteps, **kwargs):
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return self.inner_model.apply_model(input, timesteps, **kwargs)
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class CompVisTimestepsVDenoiser(torch.nn.Module):
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def __init__(self, model, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.inner_model = model
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def predict_eps_from_z_and_v(self, x_t, t, v):
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return torch.sqrt(self.inner_model.alphas_cumprod)[t.to(torch.int), None, None, None] * v + torch.sqrt(1 - self.inner_model.alphas_cumprod)[t.to(torch.int), None, None, None] * x_t
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def forward(self, input, timesteps, **kwargs):
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model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
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e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
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return e_t
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class CFGDenoiserTimesteps(CFGDenoiser):
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def __init__(self, sampler):
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super().__init__(sampler)
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self.alphas = shared.sd_model.alphas_cumprod
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self.mask_before_denoising = True
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def get_pred_x0(self, x_in, x_out, sigma):
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ts = sigma.to(dtype=int)
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a_t = self.alphas[ts][:, None, None, None]
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sqrt_one_minus_at = (1 - a_t).sqrt()
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pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
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return pred_x0
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@property
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def inner_model(self):
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if self.model_wrap is None:
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denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
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self.model_wrap = denoiser(shared.sd_model)
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return self.model_wrap
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class CompVisSampler(sd_samplers_common.Sampler):
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def __init__(self, funcname, sd_model):
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super().__init__(funcname)
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self.eta_option_field = 'eta_ddim'
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self.eta_infotext_field = 'Eta DDIM'
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self.eta_default = 0.0
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self.model_wrap_cfg = CFGDenoiserTimesteps(self)
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self.model_wrap = self.model_wrap_cfg.inner_model
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def get_timesteps(self, p, steps):
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discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
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if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
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discard_next_to_last_sigma = True
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p.extra_generation_params["Discard penultimate sigma"] = True
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steps += 1 if discard_next_to_last_sigma else 0
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timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999)
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return timesteps
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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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timesteps = self.get_timesteps(p, steps)
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timesteps_sched = timesteps[:t_enc]
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alphas_cumprod = shared.sd_model.alphas_cumprod
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sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]])
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sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]])
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xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod
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if opts.img2img_extra_noise > 0:
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p.extra_generation_params["Extra noise"] = opts.img2img_extra_noise
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extra_noise_params = ExtraNoiseParams(noise, x, xi)
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extra_noise_callback(extra_noise_params)
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noise = extra_noise_params.noise
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xi += noise * opts.img2img_extra_noise * sqrt_alpha_cumprod
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extra_params_kwargs = self.initialize(p)
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parameters = inspect.signature(self.func).parameters
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if 'timesteps' in parameters:
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extra_params_kwargs['timesteps'] = timesteps_sched
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if 'is_img2img' in parameters:
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extra_params_kwargs['is_img2img'] = True
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self.model_wrap_cfg.init_latent = x
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self.last_latent = x
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self.sampler_extra_args = {
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'cond': conditioning,
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'image_cond': image_conditioning,
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'uncond': unconditional_conditioning,
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'cond_scale': p.cfg_scale,
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's_min_uncond': self.s_min_uncond
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}
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samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
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if self.model_wrap_cfg.padded_cond_uncond:
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p.extra_generation_params["Pad conds"] = True
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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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steps = steps or p.steps
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timesteps = self.get_timesteps(p, steps)
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extra_params_kwargs = self.initialize(p)
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parameters = inspect.signature(self.func).parameters
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if 'timesteps' in parameters:
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extra_params_kwargs['timesteps'] = timesteps
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self.last_latent = x
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self.sampler_extra_args = {
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'cond': conditioning,
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'image_cond': image_conditioning,
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'uncond': unconditional_conditioning,
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'cond_scale': p.cfg_scale,
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's_min_uncond': self.s_min_uncond
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}
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samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
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if self.model_wrap_cfg.padded_cond_uncond:
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p.extra_generation_params["Pad conds"] = True
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return samples
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sys.modules['modules.sd_samplers_compvis'] = sys.modules[__name__]
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VanillaStableDiffusionSampler = CompVisSampler # temp. compatibility with older extensions
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