Stable Diffusion CLI
This example shows Stable Diffusion 1.5 with a number of optimizations that makes it run faster on Modal. The example takes about 10s to cold start and about 1.0s per image generated.
To use the new XL 1.0 model, see the example posted here.
For instance, here are 9 images produced by the prompt
An 1600s oil painting of the New York City skyline
There is also a Stable Diffusion Slack bot example which does not have all the optimizations, but shows how you can set up a Slack command to trigger Stable Diffusion.
Optimizations used in this example
As mentioned, we use a few optimizations to run this faster:
- Use run_function to download the model while building the container image
- Use a container lifecycle method to initialize the model on container startup
- Use A10G GPUs
- Use 16 bit floating point math
Basic setup
from __future__ import annotations
import io
import time
from pathlib import Path
from modal import Image, Stub, methodAll Modal programs need a Stub — an object that acts as a recipe for
the application. Let’s give it a friendly name.
stub = Stub("stable-diffusion-cli")Model dependencies
Your model will be running remotely inside a container. We will be installing all the model dependencies in the next step. We will also be “baking the model” into the image by running a Python function as a part of building the image. This lets us start containers much faster, since all the data that’s needed is already inside the image.
model_id = "runwayml/stable-diffusion-v1-5"
cache_path = "/vol/cache"
def download_models():
import diffusers
import torch
# Download scheduler configuration. Experiment with different schedulers
# to identify one that works best for your use-case.
scheduler = diffusers.DPMSolverMultistepScheduler.from_pretrained(
model_id,
subfolder="scheduler",
cache_dir=cache_path,
)
scheduler.save_pretrained(cache_path, safe_serialization=True)
# Downloads all other models.
pipe = diffusers.StableDiffusionPipeline.from_pretrained(
model_id,
revision="fp16",
torch_dtype=torch.float16,
cache_dir=cache_path,
)
pipe.save_pretrained(cache_path, safe_serialization=True)
image = (
Image.debian_slim(python_version="3.10")
.pip_install(
"accelerate",
"diffusers[torch]>=0.15.1",
"ftfy",
"torchvision",
"transformers~=4.25.1",
"triton",
"safetensors",
)
.pip_install(
"torch==2.0.1+cu117",
find_links="https://download.pytorch.org/whl/torch_stable.html",
)
.pip_install("xformers", pre=True)
.run_function(download_models)
)
stub.image = imageUsing container lifecycle methods
Modal lets you implement code that runs every time a container starts. This can be a huge optimization when you’re calling a function multiple times, since Modal reuses the same containers when possible.
The way to implement this is to turn the Modal function into a method on a
class that also implement the Python context manager interface, meaning it
has the __enter__ method (the __exit__ method is optional).
We have also have applied a few model optimizations to make the model run faster. On an A10G, the model takes about 6.5s to load into memory, and then 1.6s per generation on average. On a T4, it takes 13s to load and 3.7s per generation. Other optimizations are also available here.
This is our Modal function. The function runs through the StableDiffusionPipeline pipeline.
It sends the PIL image back to our CLI where we save the resulting image in a local file.
@stub.cls(gpu="A10G")
class StableDiffusion:
def __enter__(self):
import diffusers
import torch
torch.backends.cuda.matmul.allow_tf32 = True
scheduler = diffusers.DPMSolverMultistepScheduler.from_pretrained(
cache_path,
subfolder="scheduler",
solver_order=2,
prediction_type="epsilon",
thresholding=False,
algorithm_type="dpmsolver++",
solver_type="midpoint",
denoise_final=True, # important if steps are <= 10
low_cpu_mem_usage=True,
device_map="auto",
)
self.pipe = diffusers.StableDiffusionPipeline.from_pretrained(
cache_path,
scheduler=scheduler,
low_cpu_mem_usage=True,
device_map="auto",
)
self.pipe.enable_xformers_memory_efficient_attention()
@method()
def run_inference(
self, prompt: str, steps: int = 20, batch_size: int = 4
) -> list[bytes]:
import torch
with torch.inference_mode():
with torch.autocast("cuda"):
images = self.pipe(
[prompt] * batch_size,
num_inference_steps=steps,
guidance_scale=7.0,
).images
# Convert to PNG bytes
image_output = []
for image in images:
with io.BytesIO() as buf:
image.save(buf, format="PNG")
image_output.append(buf.getvalue())
return image_outputThis is the command we’ll use to generate images. It takes a prompt,
samples (the number of images you want to generate), steps which
configures the number of inference steps the model will make, and batch_size
which determines how many images to generate for a given prompt.
@stub.local_entrypoint()
def entrypoint(
prompt: str, samples: int = 5, steps: int = 10, batch_size: int = 1
):
print(
f"prompt => {prompt}, steps => {steps}, samples => {samples}, batch_size => {batch_size}"
)
dir = Path("/tmp/stable-diffusion")
if not dir.exists():
dir.mkdir(exist_ok=True, parents=True)
sd = StableDiffusion()
for i in range(samples):
t0 = time.time()
images = sd.run_inference.remote(prompt, steps, batch_size)
total_time = time.time() - t0
print(
f"Sample {i} took {total_time:.3f}s ({(total_time)/len(images):.3f}s / image)."
)
for j, image_bytes in enumerate(images):
output_path = dir / f"output_{j}_{i}.png"
print(f"Saving it to {output_path}")
with open(output_path, "wb") as f:
f.write(image_bytes)And this is our entrypoint; where the CLI is invoked. Explore CLI options
with: modal run stable_diffusion_cli.py --help
Performance
This example can generate pictures in about a second, with startup time of about 10s for the first picture.
See distribution of latencies below. This data was gathered by running 500 requests in sequence (meaning only the first request incurs a cold start). As you can see, the 90th percentile is 1.2s and the 99th percentile is 2.30s.
