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https://github.com/huggingface/diffusers.git
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be4afa0bb4
* find & replace all FloatTensors to Tensor * apply formatting * Update torch.FloatTensor to torch.Tensor in the remaining files * formatting * Fix the rest of the places where FloatTensor is used as well as in documentation * formatting * Update new file from FloatTensor to Tensor
191 lines
8.2 KiB
Python
191 lines
8.2 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import List, Optional, Tuple, Union
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import PIL.Image
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import torch
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from torchvision import transforms
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from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
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from diffusers.schedulers import DDIMScheduler
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from diffusers.utils.torch_utils import randn_tensor
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trans = transforms.Compose(
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[
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transforms.Resize((256, 256)),
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transforms.ToTensor(),
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transforms.Normalize([0.5], [0.5]),
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]
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)
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def preprocess(image):
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if isinstance(image, torch.Tensor):
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return image
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elif isinstance(image, PIL.Image.Image):
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image = [image]
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image = [trans(img.convert("RGB")) for img in image]
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image = torch.stack(image)
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return image
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class DDIMNoiseComparativeAnalysisPipeline(DiffusionPipeline):
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r"""
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
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library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
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Parameters:
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unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image. Can be one of
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[`DDPMScheduler`], or [`DDIMScheduler`].
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"""
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def __init__(self, unet, scheduler):
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super().__init__()
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# make sure scheduler can always be converted to DDIM
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scheduler = DDIMScheduler.from_config(scheduler.config)
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self.register_modules(unet=unet, scheduler=scheduler)
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def check_inputs(self, strength):
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if strength < 0 or strength > 1:
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raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
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def get_timesteps(self, num_inference_steps, strength, device):
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# get the original timestep using init_timestep
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init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
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t_start = max(num_inference_steps - init_timestep, 0)
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timesteps = self.scheduler.timesteps[t_start:]
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return timesteps, num_inference_steps - t_start
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def prepare_latents(self, image, timestep, batch_size, dtype, device, generator=None):
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if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
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raise ValueError(
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f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
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)
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init_latents = image.to(device=device, dtype=dtype)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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shape = init_latents.shape
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noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
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# get latents
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print("add noise to latents at timestep", timestep)
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init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
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latents = init_latents
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return latents
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@torch.no_grad()
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def __call__(
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self,
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image: Union[torch.Tensor, PIL.Image.Image] = None,
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strength: float = 0.8,
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batch_size: int = 1,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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eta: float = 0.0,
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num_inference_steps: int = 50,
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use_clipped_model_output: Optional[bool] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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) -> Union[ImagePipelineOutput, Tuple]:
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r"""
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Args:
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image (`torch.Tensor` or `PIL.Image.Image`):
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`Image`, or tensor representing an image batch, that will be used as the starting point for the
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process.
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strength (`float`, *optional*, defaults to 0.8):
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Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
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will be used as a starting point, adding more noise to it the larger the `strength`. The number of
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denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
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be maximum and the denoising process will run for the full number of iterations specified in
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`num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
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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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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make generation deterministic.
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eta (`float`, *optional*, defaults to 0.0):
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The eta parameter which controls the scale of the variance (0 is DDIM and 1 is one type of DDPM).
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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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use_clipped_model_output (`bool`, *optional*, defaults to `None`):
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if `True` or `False`, see documentation for `DDIMScheduler.step`. If `None`, nothing is passed
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downstream to the scheduler. So use `None` for schedulers which don't support this argument.
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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 `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
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Returns:
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[`~pipelines.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if `return_dict` is
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True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
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"""
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(strength)
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# 2. Preprocess image
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image = preprocess(image)
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# 3. set timesteps
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self.scheduler.set_timesteps(num_inference_steps, device=self.device)
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timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, self.device)
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latent_timestep = timesteps[:1].repeat(batch_size)
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# 4. Prepare latent variables
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latents = self.prepare_latents(image, latent_timestep, batch_size, self.unet.dtype, self.device, generator)
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image = latents
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# 5. Denoising loop
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for t in self.progress_bar(timesteps):
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# 1. predict noise model_output
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model_output = self.unet(image, t).sample
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# 2. predict previous mean of image x_t-1 and add variance depending on eta
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# eta corresponds to η in paper and should be between [0, 1]
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# do x_t -> x_t-1
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image = self.scheduler.step(
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model_output,
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t,
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image,
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eta=eta,
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use_clipped_model_output=use_clipped_model_output,
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generator=generator,
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).prev_sample
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image = (image / 2 + 0.5).clamp(0, 1)
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image = image.cpu().permute(0, 2, 3, 1).numpy()
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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, latent_timestep.item())
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return ImagePipelineOutput(images=image)
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