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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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**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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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 |
