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-# StableDiffusion Sample Code
-
-## Introduction
-This is sample code for using QAI AppBuilder to load Stable Diffusion 1.5 QNN models, run the inference and free the resource. 
-
-## Python Environment
-Please setup the Python environment according to the guide below:<br>
-https://github.com/quic/ai-engine-direct-helper/blob/main/docs/user_guide.md<br>
-Make sure to use the right QAI AppBuilder version according to the QNN SDK version which the models were generated.
-
-## Stable Diffusion QNN models
-You need to generate Stable Diffusion QNN models according to the guide below before you running it with this sample code:
-https://docs.qualcomm.com/bundle/publicresource/topics/80-64748-1/introduction.html
-
-After the models are ready, please copy them to the following path:
-```
-c:\ai-app\SD_1.5\models\sd_v1.5\stable_diffusion_v1_5_quantized-textencoder_quantized.bin
-c:\ai-app\SD_1.5\models\sd_v1.5\stable_diffusion_v1_5_quantized-unet_quantized.bin
-c:\ai-app\SD_1.5\models\sd_v1.5\stable_diffusion_v1_5_quantized-vaedecoder_quantized.bin
-```
-
-## time-embedding
-In this sample code, we need to use 'time-embedding' data. The below code can be used to generate the 'time-embedding' data:
-```
-import os
-import torch
-import numpy as np
-from diffusers.models.embeddings import get_timestep_embedding
-from diffusers import UNet2DConditionModel
-from diffusers import DPMSolverMultistepScheduler
-
-user_step = 20
-time_embeddings = UNet2DConditionModel.from_pretrained('runwayml/stable-diffusion-v1-5', subfolder='unet', cache_dir='./cache').time_embedding
-scheduler = DPMSolverMultistepScheduler(num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
-
-def get_timestep(step):
-    return np.int32(scheduler.timesteps.numpy()[step])
-
-def get_time_embedding(timestep):
-    timestep = torch.tensor([timestep])
-    t_emb = get_timestep_embedding(timestep, 320, True, 0)
-    emb = time_embeddings(t_emb).detach().numpy()
-    return emb
-
-def gen_time_embedding():
-    scheduler.set_timesteps(user_step)
-    
-    time_emb_path = ".\\models\\time-embedding_v1.5\\" + str(user_step) + "\\"
-    os.mkdir(time_emb_path)
-    for step in range(user_step):
-        file_path = time_emb_path + str(step) + ".raw"
-        timestep = get_timestep(step)
-        time_embedding = get_time_embedding(timestep)
-        time_embedding.tofile(file_path)
-
-# Only needs to executed once for generating time enbedding data to app folder.
-# Modify 'user_step' to '20', '30', '50' to generate 'time_embedding' for steps - '20', '30', '50'.
-
-user_step = 20
-gen_time_embedding()
-
-user_step = 30
-gen_time_embedding()
-
-user_step = 50
-gen_time_embedding()
-```
-
-After generated the 'time-embedding' data, please copy them to the following path:
-```
-c:\ai-app\SD_1.5\models\time-embedding\20
-c:\ai-app\SD_1.5\models\time-embedding\30
-c:\ai-app\SD_1.5\models\time-embedding\50
-```
-
-## CLIP ViT-L/14 model
-In this sample code, we need CLIP ViT-L/14 as text encoder. You can download the file below from 'https://huggingface.co/openai/clip-vit-large-patch14/tree/main' and save them to foldet 'clip-vit-large-patch14'. 
-Rename the files to below:
-```
-merges.txt
-special_tokens_map.json
-tokenizer_config.json
-vocab.json
-```
-
-After downloaded the model, please copy them to the following path:
-```
-c:\ai-app\SD_1.5\models\clip-vit-large-patch14
-```
-
-## Run the sample code
-Download the sample code from the following link: <br>
-https://github.com/quic/ai-engine-direct-helper/blob/main/Samples/StableDiffusion/StableDiffusion.py
-
-After downloaded the sample code, please copy them to the following path:
-```
-c:\ai-app\SD_1.5\
-```
-
-Run the sample code:
-```
-python StableDiffusion.py
-```
-
-## Output
-The output image will be saved to the following path:
-```
-c:\ai-app\SD_1.5\images\
-```
-
-## Reference
-You need to setup the QAI AppBuilder environment before you run the sample code. Below is the guide on how to setup the QAI AppBuilder environment:<br>
-https://github.com/quic/ai-engine-direct-helper/blob/main/README.md <br>
-https://github.com/quic/ai-engine-direct-helper/blob/main/Docs/User_Guide.md