mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
112 lines
4.2 KiB
Python
112 lines
4.2 KiB
Python
"""SAMPLING ONLY."""
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import torch
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from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
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from ldm.models.diffusion.sampler import Sampler
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from ldm.modules.diffusionmodules.util import noise_like
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class DDIMSampler(Sampler):
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def __init__(self, model, schedule='linear', device=None, **kwargs):
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super().__init__(model,schedule,model.num_timesteps,device)
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self.invokeai_diffuser = InvokeAIDiffuserComponent(self.model,
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model_forward_callback = lambda x, sigma, cond: self.model.apply_model(x, sigma, cond))
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def prepare_to_sample(self, t_enc, **kwargs):
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super().prepare_to_sample(t_enc, **kwargs)
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extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
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all_timesteps_count = kwargs.get('all_timesteps_count', t_enc)
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if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info, step_count = all_timesteps_count)
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else:
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self.invokeai_diffuser.remove_cross_attention_control()
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# This is the central routine
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@torch.no_grad()
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def p_sample(
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self,
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x,
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c,
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t,
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index,
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repeat_noise=False,
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use_original_steps=False,
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quantize_denoised=False,
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temperature=1.0,
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noise_dropout=0.0,
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score_corrector=None,
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corrector_kwargs=None,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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step_count:int=1000, # total number of steps
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**kwargs,
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):
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b, *_, device = *x.shape, x.device
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if (
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unconditional_conditioning is None
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or unconditional_guidance_scale == 1.0
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):
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# damian0815 would like to know when/if this code path is used
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e_t = self.model.apply_model(x, t, c)
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else:
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# step_index counts in the opposite direction to index
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step_index = step_count-(index+1)
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e_t = self.invokeai_diffuser.do_diffusion_step(
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x, t,
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unconditional_conditioning, c,
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unconditional_guidance_scale,
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step_index=step_index
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)
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if score_corrector is not None:
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assert self.model.parameterization == 'eps'
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e_t = score_corrector.modify_score(
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self.model, e_t, x, t, c, **corrector_kwargs
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)
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alphas = (
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self.model.alphas_cumprod
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if use_original_steps
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else self.ddim_alphas
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)
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alphas_prev = (
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self.model.alphas_cumprod_prev
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if use_original_steps
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else self.ddim_alphas_prev
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)
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sqrt_one_minus_alphas = (
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self.model.sqrt_one_minus_alphas_cumprod
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if use_original_steps
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else self.ddim_sqrt_one_minus_alphas
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)
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sigmas = (
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self.model.ddim_sigmas_for_original_num_steps
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if use_original_steps
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else self.ddim_sigmas
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)
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# select parameters corresponding to the currently considered timestep
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a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
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a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
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sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
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sqrt_one_minus_at = torch.full(
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(b, 1, 1, 1), sqrt_one_minus_alphas[index], device=device
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)
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# current prediction for x_0
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pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
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if quantize_denoised:
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pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
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# direction pointing to x_t
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dir_xt = (1.0 - a_prev - sigma_t**2).sqrt() * e_t
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noise = (
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sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
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)
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if noise_dropout > 0.0:
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noise = torch.nn.functional.dropout(noise, p=noise_dropout)
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x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
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return x_prev, pred_x0, None
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