[Model] Add stable diffusion model based on fastdeploy (#297)

* 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
2025-10-12 20:11:20 +08:00 · 2022-11-10 14:59:07 +08:00
parent fa807340be
commit d4995e5468
13 changed files with 2301 additions and 0 deletions
--- a/examples/multimodal/stable_diffusion/export_model.py
+++ b/examples/multimodal/stable_diffusion/export_model.py
@@ -0,0 +1,100 @@
+# 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.")