87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
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"""SAMPLING ONLY."""
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import torch
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from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
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MODEL_TYPES = {
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"eps": "noise",
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"v": "v"
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}
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class DPMSolverSampler(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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@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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eta=0.,
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mask=None,
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x0=None,
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temperature=1.,
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noise_dropout=0.,
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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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if conditioning is not None:
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if isinstance(conditioning, dict):
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cbs = conditioning[list(conditioning.keys())[0]].shape[0]
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if cbs != batch_size:
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print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
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else:
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if conditioning.shape[0] != batch_size:
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print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
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# sampling
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C, H, W = shape
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size = (batch_size, C, H, W)
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print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
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device = self.model.betas.device
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if x_T is None:
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img = torch.randn(size, device=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.apply_model(x, t, c),
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ns,
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model_type=MODEL_TYPES[self.model.parameterization],
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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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dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
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x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
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return x.to(device), None
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