mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 08:37:06 +08:00
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.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
import paddle
|
|
import paddlenlp
|
|
|
|
from ppdiffusers import UNet2DConditionModel, AutoencoderKL
|
|
from ppdiffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
|
|
from paddlenlp.transformers import CLIPTextModel
|
|
|
|
|
|
def parse_arguments():
|
|
import argparse
|
|
import ast
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
"--pretrained_model_name_or_path",
|
|
default='CompVis/stable-diffusion-v1-4',
|
|
help="The pretrained diffusion model.")
|
|
parser.add_argument(
|
|
"--output_path",
|
|
type=str,
|
|
required=True,
|
|
help="The pretrained diffusion model.")
|
|
return parser.parse_args()
|
|
|
|
|
|
class VAEDecoder(AutoencoderKL):
|
|
def forward(self, z):
|
|
return self.decode(z, True).sample
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.set_device('cpu')
|
|
args = parse_arguments()
|
|
# Load models and create wrapper for stable diffusion
|
|
text_encoder = CLIPTextModel.from_pretrained(
|
|
os.path.join(args.pretrained_model_name_or_path, "text_encoder"))
|
|
vae_decoder = VAEDecoder.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="vae")
|
|
unet = UNet2DConditionModel.from_pretrained(
|
|
args.pretrained_model_name_or_path, subfolder="unet")
|
|
|
|
# Convert to static graph with specific input description
|
|
text_encoder = paddle.jit.to_static(
|
|
text_encoder,
|
|
input_spec=[
|
|
paddle.static.InputSpec(
|
|
shape=[None, None], dtype="int64",
|
|
name="input_ids") # input_ids
|
|
])
|
|
|
|
# Save text_encoder in static graph model.
|
|
save_path = os.path.join(args.output_path, "text_encoder", "inference")
|
|
paddle.jit.save(text_encoder, save_path)
|
|
print(f"Save text_encoder model in {save_path} successfully.")
|
|
|
|
# Convert to static graph with specific input description
|
|
vae_decoder = paddle.jit.to_static(
|
|
vae_decoder,
|
|
input_spec=[
|
|
paddle.static.InputSpec(
|
|
shape=[None, 4, 64, 64], dtype="float32",
|
|
name="latent"), # latent
|
|
])
|
|
# Save vae_decoder in static graph model.
|
|
save_path = os.path.join(args.output_path, "vae_decoder", "inference")
|
|
paddle.jit.save(vae_decoder, save_path)
|
|
print(f"Save vae_decoder model in {save_path} successfully.")
|
|
|
|
# Convert to static graph with specific input description
|
|
unet = paddle.jit.to_static(
|
|
unet,
|
|
input_spec=[
|
|
paddle.static.InputSpec(
|
|
shape=[None, 4, None, None],
|
|
dtype="float32",
|
|
name="latent_input"), # latent
|
|
paddle.static.InputSpec(
|
|
shape=[1], dtype="int64", name="timestep"), # timesteps
|
|
paddle.static.InputSpec(
|
|
shape=[None, None, 768],
|
|
dtype="float32",
|
|
name="encoder_embedding") # encoder_embedding
|
|
])
|
|
save_path = os.path.join(args.output_path, "unet", "inference")
|
|
paddle.jit.save(unet, save_path)
|
|
print(f"Save unet model in {save_path} successfully.")
|