mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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d4995e5468
* Add stable diffusion model base on fastdeploy * Add sd infer * pipelines->multimodal * add create_ort_runtime * use fp16 input * fix pil * Add optimize unet model * add hf license * Add workspace args * Add profile func * Add schedulers * usrelace torch.Tenosr byp.ndarray * Add readme * Add trt shape setting * add dynamic shape * Add dynamic shape for stable diffusion * fix max shape setting * rename tensorrt file suffix * update dynamic shape setting * Add scheduler output * Add inference_steps and benchmark steps * add diffuser benchmark * Add paddle infer script * Rename 1 * Rename infer.py to torch_onnx_infer.py * Add export torch to onnx model * renmove export model * Add paddle export model for diffusion * Fix export model * mv torch onnx infer to infer * Fix export model * Fix infer * modif create_trt_runtime create_ort_runtime * update export torch * update requirements * add paddle inference backend * Fix unet pp run * remove print * Add paddle model export and infer * Add device id * remove profile to utils * Add -1 device id * Add safety checker args * remove safety checker temporarily * Add export model description * Add predict description * Fix readme * Fix device_id description * add timestep shape * add use fp16 precision * move use gpu * Add EulerAncestralDiscreteScheduler * Use EulerAncestralDiscreteScheduler with v1-5 model * Add export model readme * Add link of exported model * Update scheduler on README * Addd stable-diffusion-v1-5
101 lines
3.6 KiB
Python
101 lines
3.6 KiB
Python
# 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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import os
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import paddle
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import paddlenlp
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from ppdiffusers import UNet2DConditionModel, AutoencoderKL
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from ppdiffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
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from paddlenlp.transformers import CLIPTextModel
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--pretrained_model_name_or_path",
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default='CompVis/stable-diffusion-v1-4',
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help="The pretrained diffusion model.")
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parser.add_argument(
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"--output_path",
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type=str,
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required=True,
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help="The pretrained diffusion model.")
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return parser.parse_args()
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class VAEDecoder(AutoencoderKL):
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def forward(self, z):
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return self.decode(z, True).sample
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if __name__ == "__main__":
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paddle.set_device('cpu')
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args = parse_arguments()
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# Load models and create wrapper for stable diffusion
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text_encoder = CLIPTextModel.from_pretrained(
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os.path.join(args.pretrained_model_name_or_path, "text_encoder"))
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vae_decoder = VAEDecoder.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="vae")
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unet = UNet2DConditionModel.from_pretrained(
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args.pretrained_model_name_or_path, subfolder="unet")
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# Convert to static graph with specific input description
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text_encoder = paddle.jit.to_static(
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text_encoder,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, None], dtype="int64",
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name="input_ids") # input_ids
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])
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# Save text_encoder in static graph model.
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save_path = os.path.join(args.output_path, "text_encoder", "inference")
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paddle.jit.save(text_encoder, save_path)
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print(f"Save text_encoder model in {save_path} successfully.")
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# Convert to static graph with specific input description
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vae_decoder = paddle.jit.to_static(
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vae_decoder,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, 4, 64, 64], dtype="float32",
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name="latent"), # latent
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])
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# Save vae_decoder in static graph model.
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save_path = os.path.join(args.output_path, "vae_decoder", "inference")
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paddle.jit.save(vae_decoder, save_path)
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print(f"Save vae_decoder model in {save_path} successfully.")
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# Convert to static graph with specific input description
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unet = paddle.jit.to_static(
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unet,
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input_spec=[
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paddle.static.InputSpec(
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shape=[None, 4, None, None],
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dtype="float32",
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name="latent_input"), # latent
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paddle.static.InputSpec(
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shape=[1], dtype="int64", name="timestep"), # timesteps
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paddle.static.InputSpec(
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shape=[None, None, 768],
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dtype="float32",
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name="encoder_embedding") # encoder_embedding
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])
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save_path = os.path.join(args.output_path, "unet", "inference")
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paddle.jit.save(unet, save_path)
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print(f"Save unet model in {save_path} successfully.")
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