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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
62 lines
2.4 KiB
C++
62 lines
2.4 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 "fast_tokenizer/tokenizers/clip_fast_tokenizer.h"
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#include "fastdeploy/core/fd_tensor.h"
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#include "fastdeploy/runtime.h"
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#include "opencv2/core/core.hpp"
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#include <memory>
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#include <string>
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#include <vector>
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namespace fastdeploy {
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class StableDiffusionInpaintPipeline {
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public:
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typedef void (*callback_ptr)(int, int, FDTensor*);
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StableDiffusionInpaintPipeline(
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std::unique_ptr<Runtime> vae_encoder,
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std::unique_ptr<Runtime> vae_decoder,
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std::unique_ptr<Runtime> text_encoder, std::unique_ptr<Runtime> unet,
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std::unique_ptr<Scheduler> scheduler,
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const paddlenlp::fast_tokenizer::tokenizers_impl::ClipFastTokenizer&
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tokenizer);
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void Predict(const std::vector<std::string>& prompts, const cv::Mat& image,
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const cv::Mat& mask_image, std::vector<FDTensor>* output_images,
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int height = 512, int width = 512, int num_inference_steps = 50,
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float guidance_scale = 7.5,
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const std::vector<std::string>& negative_prompt = {},
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int num_images_per_prompt = 1, float eta = 0.0,
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uint32_t max_length = 77, const FDTensor* latents = nullptr,
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bool output_cv_mat = true, callback_ptr callback = nullptr,
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int callback_steps = 1);
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private:
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void PrepareMaskAndMaskedImage(const cv::Mat& image, const cv::Mat& mask_mat,
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const std::vector<int64_t>& shape,
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FDTensor* mask, FDTensor* mask_image);
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std::unique_ptr<Runtime> vae_encoder_;
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std::unique_ptr<Runtime> vae_decoder_;
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std::unique_ptr<Runtime> text_encoder_;
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std::unique_ptr<Runtime> unet_;
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std::unique_ptr<Scheduler> scheduler_;
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paddlenlp::fast_tokenizer::tokenizers_impl::ClipFastTokenizer tokenizer_;
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};
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} // namespace fastdeploy
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