LLM game

2023 vimalabs.github.io/ VIMA: General Robot Manipulation with Multimodal Prompts

Stanford Smallville (2023)

github.com/joonspk-research/generative_agents

Published as: arxiv.org/pdf/2304.03442.pdf Generative Agents: Interactive Simulacra of Human Behavior by Park et al.

Video 1. AI Agents Behaving Like Humans by Prompt Engineering (2023) Source.

Ollama

github.com/jmorganca/ollama

Highly automated wrapper for various open source LLMs.

curl https://ollama.ai/install.sh | sh
ollama run llama2

And bang, a download later, you get a prompt. On P14s it runs on CPU and generates a few tokens at a time, which is quite usable for a quick interactive play.

As mentioned at github.com/jmorganca/ollama/blob/0174665d0e7dcdd8c60390ab2dd07155ef84eb3f/docs/faq.md the downloads to under /usr/share/ollama/.ollama/models/ and ncdu tells me:

--- /usr/share/ollama ----------------------------------
    3.6 GiB [###########################] /.ollama
    4.0 KiB [                           ]  .bashrc
    4.0 KiB [                           ]  .profile
    4.0 KiB [                           ]  .bash_logout

We can also do it non-interactively with:

/bin/time ollama run llama2 'What is quantum field theory?'

which gave me:

0.13user 0.17system 2:06.32elapsed 0%CPU (0avgtext+0avgdata 17280maxresident)k
0inputs+0outputs (0major+2203minor)pagefaults 0swaps

but note that there is a random seed that affects each run by default.

Some other quick benchmarks from Amazon EC2 GPU, on Nvidia T4:

0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swaps

On Nvidia A10G:


0.03user 0.05system 0:09.59elapsed 0%CPU (0avgtext+0avgdata 17312maxresident)k
8inputs+0outputs (1major+1934minor)pagefaults 0swaps

So it's not too bad, a small article in 10s.

It tends to babble quite a lot by default, but eventually decides to stop.

TODO is it possible to make it deterministic on the CLI? There is a "seed" parameter somewhere: github.com/jmorganca/ollama/blob/31f0551dab9a10412ec6af804445e02a70a25fc2/docs/modelfile.md#parameter

Open source text-to-image model

Bibliography:

www.edenai.co/post/top-free-image-generation-tools-apis-and-open-source-models

This just works, but it is also so incredibly slow that it is useless (or at least the quality it reaches in the time we have patience to wait from), at least on any setup we've managed to try, including e.g. on an Nvidia A10G on a g5.xlarge. Running:

time imagine "a house in the forest"

would likely take hours to complete.

runwayml/stable-diffusion

github.com/runwayml/stable-diffusion

Conda install is a bit annoying, but gets the job done. The generation quality is very good.

Someone should package this better for end user "just works after Conda install" image generation, it is currently much more of a library setup.

Tested on Amazon EC2 on a g5.xlarge machine, which has an Nvidia A10G, using the AWS Deep Learning Base GPU AMI (Ubuntu 20.04) image.

First install Conda as per Section "Install Conda on Ubuntu", and then just follow the instructions from the README, notably the Reference sampling script section.

git clone https://github.com/runwayml/stable-diffusion
cd stable-diffusion/
git checkout 08ab4d326c96854026c4eb3454cd3b02109ee982
conda env create -f environment.yaml
conda activate ldm
mkdir -p models/ldm/stable-diffusion-v1/
wget -O models/ldm/stable-diffusion-v1/model.ckpt https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
python scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms

This took about 2 minutes and generated 6 images under outputs/txt2img-samples/samples, includining an image outputs/txt2img-samples/grid-0000.png which is a grid montage containing all the six images in one:

https://raw.githubusercontent.com/cirosantilli/media/master/Runwayml_stable-diffusion_a-photograph-of-an-astronaut-riding-a-horse.png

TODO how to change the number of images?

A quick attempt at removing their useless safety features (watermark and NSFW text filter) is:

diff --git a/scripts/txt2img.py b/scripts/txt2img.py
index 59c16a1..0b8ef25 100644
--- a/scripts/txt2img.py
+++ b/scripts/txt2img.py
@@ -87,10 +87,10 @@ def load_replacement(x):
 def check_safety(x_image):
     safety_checker_input = safety_feature_extractor(numpy_to_pil(x_image), return_tensors="pt")
     x_checked_image, has_nsfw_concept = safety_checker(images=x_image, clip_input=safety_checker_input.pixel_values)
-    assert x_checked_image.shape[0] == len(has_nsfw_concept)
-    for i in range(len(has_nsfw_concept)):
-        if has_nsfw_concept[i]:
-            x_checked_image[i] = load_replacement(x_checked_image[i])
+    #assert x_checked_image.shape[0] == len(has_nsfw_concept)
+    #for i in range(len(has_nsfw_concept)):
+    #    if has_nsfw_concept[i]:
+    #        x_checked_image[i] = load_replacement(x_checked_image[i])
     return x_checked_image, has_nsfw_concept


@@ -314,7 +314,7 @@ def main():
                             for x_sample in x_checked_image_torch:
                                 x_sample = 255. * rearrange(x_sample.cpu().numpy(), 'c h w -> h w c')
                                 img = Image.fromarray(x_sample.astype(np.uint8))
-                                img = put_watermark(img, wm_encoder)
+                                # img = put_watermark(img, wm_encoder)
                                 img.save(os.path.join(sample_path, f"{base_count:05}.png"))
                                 base_count += 1

but that produced 4 black images and only two unfiltered ones. Also likely the lack of sexusl training data makes its porn suck, and not in the good way.