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isidentical

AI & ML interests

Fast ML inference, 100% GPU utilization, HBM3e and fast memory

Recent Activity

liked a model about 1 month ago
genmo/mochi-1-preview
liked a model about 1 month ago
fal/AuraEquiVAE
New activity about 2 months ago
fal/AuraFlow-v0.3

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isidentical's activity

reacted to their post with ๐Ÿš€๐Ÿ”ฅ 3 months ago
posted an update 3 months ago
posted an update 3 months ago
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587
Added FLUX.1 pro/dev/schnell and AuraFlow v0.2 to fal/imgsys !!! Go play with it and get us some votez
reacted to their post with โค๏ธ๐Ÿ”ฅ 3 months ago
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1769
fal/AuraFlow-v0.3 is now here with support for different aspect resolutions (w/h up to 1536px!) and much nicer aesthetics! Make sure to install the latest diffusers to get support for it.
posted an update 3 months ago
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1769
fal/AuraFlow-v0.3 is now here with support for different aspect resolutions (w/h up to 1536px!) and much nicer aesthetics! Make sure to install the latest diffusers to get support for it.
posted an update 4 months ago
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Announcing the second open model in our Aura series of media models at @fal : fal/AuraFlow

Try it using diffusers or ComfyUI from publicly available weights, and read more about it in our blog https://blog.fal.ai/auraflow.
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posted an update 5 months ago
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1509
It is time for some Aura.

First in our series of fully open sourced / commercially available models by @fal-ai : AuraSR - a 600M parameter upscaler based on GigaGAN.

Blog: https://blog.fal.ai/introducing-aurasr-an-open-reproduction-of-the-gigagan-upscaler-2/

HF: https://huggingface.co/fal-ai/AuraSR

Code: https://github.com/fal-ai/aura-sr

Playground: https://fal.ai/models/fal-ai/aura-sr/playground

What other models would you like to see open-sourced and commercially available? :)
posted an update 6 months ago
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One shot evaluations is hard. That is honestly what I learnt throughout the last couple of weeks trying to make imgsys.org data more and more relevant. There is just so much diversity in these models that saying one is better than other one even at a particular domain is impossible.

If you have any suggestions on how we can make the testing easier for one shot, single question image model testing; please give your suggestions under this thread so we can provide a more meaningful data point to the community!
reacted to ehristoforu's post with ๐Ÿ”ฅ 6 months ago
reacted to not-lain's post with ๐Ÿ‘ 6 months ago
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1528
If you're a researcher or developing your own model ๐Ÿ‘€ you might need to take a look at huggingface's ModelHubMixin classes.
They are used to seamlessly integrate your AI model with huggingface and to save/ load your model easily ๐Ÿš€

1๏ธโƒฃ make sure you're using the appropriate library version
pip install -qU "huggingface_hub>=0.22"

2๏ธโƒฃ inherit from the appropriate class
from huggingface_hub import PyTorchModelHubMixin
from torch import nn

class MyModel(nn.Module,PyTorchModelHubMixin):
  def __init__(self, a, b):
    super().__init__()
    self.layer = nn.Linear(a,b)
  def forward(self,inputs):
    return self.layer(inputs)

first_model = MyModel(3,1)

4๏ธโƒฃ push the model to the hub (or use save_pretrained method to save locally)
first_model.push_to_hub("not-lain/test")

5๏ธโƒฃ Load and initialize the model from the hub using the original class
pretrained_model = MyModel.from_pretrained("not-lain/test")

reacted to vikhyatk's post with โค๏ธ 6 months ago
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1751
Cool new dataset from @isidentical - isidentical/moondream2-coyo-5M-captions

The VeCLIP paper showed a +3% gain while only using 14% of the data by synthetically captioning like this. You get diversity from the alt text (middle column) without having to deal with all of the noise.
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reacted to Fizzarolli's post with ๐Ÿ‘ 6 months ago
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2230
Is anyone looking into some sort of decentralized/federated dataset generation or classification by humans instead of synthetically?

From my experience with trying models, a *lot* of modern finetunes are trained on what amounts to, in essence, GPT-4 generated slop that makes everything sound like a rip-off GPT-4 (refer to i.e. the Dolphin finetunes). I have a feeling that this is a lot of the reason people haven't been quite as successful as Meta's instruct tunes of Llama 3.
posted an update 7 months ago
posted an update 10 months ago
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What is the current SOTA in terms of fast personalized image generation? Most of the techniques that produce great results (which is hard to objectively measure, but subject similarity index being close to 80-90%) take either too much time (full on DreamBooth fine-tuning the base model) or or loose on the auxilary properties (high rank LoRAs).

We have been also testing face embeddings, but even with multiple samples the quality is not anywhere close to what we expect. Even on the techniques that work, high quality (studio-level) pictures seem to be a must so another avenue that I'm curious is whether there is filter/segmentation of the input samples in an automatic way that people have looked in the past?
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