Diffusion Image Generation

19. Diffusion Image Generation#

19.1. Pre-reading#

19.1.1. Objectives#

  • Apply lessons-learned from containerization.

  • Get a diffusion text to image model working on Jetson.

  • Use ChatGPT to serve working model as API.

19.2. Image generation#

My goal is to serve a text-to-image model on the Jetson Orin Nano.

19.2.1. The tutorial#

I first read Does Stable Diffusion run on NVIDIA Jetson AGX Xavier Developer Kit with CUDA? and thought this might be possible!

Following the tutorial under Run Diffusers on Docker, I quickly came to this command:

git clone https://huggingface.co/runwayml/stable-diffusion-v1-5

Even with git-lfs installed, this was taking foreverā€¦ I pulled the plug after 25 GB came down with dozens more promised.

Going to take a different approach, but noting the Dockerfile for later reference, even though I can tell this person is not a docker expert.

19.2.2. The documentation#

The Hugging Face Diffusers Documentation has the suggestion: Choose an AutoPipeline for your task šŸ™šŸ¼

from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
prompt = "peasant and dragon combat, wood cutting style, viking era, bevel with rune"

image = pipeline(prompt, num_inference_steps=25).images[0]

Under the hood, AutoPipelineForText2Image:

  1. automatically detects a ā€œstable-diffusionā€ class from the model_index.json file

  2. loads the corresponding text-to-image StableDiffusionPipline based on the ā€œstable-diffusionā€ class name.

Letā€™s run that!ā€¦ on my Intel NUC.

19.2.3. Try it on the NUC#

I do not have an NVIDIA GPU on this device. So, a quick DuckDuckGo search (ChatGPT actually got this one wrong) tells me about torch.cuda.is_available(). So I write:

from torch import cuda

# Use GPU if available; otherwise, cpu
if cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

Then I just replace to.("cuda") with to.(device).

Install dependencies:

pip install torch  diffusers[torch] transformers

Then run it: great success! (After about 3:20, yikes!)

Hereā€™s how to display your image:

# Explore what this image even is
type(image)
# Oh, it's a PIL! We can just
image.show()

19.2.4. Add the API#

I want this thing to be served. Letā€™s try ChatGPT.

Time for some prompt engineering.

First, I know I need to be specific about what I want as well as the steps I want the LLM to take.

Iā€™ve heard FastAPI is a good choice, but have never used it before.

19.2.4.1. Prompt 1#

Here is what I try first

I want to adapt the following code to serve a REST API where the user
passes a prompt with `prompt` and a JPEG is returned.

~~~python
from diffusers import AutoPipelineForText2Image
from torch import cuda

# https://huggingface.co/runwayml/stable-diffusion-v1-5
diffusion_model: str = "runwayml/stable-diffusion-v1-5"

# Use GPU if available; otherwise, cpu
if cuda.is_available():
    device = "cuda"
else:
    device = "cpu"

# The auto pipeline automatically detects and loads the model
# https://huggingface.co/docs/diffusers/tutorials/autopipeline#choose-an-autopipeline-for-your-task
# Use_safetensors enables a NSFW filter.
pipeline = AutoPipelineForText2Image.from_pretrained(
    diffusion_model, use_safetensors=True
).to(device)

# Generate the image based on the prompt
prompt = "A falcon flying over mountains"
image = pipeline(prompt, num_inference_steps=25).images[0]
~~~

1. Create the FastAPI function `generate_img`
2. Use the `prompt` and `pipeline` to make the image
3. Convert the image to JPEG in memory
4. Return the image

I also want you to talk about how to serve this API and how to call it
using `curl -o`.

Ok, tbh this is totally not how I did it. I walked the dog with individual prompts, starting from a basic example. I would run those, search some things or ask for clarification, and iterate.

This prompt will probably give you working code, but it also will almost certainly be way too complex.

For example, I ended up with a GET method doing it bit-by-bit but the prompt above gave me a POST method. Maybe thatā€™s better, in truth.

At minimum, you should

  1. Have @app.post or @app.get macro

  2. Run the app with uvicorn

  3. Be able to curl the port and save the image. At this point, single word prompts with no whitespace are fine.

19.3. Containerize the app#

Here is a Dockerfile to get you started:

FROM nvcr.io/nvidia/pytorch:23.09-py3

EXPOSE 5858

WORKDIR /app
COPY ./generate_img.py /app

# Ideally, pin to versions you know work.
RUN pip install --no-cache-dir \

# TODO

  1. Switch from the generic nvidia/pytorch base to the one designed for the Jetson.

  2. Finish writing generate_img.py and make sure it is in the same directory as your Dockerfile.

  3. Finish installing dependencies with pip.

  4. Write the CMD layer.

  5. Figure out the docker run command.

    • You should use a -v volume to reuse the model cache

    • You need to bind the correct port

After all that, you should be able to curl your container and get a generated image!

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