classifier-free-guidance-pytorch

Classifier Free Guidance - Pytorch

Implementation of Classifier Free Guidance in Pytorch, with emphasis on text conditioning, and flexibility to include multiple text embedding models, as done in eDiff-I

It is clear now that text guidance is the ultimate interface to models. This repository will leverage some python decorator magic to make it easy to incorporate SOTA text conditioning to any model.

Update: there is a chance that CFG has been obsoleted by a new paper

Appreciation

• StabilityAI for the generous sponsorship, as well as my other sponsors out there

• 🤗 Huggingface for their amazing transformers library. The text conditioning module will use T5 embeddings, as latest research recommends

• OpenCLIP for providing SOTA open sourced CLIP models. The eDiff model sees immense improvements by combining the T5 embeddings with CLIP text embeddings

Install

$ pip install classifier-free-guidance-pytorch

Usage

import torch
from classifier_free_guidance_pytorch import TextConditioner

text_conditioner = TextConditioner(
    model_types = 't5',    
    hidden_dims = (256, 512),
    hiddens_channel_first = False,
    cond_drop_prob = 0.2  # conditional dropout 20% of the time, must be greater than 0. to unlock classifier free guidance
).cuda()

# pass in your text as a List[str], and get back a List[callable]
# each callable function receives the hiddens in the dimensions listed at init (hidden_dims)

first_condition_fn, second_condition_fn = text_conditioner(['a dog chasing after a ball'])

# these hiddens will be in the direct flow of your model, say in a unet

first_hidden = torch.randn(1, 16, 256).cuda()
second_hidden = torch.randn(1, 32, 512).cuda()

# conditioned features

first_conditioned = first_condition_fn(first_hidden)
second_conditioned = second_condition_fn(second_hidden)

If you wish to use cross attention based conditioning (each hidden feature in your network can attend to individual subword tokens), just import the AttentionTextConditioner instead. Rest is the same

from classifier_free_guidance_pytorch import AttentionTextConditioner

text_conditioner = AttentionTextConditioner(
    model_types = ('t5', 'clip'),   # something like in eDiff paper, where they used both T5 and Clip for even better results (Balaji et al.)
    hidden_dims = (256, 512),
    cond_drop_prob = 0.2
)

Magic Class Decorator

This is a work in progress to make it as easy as possible to text condition your network.

First, let's say you have a simple two layer network

import torch
from torch import nn

class MLP(nn.Module):
    def __init__(
        self,
        dim
    ):
        super().__init__()
        self.proj_in = nn.Sequential(nn.Linear(dim, dim * 2), nn.ReLU())
        self.proj_mid = nn.Sequential(nn.Linear(dim * 2, dim), nn.ReLU())
        self.proj_out = nn.Linear(dim, 1)

    def forward(
        self,
        data
    ):
        hiddens1 = self.proj_in(data)
        hiddens2 = self.proj_mid(hiddens1)
        return self.proj_out(hiddens2)

# instantiate model and pass in some data, get (in this case) a binary prediction

model = MLP(dim = 256)

data = torch.randn(2, 256)

pred = model(data)

You would like to condition the hidden layers (hiddens1 and hiddens2) with text. Each batch element here would get its own free text conditioning

This has been whittled down to ~3 step using this repository.

import torch
from torch import nn

from classifier_free_guidance_pytorch import classifier_free_guidance_class_decorator

@classifier_free_guidance_class_decorator
class MLP(nn.Module):
    def __init__(self, dim):
        super().__init__()

        self.proj_in = nn.Sequential(nn.Linear(dim, dim * 2), nn.ReLU())
        self.proj_mid = nn.Sequential(nn.Linear(dim * 2, dim), nn.ReLU())
        self.proj_out = nn.Linear(dim, 1)

    def forward(
        self,
        inp,
        cond_fns # List[Callable] - (1) your forward function now receives a list of conditioning functions, which you invoke on your hidden tensors
    ):
        cond_hidden1, cond_hidden2 = cond_fns # conditioning functions are given back in the order of the `hidden_dims` set on the text conditioner

        hiddens1 = self.proj_in(inp)
        hiddens1 = cond_hidden1(hiddens1) # (2) condition the first hidden layer with FiLM

        hiddens2 = self.proj_mid(hiddens1)
        hiddens2 = cond_hidden2(hiddens2) # condition the second hidden layer with FiLM

        return self.proj_out(hiddens2)

# instantiate your model - extra keyword arguments will need to be defined, prepended by `text_condition_`

model = MLP(
    dim = 256,
    text_condition_type = 'film',                 # can be film, attention, or null (none)
    text_condition_model_types = ('t5', 'clip'),  # in this example, conditioning on both T5 and OpenCLIP
    text_condition_hidden_dims = (512, 256),      # and pass in the hidden dimensions you would like to condition on. in this case there are two hidden dimensions (dim * 2 and dim, after the first and second projections)
    text_condition_cond_drop_prob = 0.25          # conditional dropout probability for classifier free guidance. can be set to 0. if you do not need it and just want the text conditioning
)

# now you have your input data as well as corresponding free text as List[str]

data = torch.randn(2, 256)
texts = ['a description', 'another description']

# (3) train your model, passing in your list of strings as 'texts'

pred  = model(data, texts = texts)

# after much training, you can now do classifier free guidance by passing in a condition scale of > 1. !

model.eval()
guided_pred = model(data, texts = texts, cond_scale = 3., remove_parallel_component = True)  # cond_scale stands for conditioning scale from classifier free guidance paper

Todo

• [x] complete film conditioning, without classifier free guidance (used here)

• [x] add classifier free guidance for film conditioning

• [x] complete cross attention conditioning

• [ ] stress test for spacetime unet in make-a-video

Citations

@article{Ho2022ClassifierFreeDG,
    title   = {Classifier-Free Diffusion Guidance},
    author  = {Jonathan Ho},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2207.12598}
}

@article{Balaji2022eDiffITD,
    title   = {eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers},
    author  = {Yogesh Balaji and Seungjun Nah and Xun Huang and Arash Vahdat and Jiaming Song and Karsten Kreis and Miika Aittala and Timo Aila and Samuli Laine and Bryan Catanzaro and Tero Karras and Ming-Yu Liu},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2211.01324}
}

@inproceedings{dao2022flashattention,
    title   = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
    author  = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
    booktitle = {Advances in Neural Information Processing Systems},
    year    = {2022}
}

@inproceedings{Lin2023CommonDN,
    title   = {Common Diffusion Noise Schedules and Sample Steps are Flawed},
    author  = {Shanchuan Lin and Bingchen Liu and Jiashi Li and Xiao Yang},
    year    = {2023}
}

@inproceedings{Chung2024CFGMC,
    title   = {CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models},
    author  = {Hyungjin Chung and Jeongsol Kim and Geon Yeong Park and Hyelin Nam and Jong Chul Ye},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:270391454}
}

@inproceedings{Sadat2024EliminatingOA,
    title   = {Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models},
    author  = {Seyedmorteza Sadat and Otmar Hilliges and Romann M. Weber},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273098845}
}