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. 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). Guided diffusion sampling scripts (https://arxiv.org/abs/2105.05233) are included, specifically CLIP guided diffusion. This repo also includes a diffusion model conditioned on CLIP text embeddings that supports classifier-free guidance (https://openreview.net/pdf?id=qw8AKxfYbI), similar to GLIDE (https://arxiv.org/abs/2112.10741). Sampling methods include DDPM, DDIM (https://arxiv.org/abs/2010.02502), and PRK/PLMS (https://openreview.net/forum?id=PlKWVd2yBkY).
Thank you to stability.ai for compute to train these models!
-
PyTorch (installation instructions)
-
requests, tqdm (install with
pip install) -
CLIP (https://github.com/openai/CLIP), and its additional pip-installable dependencies: ftfy, regex. If you
git clone --recursivethis repo, it should fetch CLIP automatically.
- CC12M_1 CFG 256x256, SHA-256
4fc95ee1b3205a3f7422a07746383776e1dbc367eaf06a5b658ad351e77b7bda
A 602M parameter CLIP conditioned model trained on Conceptual 12M for 3.1M steps and then fine-tuned for classifier-free guidance for 250K additional steps. This is the recommended model to use.
- CC12M_1 256x256, SHA-256
63946d1f6a1cb54b823df818c305d90a9c26611e594b5f208795864d5efe0d1f
As above, before CFG fine-tuning. The model from the original release of this repo.
- YFCC_1 512x512, SHA-256
a1c0f6baaf89cb4c461f691c2505e451ff1f9524744ce15332b7987cc6e3f0c8
A 481M parameter unconditional model trained on a 33 million image original resolution subset of Yahoo Flickr Creative Commons 100 Million.
- YFCC_2 512x512, SHA-256
69ad4e534feaaebfd4ccefbf03853d5834231ae1b5402b9d2c3e2b331de27907
A 968M parameter unconditional model trained on a 33 million image original resolution subset of Yahoo Flickr Creative Commons 100 Million.
It also contains PyTorch ports of the four models from v-diffusion-jax, danbooru_128, imagenet_128, wikiart_128, wikiart_256:
-
Danbooru SFW 128x128, SHA-256
1728940d3531504246dbdc75748205fd8a24238a17e90feb82a64d7c8078c449 -
ImageNet 128x128, SHA-256
cac117cd0ed80390b2ae7f3d48bf226fd8ee0799d3262c13439517da7c214a67 -
WikiArt 128x128, SHA-256
b3ca8d0cf8bd47dcbf92863d0ab6e90e5be3999ab176b294c093431abdce19c1 -
WikiArt 256x256, SHA-256
da45c38aa31cd0d2680d29a3aaf2f50537a4146d80bba2ca3e7a18d227d9b627
If the model checkpoint for cc12m_1_cfg is stored in checkpoints/, the following will generate four images:
./cfg_sample.py "the rise of consciousness":5 -n 4 -bs 4 --seed 0
If they are somewhere else, you need to specify the path to the checkpoint with --checkpoint.
There is a cc12m_1_cfg Colab (a simplified version of cfg_sample.py) here, which can be used for free.
usage: cfg_sample.py [-h] [--images [IMAGE ...]] [--batch-size BATCH_SIZE]
[--checkpoint CHECKPOINT] [--device DEVICE] [--eta ETA] [--init INIT]
[--method {ddpm,ddim,prk,plms,pie,plms2,iplms}] [--model {cc12m_1_cfg}]
[-n N] [--seed SEED] [--size SIZE SIZE]
[--starting-timestep STARTING_TIMESTEP] [--steps STEPS]
[prompts ...]
prompts: the text prompts to use. Weights for text prompts can be specified by putting the weight after a colon, for example: "the rise of consciousness:5". A weight of 1 will sample images that match the prompt roughly as well as images usually match prompts like that in the training set. The default weight is 3.
--batch-size: sample this many images at a time (default 1)
--checkpoint: manually specify the model checkpoint file
--device: the PyTorch device name to use (default autodetects)
--eta: set to 0 (the default) while using --method ddim for deterministic (DDIM) sampling, 1 for stochastic (DDPM) sampling, and in between to interpolate between the two.
--images: the image prompts to use (local files or HTTP(S) URLs). Weights for image prompts can be specified by putting the weight after a colon, for example: "image_1.png:5". The default weight is 3.
--init: specify the init image (optional)
--method: specify the sampling method to use (DDPM, DDIM, PRK, PLMS, PIE, PLMS2, or IPLMS) (default PLMS). DDPM is the original SDE sampling method, DDIM integrates the probability flow ODE using a first order method, PLMS is fourth-order pseudo Adams-Bashforth, and PLMS2 is second-order pseudo Adams-Bashforth. PRK (fourth-order Pseudo Runge-Kutta) and PIE (second-order Pseudo Improved Euler) are used to bootstrap PLMS and PLMS2 but can be used on their own if you desire (slow). IPLMS is the fourth order "Improved PLMS" sampler from (Fast Sampling of Diffusion Models with Exponential Integrator)[https://arxiv.org/abs/2204.13902].
--model: specify the model to use (default cc12m_1_cfg)
-n: sample until this many images are sampled (default 1)
--seed: specify the random seed (default 0)
--starting-timestep: specify the starting timestep if an init image is used (range 0-1, default 0.9)
--size: the output image size (default auto)
--steps: specify the number of diffusion timesteps (default is 50, can be lower for faster but lower quality sampling, must be much higher with DDIM and especially DDPM)
usage: clip_sample.py [-h] [--images [IMAGE ...]] [--batch-size BATCH_SIZE]
[--checkpoint CHECKPOINT] [--clip-guidance-scale CLIP_GUIDANCE_SCALE]
[--cutn CUTN] [--cut-pow CUT_POW] [--device DEVICE] [--eta ETA]
[--init INIT] [--method {ddpm,ddim,prk,plms,pie,plms2,iplms}]
[--model {cc12m_1,cc12m_1_cfg,danbooru_128,imagenet_128,wikiart_128,wikiart_256,yfcc_1,yfcc_2}]
[-n N] [--seed SEED] [--size SIZE SIZE]
[--starting-timestep STARTING_TIMESTEP] [--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.
--cutn: the number of random crops to compute CLIP embeddings for (default 16)
--cut-pow: the random crop size power (default 1)
--device: the PyTorch device name to use (default autodetects)
--eta: set to 0 (the default) while using --method ddim for deterministic (DDIM) sampling, 1 for stochastic (DDPM) sampling, and in between to interpolate between the two.
--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".
--init: specify the init image (optional)
--method: specify the sampling method to use (DDPM, DDIM, PRK, PLMS, PIE, PLMS2, or IPLMS) (default PLMS). DDPM is the original SDE sampling method, DDIM integrates the probability flow ODE using a first order method, PLMS is fourth-order pseudo Adams-Bashforth, and PLMS2 is second-order pseudo Adams-Bashforth. PRK (fourth-order Pseudo Runge-Kutta) and PIE (second-order Pseudo Improved Euler) are used to bootstrap PLMS and PLMS2 but can be used on their own if you desire (slow). IPLMS is the fourth order "Improved PLMS" sampler from (Fast Sampling of Diffusion Models with Exponential Integrator)[https://arxiv.org/abs/2204.13902].
--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)
--starting-timestep: specify the starting timestep if an init image is used (range 0-1, default 0.9)
--size: the output image size (default auto)
--steps: specify the number of diffusion timesteps (default is 1000, can lower for faster but lower quality sampling)