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Add trt cache file

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This commit is contained in:
zhoushunjie
2022-12-05 09:19:11 +00:00
parent a88c6a6905
commit 50dd3c3e85
2 changed files with 33 additions and 8 deletions
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30
examples/multimodal/stable_diffusion/cpp/main.cc
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@@ -12,12 +12,12 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "dpm_solver_multistep_scheduler.h"
#include "./dpm_solver_multistep_scheduler.h"
#include "./pipeline_stable_diffusion_inpaint.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/common/processors/mat.h"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "pipeline_stable_diffusion_inpaint.h"
#include <iostream>
#include <memory>
#include <sstream>
@@ -68,7 +68,7 @@ std::unique_ptr<fastdeploy::Runtime> CreateRuntime(
runtime_option.SetTrtInputShape(it->first, min_shape, opt_shape,
max_shape);
}
runtime_option.SetTrtCacheFile("");
runtime_option.SetTrtCacheFile("paddle.trt");
runtime_option.EnablePaddleTrtCollectShape();
runtime_option.DisablePaddleTrtOPs(disable_paddle_trt_ops);
if (use_fp16) {
@@ -183,9 +183,12 @@ int main() {
paddlenlp::fast_tokenizer::tokenizers_impl::ClipFastTokenizer tokenizer(
"clip/vocab.json", "clip/merges.txt", /* max_length = */ max_length);
fastdeploy::StableDiffusionInpaintPipeline pipe(
std::move(vae_encoder_runtime), std::move(vae_decoder_runtime),
std::move(text_encoder_runtime), std::move(unet_runtime),
/* scheduler = */ std::move(dpm), tokenizer);
/* vae_encoder = */ std::move(vae_encoder_runtime),
/* vae_decoder = */ std::move(vae_decoder_runtime),
/* text_encoder = */ std::move(text_encoder_runtime),
/* unet = */ std::move(unet_runtime),
/* scheduler = */ std::move(dpm),
/* tokenizer = */ tokenizer);
// 7. Read images
auto image = cv::imread("overture-creations.png");
@@ -197,8 +200,19 @@ int main() {
std::vector<fastdeploy::FDTensor> outputs;
fastdeploy::TimeCounter tc;
tc.Start();
pipe.Predict(prompts, image, mask_image, &outputs, /* height = */ 512,
/* width = */ 512, /* num_inference_steps = */ 50);
pipe.Predict(prompts, image, mask_image, &outputs,
/* height = */ 512,
/* width = */ 512,
/* num_inference_steps = */ 50,
/* guidance_scale = */ 7.5,
/* negative_prompt = */ {},
/* num_images_per_prompt = */ 1,
/* eta = */ 1.0,
/* max_length = */ max_length,
/* latents = */ nullptr,
/* output_cv_mat = */ true,
/* callback = */ nullptr,
/* callback_steps = */ 1);
tc.End();
tc.PrintInfo();
fastdeploy::vision::FDMat mat = fastdeploy::vision::FDMat::Create(outputs[0]);