90 lines
2.9 KiB
Python
90 lines
2.9 KiB
Python
"""SAMPLING ONLY."""
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import torch
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from .uni_pc import NoiseScheduleVP, model_wrapper, UniPC
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class UniPCSampler(object):
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def __init__(self, model, **kwargs):
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super().__init__()
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self.model = model
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to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
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self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
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def register_buffer(self, name, attr):
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if type(attr) == torch.Tensor:
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if attr.device != torch.device("cuda"):
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attr = attr.to(torch.device("cuda"))
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setattr(self, name, attr)
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def unipc_encode(self, latent, device, strength, steps, noise=None):
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ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
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uni_pc = UniPC(None, ns, predict_x0=True, thresholding=False, variant='bh1')
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t_0 = 1. / ns.total_N
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timesteps = uni_pc.get_time_steps("time_uniform", strength, t_0, steps, device)
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timesteps = timesteps[0].expand((latent.shape[0]))
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noisy_latent = uni_pc.unipc_encode(latent, timesteps, noise=noise)
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return noisy_latent
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@torch.no_grad()
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def sample(self,
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S,
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batch_size,
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shape,
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conditioning=None,
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callback=None,
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normals_sequence=None,
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img_callback=None,
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quantize_x0=False,
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strength=None,
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eta=0.,
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mask=None,
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x0=None,
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temperature=1.,
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score_corrector=None,
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corrector_kwargs=None,
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verbose=True,
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x_T=None,
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log_every_t=100,
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unconditional_guidance_scale=1.,
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unconditional_conditioning=None,
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# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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**kwargs
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):
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# sampling
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B, C, F, H, W = shape
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size = (B, C, F, H, W)
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if x_T is None:
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img = torch.randn(size, device=self.model.device)
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else:
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img = x_T
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ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
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model_fn = model_wrapper(
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lambda x, t, c: self.model(x, t, c),
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ns,
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model_type="noise",
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guidance_type="classifier-free",
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condition=conditioning,
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unconditional_condition=unconditional_conditioning,
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guidance_scale=unconditional_guidance_scale,
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)
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uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant='bh1')
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x = uni_pc.sample(
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img,
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steps=S,
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t_start=strength,
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skip_type="time_uniform",
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method="multistep",
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order=3,
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lower_order_final=True,
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initial_corrector=True,
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callback=callback
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)
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return x.to(self.model.device) |