# Distilled Stable Diffusion inference
## Distilled Stable Diffusion inference
Stable Diffusion inference can be a computationally intensive process because it must iteratively denoise the latents to generate an image. To reduce the computational burden, you can use a *distilled* version of the Stable Diffusion model from [Nota AI](https://huggingface.co/nota-ai). The distilled version of their Stable Diffusion model eliminates some of the residual and attention blocks from the UNet, reducing the model size by 51% and improving latency on CPU/GPU by 43%.
Read this [blog post](https://huggingface.co/blog/sd_distillation) to learn more about how knowledge distillation training works to produce a faster, smaller, and cheaper generative model.
Let’s load the distilled Stable Diffusion model and compare it against the original Stable Diffusion model:
Copied
```
from diffusers import StableDiffusionPipeline
import torch
distilled = StableDiffusionPipeline.from_pretrained(
"nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
original = StableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
```
Given a prompt, get the inference time for the original model:
Copied
```
import time
seed = 2023
generator = torch.manual_seed(seed)
NUM_ITERS_TO_RUN = 3
NUM_INFERENCE_STEPS = 25
NUM_IMAGES_PER_PROMPT = 4
prompt = "a golden vase with different flowers"
start = time.time_ns()
for _ in range(NUM_ITERS_TO_RUN):
images = original(
prompt,
num_inference_steps=NUM_INFERENCE_STEPS,
generator=generator,
num_images_per_prompt=NUM_IMAGES_PER_PROMPT
).images
end = time.time_ns()
original_sd = f"{(end - start) / 1e6:.1f}"
print(f"Execution time -- {original_sd} ms\n")
"Execution time -- 45781.5 ms"
```
Time the distilled model inference:
Copied
```
start = time.time_ns()
for _ in range(NUM_ITERS_TO_RUN):
images = distilled(
prompt,
num_inference_steps=NUM_INFERENCE_STEPS,
generator=generator,
num_images_per_prompt=NUM_IMAGES_PER_PROMPT
).images
end = time.time_ns()
distilled_sd = f"{(end - start) / 1e6:.1f}"
print(f"Execution time -- {distilled_sd} ms\n")
"Execution time -- 29884.2 ms"
```
original Stable Diffusion (45781.5 ms)
distilled Stable Diffusion (29884.2 ms)
### Tiny AutoEncoder
To speed inference up even more, use a tiny distilled version of the [Stable Diffusion VAE](https://huggingface.co/sayakpaul/taesdxl-diffusers) to denoise the latents into images. Replace the VAE in the distilled Stable Diffusion model with the tiny VAE:
Copied
```
from diffusers import AutoencoderTiny
distilled.vae = AutoencoderTiny.from_pretrained(
"sayakpaul/taesd-diffusers", torch_dtype=torch.float16, use_safetensors=True,
).to("cuda")
```
Time the distilled model and distilled VAE inference:
Copied
```
start = time.time_ns()
for _ in range(NUM_ITERS_TO_RUN):
images = distilled(
prompt,
num_inference_steps=NUM_INFERENCE_STEPS,
generator=generator,
num_images_per_prompt=NUM_IMAGES_PER_PROMPT
).images
end = time.time_ns()
distilled_tiny_sd = f"{(end - start) / 1e6:.1f}"
print(f"Execution time -- {distilled_tiny_sd} ms\n")
"Execution time -- 27165.7 ms"
```
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://boinc-ai.gitbook.io/diffusers/using-diffusers/pipelines-for-inference/distilled-stable-diffusion-inference.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
