You can deploy this model and start earning money today!

nicolas-dufour/
CAD_256

Name: CAD_256
Availability: InStock

pytorch_model_hub_mixin

text-to-image

mit

Views

No views yet

Library: https://github.com/nicolas-dufour/CAD

Don’t drop your samples! Coherence-aware training benefits Conditional diffusion

Nicolas Dufour, Victor Besnier, Vicky Kalogeiton, David Picard

This repo has the code for the paper "Dont Drop Your samples: Coherence-aware training benefits Condition Diffusion" accepted at CVPR 2024 as a Highlight.

The core idea is that diffusion model is usually trained on noisy data. The usual solution is to filter massive datapools. We propose a new training method that leverages the coherence of the data to improve the training of diffusion models. We show that this method improves the quality of the generated samples on several datasets.

Project website: https://nicolas-dufour.github.io/cad

Install

To install, first create a conda env with python 3.10

conda create -n cad python=3.10

Activate the env

conda activate cad

For inference only,

pip install cad-diffusion

Pretrained models

To use the pretrained model do the following:

python

from cad import CADT2IPipeline

pipe = CADT2IPipeline("nicolas-dufour/CAD_256").to("cuda")

prompt = "An avocado armchair"

image = pipe(prompt, cfg=15)

If you just want to download the models, not the sampling pipeline, you can do:

python

from cad import CAD

model = CAD.from_pretrained("nicolas-dufour/CAD_256")

Models are hosted in the hugging face hub. The previous scripts download them automatically, but weights can be found at:

https://huggingface.co/nicolas-dufour/CAD_256

https://huggingface.co/nicolas-dufour/CAD_512

Using the Pipeline

The CADT2IPipeline class provides a comprehensive interface for generating images from text prompts. Here's a detailed guide on how to use it:

Basic Usage

python

from cad import CADT2IPipeline

# Initialize the pipeline
pipe = CADT2IPipeline("nicolas-dufour/CAD_512").to("cuda")

# Generate an image from a prompt
prompt = "An avocado armchair"
image = pipe(prompt, cfg=15)

Advanced Configuration

The pipeline can be initialized with several customization options:

python

pipe = CADT2IPipeline(
    model_path="nicolas-dufour/CAD_512",
    sampler="ddim",                    # Options: "ddim", "ddpm", "dpm", "dpm_2S", "dpm_2M"
    scheduler="sigmoid",               # Options: "sigmoid", "cosine", "linear"
    postprocessing="sd_1_5_vae",      # Options: "consistency-decoder", "sd_1_5_vae"
    scheduler_start=-3,
    scheduler_end=3,
    scheduler_tau=1.1,
    device="cuda"
)

Generation Parameters

The pipeline's __call__ method accepts various parameters to control the generation process:

python

image = pipe(
    cond="A beautiful landscape",          # Text prompt or list of prompts
    num_samples=4,                         # Number of images to generate
    cfg=15,                               # Classifier-free guidance scale
    guidance_type="constant",             # Type of guidance: "constant", "linear"
    guidance_start_step=0,                # Step to start guidance
    coherence_value=1.0,                  # Coherence value for sampling
    uncoherence_value=0.0,                # Uncoherence value for sampling
    thresholding_type="clamp",           # Type of thresholding: "clamp", "dynamic_thresholding", "per_channel_dynamic_thresholding"
    clamp_value=1.0,                      # Clamp value for thresholding
    thresholding_percentile=0.995         # Percentile for thresholding
)

Guidance Types

constant: Applies uniform guidance throughout the sampling process
linear: Linearly increases guidance strength from start to end
exponential: Exponentially increases guidance strength from start to end

Thresholding Types

clamp: Clamps values to a fixed range using clamp_value
dynamic: Dynamically adjusts thresholds based on the batch statistics
percentile: Uses percentile-based thresholding with thresholding_percentile

Advanced Parameters

For more control over the generation process, you can also specify:

x_N: Initial noise tensor
latents: Previous latents for continuation
num_steps: Custom number of sampling steps
sampler: Custom sampler function
scheduler: Custom scheduler function
guidance_start_step: Step to start guidance
generator: Random number generator for reproducibility
unconfident_prompt: Custom unconfident prompt text

Citation

If you happen to use this repo in your experiments, you can acknowledge us by citing the following paper:

bibtex

@article{dufour2024dont,
  title={Don’t drop your samples! Coherence-aware training benefits Conditional diffusion},
  author={Nicolas Dufour and Victor Besnier and Vicky Kalogeiton and David Picard},
  journal={CVPR}
  year={2024}
}