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The DPMSolver order which can be 1 or 2 or 3. It is recommended to use solver_order=2 for guided |
sampling, and solver_order=3 for unconditional sampling. prediction_type (str, defaults to epsilon, optional) — |
Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process), |
sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen |
Video paper). thresholding (bool, defaults to False) — |
Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such |
as Stable Diffusion. dynamic_thresholding_ratio (float, defaults to 0.995) — |
The ratio for the dynamic thresholding method. Valid only when thresholding=True. sample_max_value (float, defaults to 1.0) — |
The threshold value for dynamic thresholding. Valid only when thresholding=True and |
algorithm_type="dpmsolver++". algorithm_type (str, defaults to dpmsolver++) — |
Algorithm type for the solver; can be dpmsolver, dpmsolver++, sde-dpmsolver or sde-dpmsolver++. The |
dpmsolver type implements the algorithms in the DPMSolver |
paper, and the dpmsolver++ type implements the algorithms in the |
DPMSolver++ paper. It is recommended to use dpmsolver++ or |
sde-dpmsolver++ with solver_order=2 for guided sampling like in Stable Diffusion. solver_type (str, defaults to midpoint) — |
Solver type for the second-order solver; can be midpoint or heun. The solver type slightly affects the |
sample quality, especially for a small number of steps. It is recommended to use midpoint solvers. lower_order_final (bool, defaults to True) — |
Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can |
stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10. euler_at_final (bool, defaults to False) — |
Whether to use Euler’s method in the final step. It is a trade-off between numerical stability and detail |
richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference |
steps, but sometimes may result in blurring. use_karras_sigmas (bool, optional, defaults to False) — |
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True, |
the sigmas are determined according to a sequence of noise levels {σi}. use_lu_lambdas (bool, optional, defaults to False) — |
Whether to use the uniform-logSNR for step sizes proposed by Lu’s DPM-Solver in the noise schedule during |
the sampling process. If True, the sigmas and time steps are determined according to a sequence of |
lambda(t). final_sigmas_type (str, 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. lambda_min_clipped (float, defaults to -inf) — |
Clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for the |
cosine (squaredcos_cap_v2) noise schedule. variance_type (str, optional) — |
Set to “learned” or “learned_range” for diffusion models that predict variance. If set, the model’s output |
contains the predicted Gaussian variance. timestep_spacing (str, defaults to "linspace") — |
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and |
Sample Steps are Flawed for more information. steps_offset (int, defaults to 0) — |
An offset added to the inference steps. You can use a combination of offset=1 and |
set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable |
Diffusion. DPMSolverMultistepScheduler is a fast dedicated high-order solver for diffusion ODEs. This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic |
methods the library implements for all schedulers such as loading and saving. convert_model_output < source > ( model_output: FloatTensor *args sample: FloatTensor = None **kwargs ) → torch.FloatTensor Parameters model_output (torch.FloatTensor) — |
The direct output from the learned diffusion model. sample (torch.FloatTensor) — |
A current instance of a sample created by the diffusion process. Returns |
torch.FloatTensor |
The converted model output. |
Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is |
designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an |
integral of the data prediction model. The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise |
prediction and data prediction models. dpm_solver_first_order_update < source > ( model_output: FloatTensor *args sample: FloatTensor = None noise: Optional = None **kwargs ) → torch.FloatTensor Parameters model_output (torch.FloatTensor) — |
The direct output from the learned diffusion model. sample (torch.FloatTensor) — |
A current instance of a sample created by the diffusion process. Returns |
torch.FloatTensor |
The sample tensor at the previous timestep. |
One step for the first-order DPMSolver (equivalent to DDIM). multistep_dpm_solver_second_order_update < source > ( model_output_list: List *args sample: FloatTensor = None noise: Optional = None **kwargs ) → torch.FloatTensor Parameters model_output_list (List[torch.FloatTensor]) — |
The direct outputs from learned diffusion model at current and latter timesteps. sample (torch.FloatTensor) — |
A current instance of a sample created by the diffusion process. Returns |
torch.FloatTensor |
The sample tensor at the previous timestep. |
One step for the second-order multistep DPMSolver. multistep_dpm_solver_third_order_update < source > ( model_output_list: List *args sample: FloatTensor = None **kwargs ) → torch.FloatTensor Parameters model_output_list (List[torch.FloatTensor]) — |
The direct outputs from learned diffusion model at current and latter timesteps. sample (torch.FloatTensor) — |
A current instance of a sample created by diffusion process. Returns |
torch.FloatTensor |
The sample tensor at the previous timestep. |
One step for the third-order multistep DPMSolver. scale_model_input < source > ( sample: FloatTensor *args **kwargs ) → torch.FloatTensor Parameters sample (torch.FloatTensor) — |
The input sample. Returns |
torch.FloatTensor |
A scaled input sample. |
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the |
current timestep. set_timesteps < source > ( num_inference_steps: int = None device: Union = None ) Parameters num_inference_steps (int) — |
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. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: FloatTensor timestep: int sample: FloatTensor generator = None return_dict: bool = True ) → SchedulerOu... |
The direct output from learned diffusion model. timestep (int) — |
The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) — |
A current instance of a sample created by the diffusion process. generator (torch.Generator, optional) — |
A random number generator. return_dict (bool) — |
Whether or not to return a SchedulerOutput or tuple. Returns |
SchedulerOutput or tuple |
If return_dict is True, SchedulerOutput is returned, otherwise a |
tuple is returned where the first element is the sample tensor. |
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with |
the multistep DPMSolver. SchedulerOutput class diffusers.schedulers.scheduling_utils.SchedulerOutput < source > ( prev_sample: FloatTensor ) Parameters prev_sample (torch.FloatTensor 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. Base class for the output of a scheduler’s step function. |
Safe Stable Diffusion |
Safe Stable Diffusion was proposed in Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models and mitigates the well known issue that models like Stable Diffusion that are trained on unfiltered, web-crawled datasets tend to suffer from inappropriate degeneration. For instance Stable Diffusion m... |
Safe Stable Diffusion is an extension to the Stable Diffusion that drastically reduces content like this. |
The abstract of the paper is the following: |
Text-conditioned image generation models have recently achieved astonishing results in image quality and text alignment and are consequently employed in a fast-growing number of applications. Since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer, as we... |
Overview: |
Pipeline |
Tasks |
Colab |
Demo |
pipeline_stable_diffusion_safe.py |
Text-to-Image Generation |
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