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import warnings |
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from typing import Optional, Tuple, Union |
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import torch |
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from ...models import UNet2DModel |
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from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput |
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from ...schedulers import KarrasVeScheduler |
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class KarrasVePipeline(DiffusionPipeline): |
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r""" |
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Stochastic sampling from Karras et al. [1] tailored to the Variance-Expanding (VE) models [2]. Use Algorithm 2 and |
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the VE column of Table 1 from [1] for reference. |
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[1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." |
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https://arxiv.org/abs/2206.00364 [2] Song, Yang, et al. "Score-based generative modeling through stochastic |
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differential equations." https://arxiv.org/abs/2011.13456 |
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Parameters: |
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unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. |
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scheduler ([`KarrasVeScheduler`]): |
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Scheduler for the diffusion process to be used in combination with `unet` to denoise the encoded image. |
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""" |
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unet: UNet2DModel |
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scheduler: KarrasVeScheduler |
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def __init__(self, unet: UNet2DModel, scheduler: KarrasVeScheduler): |
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super().__init__() |
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scheduler = scheduler.set_format("pt") |
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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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batch_size: int = 1, |
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num_inference_steps: int = 50, |
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generator: Optional[torch.Generator] = None, |
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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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r""" |
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Args: |
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batch_size (`int`, *optional*, defaults to 1): |
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The number of images to generate. |
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generator (`torch.Generator`, *optional*): |
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A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation |
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deterministic. |
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num_inference_steps (`int`, *optional*, defaults to 50): |
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
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expense of slower inference. |
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output_type (`str`, *optional*, defaults to `"pil"`): |
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The output format of the generate image. Choose between |
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`. |
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return_dict (`bool`, *optional*, defaults to `True`): |
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Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple. |
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Returns: |
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[`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if |
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`return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the |
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generated images. |
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""" |
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if "torch_device" in kwargs: |
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device = kwargs.pop("torch_device") |
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warnings.warn( |
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"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0." |
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" Consider using `pipe.to(torch_device)` instead." |
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) |
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if device is None: |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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self.to(device) |
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img_size = self.unet.config.sample_size |
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shape = (batch_size, 3, img_size, img_size) |
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model = self.unet |
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sample = torch.randn(*shape) * self.scheduler.config.sigma_max |
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sample = sample.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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sigma = self.scheduler.schedule[t] |
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sigma_prev = self.scheduler.schedule[t - 1] if t > 0 else 0 |
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sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator) |
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model_output = (sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2).sample |
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step_output = self.scheduler.step(model_output, sigma_hat, sigma_prev, sample_hat) |
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if sigma_prev != 0: |
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model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample |
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step_output = self.scheduler.step_correct( |
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model_output, |
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sigma_hat, |
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sigma_prev, |
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sample_hat, |
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step_output.prev_sample, |
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step_output["derivative"], |
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) |
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sample = step_output.prev_sample |
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sample = (sample / 2 + 0.5).clamp(0, 1) |
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image = sample.cpu().permute(0, 2, 3, 1).numpy() |
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if output_type == "pil": |
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image = self.numpy_to_pil(sample) |
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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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