Stable Diffusion 1.5 and Stable Diffusion XL Tutorials

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 # Pretrained Models
-*.onnx
+**/*.onnx
+**/onnx/*.opt
+**/*.bin
+**/*.plan
+**/pytorch_model
 
 # Python Stuff
-__pycache__
+**/__pycache__
 
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+**/downloads

Popular_Models_Guide/StableDiffusion/README.md

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+
+# Deploying Stable Diffusion Models with Triton and TensorRT
+
+This example demonstrates how to deploy Stable Diffusion models in
+Triton by leveraging the [TensorRT demo](https://github.com/NVIDIA/TensorRT/tree/release/9.2/demo/Diffusion)
+pipeline and utilities.
+
+Using the TensorRT demo as a base this example contains a reusable
+[python based backend](https://github.com/triton-inference-server/backend/blob/main/docs/python_based_backends.md), [`/backend/diffusion/model.py`](backend/diffusion/model.py),
+suitable for deploying multiple versions and configurations of
+Diffusion models.
+
+For more information on Stable Diffusion please visit
+[stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5),
+[stable-diffusion-xl](https://huggingface.co/docs/diffusers/en/using-diffusers/sdxl). For
+more information on the TensorRT implementation please see the [TensorRT demo](https://github.com/NVIDIA/TensorRT/tree/release/9.2/demo/Diffusion).
+
+> [!Note]
+> This example is given as sample code and should be reviewed before use in production settings.
+
+| [Requirements](#requirements) | [Building Server Image](#building-the-triton-inference-server-image) | [Stable Diffusion v1.5](#building-and-running-stable-diffusion-v-15) | [Stable Diffusion XL](#building-and-running-stable-diffusion-xl) | [Sending an Inference Request](#sending-an-inference-request) | [Model Configuration](docs/model_configuration.md) | [Sample Client](#sample-client) | [Known Issues and Limitations](#known-issues-and-limitations) |
+
+## Requirements
+
+The following instructions require a Linux system with Docker
+installed. For CUDA support, make sure your CUDA driver meets the
+requirements in the "NVIDIA Driver" section of [Deep Learning Framework
+support matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
+
+## Building the Triton Inference Server Image
+
+The example is designed based on the
+`nvcr.io/nvidia/tritonserver:YY.MM-py3` docker image and [TensorRT OSS v9.2.0](https://github.com/NVIDIA/TensorRT/releases/tag/v9.2.0).
+
+A set of convenience scripts are provided to create a docker image
+based on the `nvcr.io/nvidia/tritonserver:YY.MM-py3` image with the
+dependencies for the TensorRT Stable Diffusion demo installed.
+
+### Triton Inference Server + TensorRT OSS
+
+#### Clone Repository
+```bash
+git clone https://github.com/triton-inference-server/tutorials.git --single-branch
+cd tutorials/Popular_Models_Guide/StableDiffusion
+```
+
+#### Build Tritonserver Diffusion Docker Image
+```bash
+./build.sh
+```
+
+#### Included Models
+
+The `default` build includes model configuration files located in the
+`/diffusion-models` folder. Example configurations are provided for
+[`stable_diffusion_1_5`](diffusion-models/stable_diffustion_1_5) and
+[`stable_diffusion_xl`](diffusion-models/stable_diffustion_xl).
+
+Model artifacts and engine files are not included in the image but are
+built into a volume mounted directory as a separate step.
+
+## Building and Running Stable Diffusion v 1.5
+
+### Start Tritonserver Diffusion Container
+
+The following command starts a container and volume mounts the current
+directory as `workspace`.
+
+```bash
+./run.sh
+```
+
+### Build Stable Diffusion v 1.5 Engine
+
+```bash
+./scripts/build_models.sh --model stable_diffusion_1_5
+```
+
+#### Expected Output
+```
+ diffusion-models
+|-- stable_diffusion_1_5
+|   |-- 1
+|   |   |-- 1.5-engine-batch-size-1
+|   |   |-- 1.5-onnx
+|   |   |-- 1.5-pytorch_model
+|   `-- config.pbtxt
+
+```
+
+### Start a Server Instance
+
+> [!Note]
+> We use `EXPLICIT` model control mode for demonstration purposes to
+> control which stable diffusion version is loaded.  For production
+> deployments please refer to [Secure Deployment Considerations][secure_guide]
+> for more information on the risks associated with `EXPLICIT` mode.
+
+[secure_guide]: https://github.com/triton-inference-server/server/blob/main/docs/customization_guide/deploy.md
+
+```bash
+tritonserver --model-repository diffusion-models --model-control-mode explicit --load-model stable_diffusion_1_5
+```
+
+#### Expected Output
+```
+<SNIP>
+I0229 20:15:52.125050 749 server.cc:676]
++----------------------+---------+--------+
+| Model                | Version | Status |
++----------------------+---------+--------+
+| stable_diffusion_1_5 | 1       | READY  |
++----------------------+---------+--------+
+
+<SNIP>
+```
+
+## Building and Running Stable Diffusion XL
+
+### Start `tritonserver:rYY.MM-diffusion` Container
+
+The following command starts a container and volume mounts the current
+directory as `workspace`.
+
+```bash
+./run.sh
+```
+
+### Build Stable Diffusion XL Engine
+
+```bash
+./scripts/build_models.sh --model stable_diffusion_xl
+```
+
+#### Expected Output
+```
+ diffusion-models
+ |-- stable_diffusion_xl
+    |-- 1
+    |   |-- xl-1.0-engine-batch-size-1
+    |   |-- xl-1.0-onnx
+    |   `-- xl-1.0-pytorch_model
+    `-- config.pbtxt
+```
+
+### Start a Server Instance
+
+> [!Note]
+> We use `EXPLICIT` model control mode for demonstration purposes to
+> control which stable diffusion version is loaded.  For production
+> deployments please refer to [Secure Deployment Considerations][secure_guide]
+> for more information on the risks associated with `EXPLICIT` mode.
+
+
+```bash
+tritonserver --model-repository diffusion-models --model-control-mode explicit --load-model stable_diffusion_xl
+```
+
+#### Expected Output
+```
+<SNIP>
+I0229 20:22:22.912465 1440 server.cc:676]
++---------------------+---------+--------+
+| Model               | Version | Status |
++---------------------+---------+--------+
+| stable_diffusion_xl | 1       | READY  |
++---------------------+---------+--------+
+
+<SNIP>
+```
+
+## Sending an Inference Request
+
+We've provided a sample [client](client.py) application to make
+sending and receiving requests simpler.
+
+### Start Tritonserver Diffusion Container
+
+In a separate terminal from the server start a new container.
+
+The following command starts a container and volume mounts the current
+directory as `workspace`.
+
+```bash
+./run.sh
+```
+
+
+### Send Prompt to Stable Diffusion 1.5
+
+```bash
+python3 client.py --model stable_diffusion_1_5 --prompt "butterfly in new york, 4k, realistic" --save-image
+```
+
+#### Example Output
+
+```bash
+Client: 0 Throughput: 0.7201335361144658 Avg. Latency: 1.3677194118499756
+Throughput: 0.7163933558221957 Total Time: 1.395881175994873
+```
+
+If `--save-image` is given then output images will be saved as jpegs.
+
+`
+ client_0_generated_image_0.jpg
+`
+
+![sample_generated_image](./docs/client_0_generated_image_0_1_5.jpg)
+
+
+### Send Prompt to Stable Diffusion XL
+
+```bash
+python3 client.py --model stable_diffusion_xl --prompt "butterfly in new york, 4k, realistic" --save-image
+```
+
+#### Example Output
+
+```bash
+Client: 0 Throughput: 0.1825067711674996 Avg. Latency: 5.465569257736206
+Throughput: 0.18224859609447058 Total Time: 5.487010717391968
+```
+
+If `--save-image` is given then output images will be saved as jpegs.
+
+`
+ client_0_generated_image_0.jpg
+`
+
+![sample_generated_image](./docs/client_0_generated_image_0_xl.jpg)
+
+
+## Sample Client
+
+The sample [client](client.py) application enables users to quickly
+test the diffusion models under different concurrency scenarios. For a
+full list and description of the client application's options use:
+
+```
+python3 client.py --help
+```
+
+### Sending Concurrent Requests
+
+To increase load and concurrency users can use the `clients` and
+`requests` options to control the number of client processes and the
+number of requests sent by each client.
+
+#### Example: Ten Clients Sending Ten Requests Each
+
+The following command enables ten clients each sending ten
+requests. Each client is an independent process that sends its
+requests one after the other in parallel with the other nine clients.
+
+```bash
+python3 client.py --model stable_diffusion_xl --requests 10 --clients 10
+```
+
+## Known Issues and Limitations
+
+1. When shutting down the server, an invalid memory operation occurs:
+
+   > [!Note]
+   > This error is also seen in standalone applications outside of the Triton Inference Server
+   > and we believe this is due to an interaction between imported python modules. Further
+   > we haven't seen any issues related to this error and believe it can be safely
+   > ignored.
+
+
+   ```
+   free(): invalid pointer
+   ```
+
+
+2. The diffusion backend doesn't yet support using an optional refiner
+   model unlike the [demo][demo_reference] it's based on. See also
+   [demo_txt2img_xl.py][demo_code]
+
+
+[demo_code]: https://github.com/NVIDIA/TensorRT/blob/release/9.2/demo/Diffusion/demo_txt2img_xl.py
+
+
+[demo_reference]: https://github.com/NVIDIA/TensorRT/tree/release/9.2/demo/Diffusion#text-to-image-using-sdxl-stable-diffusion-xl
+