v-diffusion-pytorch

v objective diffusion inference code for PyTorch, by Katherine Crowson (@RiversHaveWings) and Chainbreakers AI (@jd_pressman).

The models are denoising diffusion probabilistic models (https://arxiv.org/abs/2006.11239), which are trained to reverse a gradual noising process, allowing the models to generate samples from the learned data distributions starting from random noise. DDIM-style deterministic sampling (https://arxiv.org/abs/2010.02502) is also supported. The models are also trained on continuous timesteps. They use the ‘v’ objective from Progressive Distillation for Fast Sampling of Diffusion Models (https://openreview.net/forum?id=TIdIXIpzhoI).

Thank you to stability.ai for compute to train these models!

Dependencies

Model checkpoints:

  • CC12M 256×256, SHA-256 63946d1f6a1cb54b823df818c305d90a9c26611e594b5f208795864d5efe0d1f

A 602M parameter CLIP conditioned model trained on Conceptual 12M for 3.1M steps.

Sampling

Example

If the model checkpoints are stored in checkpoints/, the following will generate an image:

./clip_sample.py "the rise of consciousness" --model cc12m_1 --seed 0

If they are somewhere else, you need to specify the path to the checkpoint with --checkpoint.

CLIP conditioned/guided sampling

usage: clip_sample.py [-h] [--images [IMAGE ...]] [--batch-size BATCH_SIZE]
                      [--checkpoint CHECKPOINT] [--clip-guidance-scale CLIP_GUIDANCE_SCALE]
                      [--device DEVICE] [--eta ETA] [--model {cc12m_1}] [-n N] [--seed SEED]
                      [--steps STEPS]
                      [prompts ...]

prompts: the text prompts to use. Relative weights for text prompts can be specified by putting the weight after a colon, for example: "the rise of consciousness:0.5".

--batch-size: sample this many images at a time (default 1)

--checkpoint: manually specify the model checkpoint file

--clip-guidance-scale: how strongly the result should match the text prompt (default 500). If set to 0, the cc12m_1 model will still be CLIP conditioned and sampling will go faster and use less memory.

--device: the PyTorch device name to use (default autodetects)

--eta: set to 0 for deterministic (DDIM) sampling, 1 (the default) for stochastic (DDPM) sampling, and in between to interpolate between the two. DDIM is preferred for low numbers of timesteps.

--images: the image prompts to use (local files or HTTP(S) URLs). Relative weights for image prompts can be specified by putting the weight after a colon, for example: "image_1.png:0.5".

--model: specify the model to use (default cc12m_1)

-n: sample until this many images are sampled (default 1)

--seed: specify the random seed (default 0)

--steps: specify the number of diffusion timesteps (default is 1000, can lower for faster but lower quality sampling)

GitHub

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