text stringlengths 0 5.54k |
|---|
pipeline = AutoPipelineForText2Image.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", |
torch_dtype=torch.float16, |
variant="fp16", |
use_safetensors=True |
).to("cuda") |
image = pipe( |
prompt = "A croissant shaped like a cute bear." |
negative_prompt = "Deformed, ugly, bad anatomy" |
callback_on_step_end=decode_tensors, |
callback_on_step_end_tensor_inputs=["latents"], |
).images[0] step 0 step 19 step 29 step 39 step 49 |
DPMSolverMultistepScheduler DPMSolverMultistep is a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and J... |
samples, and it can generate quite good samples even in 10 steps. Tips It is recommended to set solver_order to 2 for guide sampling, and solver_order=3 for unconditional sampling. Dynamic thresholding from Imagen is supported, and for pixel-space |
diffusion models, you can set both algorithm_type="dpmsolver++" and thresholding=True to use the dynamic |
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as |
Stable Diffusion. The SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order sde-dpmsolver++. DPMSolverMultistepScheduler class diffusers.DPMSolverMultistepSchedule... |
The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) — |
The starting beta value of inference. beta_end (float, defaults to 0.02) — |
The final beta value. beta_schedule (str, defaults to "linear") — |
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from |
linear, scaled_linear, or squaredcos_cap_v2. trained_betas (np.ndarray, optional) — |
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. solver_order (int, defaults to 2) — |
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. rescale_betas_zero_snr (bool, defaults to False) — |
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and |
dark samples instead of limiting it to samples with medium brightness. Loosely related to |
--offset_noise. 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_begin_index < source > ( begin_index: int = 0 ) Parameters begin_index (int) — |
The begin index for the scheduler. Sets the begin index for the scheduler. This function should be run from pipeline before the inference. set_timesteps < source > ( num_inference_steps: int = None device: Union = None ) Parameters num_inference_steps (int) — |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.