Buckets:
| # EDMEulerScheduler | |
| The Karras formulation of the Euler scheduler (Algorithm 2) from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original [k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51) implementation by [Katherine Crowson](https://github.com/crowsonkb/). | |
| ## EDMEulerScheduler[[diffusers.EDMEulerScheduler]] | |
| - **sigma_min** (`float`, *optional*, defaults to `0.002`) -- | |
| Minimum noise magnitude in the sigma schedule. This was set to 0.002 in the EDM paper [1]; a reasonable | |
| range is [0, 10]. | |
| - **sigma_max** (`float`, *optional*, defaults to `80.0`) -- | |
| Maximum noise magnitude in the sigma schedule. This was set to 80.0 in the EDM paper [1]; a reasonable | |
| range is [0.2, 80.0]. | |
| - **sigma_data** (`float`, *optional*, defaults to `0.5`) -- | |
| The standard deviation of the data distribution. This is set to 0.5 in the EDM paper [1]. | |
| - **sigma_schedule** (`Literal["karras", "exponential"]`, *optional*, defaults to `"karras"`) -- | |
| Sigma schedule to compute the `sigmas`. By default, we use the schedule introduced in the EDM paper | |
| (https://huggingface.co/papers/2206.00364). The `"exponential"` schedule was incorporated in this model: | |
| https://huggingface.co/stabilityai/cosxl. | |
| - **num_train_timesteps** (`int`, *optional*, defaults to `1000`) -- | |
| The number of diffusion steps to train the model. | |
| - **prediction_type** (`Literal["epsilon", "v_prediction"]`, *optional*, defaults to `"epsilon"`) -- | |
| Prediction type of the scheduler function. `"epsilon"` predicts the noise of the diffusion process, and | |
| `"v_prediction"` (see section 2.4 of [Imagen Video](https://huggingface.co/papers/2210.02303) paper). | |
| - **rho** (`float`, *optional*, defaults to `7.0`) -- | |
| The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1]. | |
| - **final_sigmas_type** (`Literal["zero", "sigma_min"]`, *optional*, defaults to `"zero"`) -- | |
| The final `sigma` value for the noise schedule during the sampling process. If `"sigma_min"`, the final | |
| sigma is the same as the last sigma in the training schedule. If `"zero"`, the final sigma is set to 0. | |
| Implements the Euler scheduler in EDM formulation as presented in Karras et al. 2022 [1]. | |
| [1] Karras, Tero, et al. "Elucidating the Design Space of Diffusion-Based Generative Models." | |
| https://huggingface.co/papers/2206.00364 | |
| This model inherits from [SchedulerMixin](/docs/diffusers/pr_13966/en/api/schedulers/overview#diffusers.SchedulerMixin) and [ConfigMixin](/docs/diffusers/pr_13966/en/api/configuration#diffusers.ConfigMixin). Check the superclass documentation for the generic | |
| methods the library implements for all schedulers such as loading and saving. | |
| - **original_samples** (`torch.Tensor`) -- | |
| The original samples to which noise will be added. | |
| - **noise** (`torch.Tensor`) -- | |
| The noise tensor to add to the original samples. | |
| - **timesteps** (`torch.Tensor`) -- | |
| The timesteps at which to add noise, determining the noise level from the schedule.`torch.Tensor`The noisy samples with added noise scaled according to the timestep schedule. | |
| Add noise to the original samples according to the noise schedule at the specified timesteps. | |
| - **timestep** (`float` or `torch.Tensor`) -- | |
| The timestep value to find in the schedule. | |
| - **schedule_timesteps** (`torch.Tensor`, *optional*) -- | |
| The timestep schedule to search in. If `None`, uses `self.timesteps`.`int`The index of the timestep in the schedule. For the very first step, returns the second index if | |
| multiple matches exist to avoid skipping a sigma when starting mid-schedule (e.g., for image-to-image). | |
| Find the index of a given timestep in the timestep schedule. | |
| - **sample** (`torch.Tensor`) -- | |
| The input sample tensor to precondition. | |
| - **sigma** (`float` or `torch.Tensor`) -- | |
| The current sigma (noise level) value.`torch.Tensor`The scaled input sample. | |
| Precondition the input sample by scaling it according to the EDM formulation. | |
| - **sigma** (`float` or `torch.Tensor`) -- | |
| The sigma (noise level) value to precondition.`torch.Tensor`The preconditioned noise value computed as `0.25 * log(sigma)`. | |
| Precondition the noise level by applying a logarithmic transformation. | |
| - **sample** (`torch.Tensor`) -- | |
| The input sample tensor. | |
| - **model_output** (`torch.Tensor`) -- | |
| The direct output from the learned diffusion model. | |
| - **sigma** (`float` or `torch.Tensor`) -- | |
| The current sigma (noise level) value.`torch.Tensor`The denoised sample computed by combining the skip connection and output scaling. | |
| Precondition the model outputs according to the EDM formulation. | |
| - **sample** (`torch.Tensor`) -- | |
| The input sample tensor. | |
| - **timestep** (`float` or `torch.Tensor`) -- | |
| The current timestep in the diffusion chain.`torch.Tensor`A scaled input sample. | |
| Scale the denoising model input to match the Euler algorithm. Ensures interchangeability with schedulers that | |
| need to scale the denoising model input depending on the current timestep. | |
| - **begin_index** (`int`, defaults to `0`) -- | |
| The begin index for the scheduler. | |
| Sets the begin index for the scheduler. This function should be run from pipeline before the inference. | |
| - **num_inference_steps** (`int`, *optional*) -- | |
| The number of diffusion steps used when generating samples with a pre-trained model. | |
| - **device** (`str` or `torch.device`, *optional*) -- | |
| The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. | |
| - **sigmas** (`torch.Tensor | list[float]`, *optional*) -- | |
| Custom sigmas to use for the denoising process. If not defined, the default behavior when | |
| `num_inference_steps` is passed will be used. | |
| Sets the discrete timesteps used for the diffusion chain (to be run before inference). | |
| - **model_output** (`torch.Tensor`) -- | |
| The direct output from the learned diffusion model. | |
| - **timestep** (`float` or `torch.Tensor`) -- | |
| The current discrete timestep in the diffusion chain. | |
| - **sample** (`torch.Tensor`) -- | |
| A current instance of a sample created by the diffusion process. | |
| - **s_churn** (`float`, *optional*, defaults to `0.0`) -- | |
| The amount of stochasticity to add at each step. Higher values add more noise. | |
| - **s_tmin** (`float`, *optional*, defaults to `0.0`) -- | |
| The minimum sigma threshold below which no noise is added. | |
| - **s_tmax** (`float`, *optional*, defaults to `float("inf")`) -- | |
| The maximum sigma threshold above which no noise is added. | |
| - **s_noise** (`float`, *optional*, defaults to `1.0`) -- | |
| Scaling factor for noise added to the sample. | |
| - **generator** (`torch.Generator`, *optional*) -- | |
| A random number generator for reproducibility. | |
| - **return_dict** (`bool`, *optional*, defaults to `True`) -- | |
| Whether or not to return an [EDMEulerSchedulerOutput](/docs/diffusers/pr_13966/en/api/schedulers/edm_euler#diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput) or tuple. | |
| - **pred_original_sample** (`torch.Tensor`, *optional*) -- | |
| The predicted denoised sample from a previous step. If provided, skips recomputation.[EDMEulerSchedulerOutput](/docs/diffusers/pr_13966/en/api/schedulers/edm_euler#diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput) or `tuple`If `return_dict` is `True`, an [EDMEulerSchedulerOutput](/docs/diffusers/pr_13966/en/api/schedulers/edm_euler#diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput) is | |
| returned, otherwise a tuple is returned where the first element is the previous sample tensor and the | |
| second element is the predicted original sample tensor. | |
| Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion | |
| process from the learned model outputs (most often the predicted noise). | |
| ## EDMEulerSchedulerOutput[[diffusers.schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput]] | |
| - **prev_sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images) -- | |
| Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the | |
| denoising loop. | |
| - **pred_original_sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images) -- | |
| The predicted denoised sample `(x_{0})` based on the model output from the current timestep. | |
| `pred_original_sample` can be used to preview progress or for guidance. | |
| Output class for the scheduler's `step` function output. | |
Xet Storage Details
- Size:
- 8.6 kB
- Xet hash:
- 70eaaaac66927c641959da8ef3df025da712342add285111e38226b267f3d2c3
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.