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https://github.com/PaddlePaddle/FastDeploy.git
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d74e1209ae
* Update Inpaint pipeline * Update concat * Add GaussianRandomKernel * Update GaussianRandom * Add vae endoder * Add unet infer * Add vae decoder predict * add PrepareMaskAndMaskedImage * Add imwrite * Add time counter * Fix pipeline * use FDTensor move * Fix scaled_linear dpm solver * Add RGB2BGR
88 lines
3.5 KiB
C++
88 lines
3.5 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. 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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#pragma once
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#include "./scheduler.h"
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#include "fastdeploy/core/fd_tensor.h"
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namespace fastdeploy {
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class DPMSolverMultistepScheduler : public Scheduler {
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public:
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DPMSolverMultistepScheduler(int num_train_timesteps = 1000,
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float beta_start = 0.0001, float beta_end = 0.02,
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const std::string& beta_schedule = "linear",
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const std::vector<float>& trained_betas = {},
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int solver_order = 2, bool predict_epsilon = true,
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bool thresholding = false,
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float dynamic_thresholding_ratio = 0.995,
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float sample_max_value = 1.0,
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const std::string& algorithm_type = "dpmsolver++",
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const std::string& solver_type = "midpoint",
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bool lower_order_final = true);
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void BetaForAlphaBar(FDTensor* out, int num_diffusion_timesteps,
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float max_beta = 0.999);
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void ConvertModelOutput(const FDTensor& model_output, int timestep,
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const FDTensor& sample, FDTensor* out);
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void DPMSolverFirstOrderUpdate(const FDTensor& model_output, int timestep,
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int prev_timestep, const FDTensor& sample,
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FDTensor* out);
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void MultiStepDPMSolverSecondOrderUpdate(
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const std::vector<FDTensor>& model_output_list,
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const std::vector<int>& timestep_list, int prev_timestep,
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const FDTensor& sample, FDTensor* out);
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void MultiStepDPMSolverThirdOrderUpdate(
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const std::vector<FDTensor>& model_output_list,
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const std::vector<int>& timestep_list, int prev_timestep,
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const FDTensor& sample, FDTensor* out);
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void SetTimesteps(int num_inference_steps) override;
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void Step(const FDTensor& model_output, int timestep, const FDTensor& sample,
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FDTensor* prev_sample) override;
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void ScaleModelInput(const FDTensor& sample, FDTensor* out,
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const std::vector<FDTensor>& timesteps = {}) override;
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void AddNoise(const FDTensor& original_samples, const FDTensor& noise,
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const FDTensor& timesteps, FDTensor* out) override;
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float InitNoiseSigma() override;
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FDTensor GetTimesteps() override;
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struct Config {
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int num_train_timesteps_;
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float beta_start_;
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float beta_end_;
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std::string beta_schedule_;
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int solver_order_;
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bool predict_epsilon_;
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bool thresholding_;
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float dynamic_thresholding_ratio_;
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float sample_max_value_;
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std::string algorithm_type_;
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std::string solver_type_;
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bool lower_order_final_;
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} config;
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private:
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FDTensor betas_;
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FDTensor alphas_;
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FDTensor alphas_cumprod_;
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FDTensor alpha_t_;
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FDTensor sigma_t_;
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FDTensor lambda_t_;
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int num_inference_steps_;
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FDTensor timesteps_;
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int lower_order_nums_;
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std::vector<FDTensor> model_outputs_;
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};
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} // namespace fastdeploy
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