Delete bit_diffusion.py
Browse files- bit_diffusion.py +0 -264
bit_diffusion.py
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from typing import Optional, Tuple, Union
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import torch
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from einops import rearrange, reduce
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from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNet2DConditionModel
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from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
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from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput
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BITS = 8
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# convert to bit representations and back taken from https://github.com/lucidrains/bit-diffusion/blob/main/bit_diffusion/bit_diffusion.py
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def decimal_to_bits(x, bits=BITS):
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"""expects image tensor ranging from 0 to 1, outputs bit tensor ranging from -1 to 1"""
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device = x.device
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x = (x * 255).int().clamp(0, 255)
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mask = 2 ** torch.arange(bits - 1, -1, -1, device=device)
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mask = rearrange(mask, "d -> d 1 1")
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x = rearrange(x, "b c h w -> b c 1 h w")
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bits = ((x & mask) != 0).float()
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bits = rearrange(bits, "b c d h w -> b (c d) h w")
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bits = bits * 2 - 1
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return bits
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def bits_to_decimal(x, bits=BITS):
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"""expects bits from -1 to 1, outputs image tensor from 0 to 1"""
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device = x.device
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x = (x > 0).int()
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mask = 2 ** torch.arange(bits - 1, -1, -1, device=device, dtype=torch.int32)
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mask = rearrange(mask, "d -> d 1 1")
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x = rearrange(x, "b (c d) h w -> b c d h w", d=8)
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dec = reduce(x * mask, "b c d h w -> b c h w", "sum")
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return (dec / 255).clamp(0.0, 1.0)
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# modified scheduler step functions for clamping the predicted x_0 between -bit_scale and +bit_scale
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def ddim_bit_scheduler_step(
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self,
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model_output: torch.Tensor,
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timestep: int,
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sample: torch.Tensor,
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eta: float = 0.0,
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use_clipped_model_output: bool = True,
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generator=None,
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return_dict: bool = True,
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) -> Union[DDIMSchedulerOutput, Tuple]:
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"""
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Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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model_output (`torch.Tensor`): direct output from learned diffusion model.
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timestep (`int`): current discrete timestep in the diffusion chain.
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sample (`torch.Tensor`):
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current instance of sample being created by diffusion process.
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eta (`float`): weight of noise for added noise in diffusion step.
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use_clipped_model_output (`bool`): TODO
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generator: random number generator.
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return_dict (`bool`): option for returning tuple rather than DDIMSchedulerOutput class
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Returns:
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[`~schedulers.scheduling_utils.DDIMSchedulerOutput`] or `tuple`:
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[`~schedulers.scheduling_utils.DDIMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
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returning a tuple, the first element is the sample tensor.
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"""
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if self.num_inference_steps is None:
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raise ValueError(
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"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
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)
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# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
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# Ideally, read DDIM paper in-detail understanding
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# Notation (<variable name> -> <name in paper>
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# - pred_noise_t -> e_theta(x_t, t)
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# - pred_original_sample -> f_theta(x_t, t) or x_0
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# - std_dev_t -> sigma_t
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# - eta -> η
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# - pred_sample_direction -> "direction pointing to x_t"
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# - pred_prev_sample -> "x_t-1"
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# 1. get previous step value (=t-1)
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prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
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# 2. compute alphas, betas
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alpha_prod_t = self.alphas_cumprod[timestep]
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alpha_prod_t_prev = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
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beta_prod_t = 1 - alpha_prod_t
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# 3. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
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# 4. Clip "predicted x_0"
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scale = self.bit_scale
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if self.config.clip_sample:
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pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
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# 5. compute variance: "sigma_t(η)" -> see formula (16)
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# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
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variance = self._get_variance(timestep, prev_timestep)
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std_dev_t = eta * variance ** (0.5)
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if use_clipped_model_output:
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# the model_output is always re-derived from the clipped x_0 in Glide
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model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
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# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
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# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
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prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
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if eta > 0:
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# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
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device = model_output.device if torch.is_tensor(model_output) else "cpu"
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noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator).to(device)
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variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * noise
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prev_sample = prev_sample + variance
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if not return_dict:
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return (prev_sample,)
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return DDIMSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample)
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def ddpm_bit_scheduler_step(
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self,
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model_output: torch.Tensor,
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timestep: int,
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sample: torch.Tensor,
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prediction_type="epsilon",
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generator=None,
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return_dict: bool = True,
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) -> Union[DDPMSchedulerOutput, Tuple]:
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"""
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Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
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process from the learned model outputs (most often the predicted noise).
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Args:
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model_output (`torch.Tensor`): direct output from learned diffusion model.
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timestep (`int`): current discrete timestep in the diffusion chain.
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sample (`torch.Tensor`):
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current instance of sample being created by diffusion process.
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prediction_type (`str`, default `epsilon`):
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indicates whether the model predicts the noise (epsilon), or the samples (`sample`).
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generator: random number generator.
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return_dict (`bool`): option for returning tuple rather than DDPMSchedulerOutput class
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Returns:
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[`~schedulers.scheduling_utils.DDPMSchedulerOutput`] or `tuple`:
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[`~schedulers.scheduling_utils.DDPMSchedulerOutput`] if `return_dict` is True, otherwise a `tuple`. When
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returning a tuple, the first element is the sample tensor.
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"""
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t = timestep
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if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
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model_output, predicted_variance = torch.split(model_output, sample.shape[1], dim=1)
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else:
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predicted_variance = None
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# 1. compute alphas, betas
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alpha_prod_t = self.alphas_cumprod[t]
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alpha_prod_t_prev = self.alphas_cumprod[t - 1] if t > 0 else self.one
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beta_prod_t = 1 - alpha_prod_t
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beta_prod_t_prev = 1 - alpha_prod_t_prev
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# 2. compute predicted original sample from predicted noise also called
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# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
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if prediction_type == "epsilon":
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pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
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elif prediction_type == "sample":
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pred_original_sample = model_output
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else:
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raise ValueError(f"Unsupported prediction_type {prediction_type}.")
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# 3. Clip "predicted x_0"
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scale = self.bit_scale
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if self.config.clip_sample:
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pred_original_sample = torch.clamp(pred_original_sample, -scale, scale)
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# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
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# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
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pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * self.betas[t]) / beta_prod_t
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current_sample_coeff = self.alphas[t] ** (0.5) * beta_prod_t_prev / beta_prod_t
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# 5. Compute predicted previous sample µ_t
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# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
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pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
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# 6. Add noise
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variance = 0
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if t > 0:
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noise = torch.randn(
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model_output.size(), dtype=model_output.dtype, layout=model_output.layout, generator=generator
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).to(model_output.device)
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variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * noise
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pred_prev_sample = pred_prev_sample + variance
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if not return_dict:
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return (pred_prev_sample,)
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return DDPMSchedulerOutput(prev_sample=pred_prev_sample, pred_original_sample=pred_original_sample)
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class BitDiffusion(DiffusionPipeline):
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def __init__(
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self,
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unet: UNet2DConditionModel,
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scheduler: Union[DDIMScheduler, DDPMScheduler],
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bit_scale: Optional[float] = 1.0,
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):
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super().__init__()
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self.bit_scale = bit_scale
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self.scheduler.step = (
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ddim_bit_scheduler_step if isinstance(scheduler, DDIMScheduler) else ddpm_bit_scheduler_step
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)
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self.register_modules(unet=unet, scheduler=scheduler)
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@torch.no_grad()
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def __call__(
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self,
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height: Optional[int] = 256,
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width: Optional[int] = 256,
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num_inference_steps: Optional[int] = 50,
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generator: Optional[torch.Generator] = None,
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batch_size: Optional[int] = 1,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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**kwargs,
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) -> Union[Tuple, ImagePipelineOutput]:
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latents = torch.randn(
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(batch_size, self.unet.config.in_channels, height, width),
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generator=generator,
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)
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latents = decimal_to_bits(latents) * self.bit_scale
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latents = latents.to(self.device)
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self.scheduler.set_timesteps(num_inference_steps)
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for t in self.progress_bar(self.scheduler.timesteps):
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# predict the noise residual
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noise_pred = self.unet(latents, t).sample
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# compute the previous noisy sample x_t -> x_t-1
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latents = self.scheduler.step(noise_pred, t, latents).prev_sample
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image = bits_to_decimal(latents)
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if output_type == "pil":
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image = self.numpy_to_pil(image)
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if not return_dict:
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return (image,)
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return ImagePipelineOutput(images=image)
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