Package: v-diffusion-pytorch
v-diffusion-pytorchv 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 tostability.aifor compute to train these models!Installationpip install v-diffusion-pytorchor git clone thenpip install -e .Model checkpoints:CC12M_1 CFG 256x256, SHA-2564fc95ee1b3205a3f7422a07746383776e1dbc367eaf06a5b658ad351e77b7bdaA 602M parameter CLIP conditioned model trained onConceptual 12Mfor 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-25663946d1f6a1cb54b823df818c305d90a9c26611e594b5f208795864d5efe0d1fAs above, before CFG fine-tuning. The model from the original release of this repo.YFCC_1 512x512, SHA-256a1c0f6baaf89cb4c461f691c2505e451ff1f9524744ce15332b7987cc6e3f0c8A 481M parameter unconditional model trained on a 33 million image original resolution subset ofYahoo Flickr Creative Commons 100 Million.YFCC_2 512x512, SHA-25669ad4e534feaaebfd4ccefbf03853d5834231ae1b5402b9d2c3e2b331de27907A 968M parameter unconditional model trained on a 33 million image original resolution subset ofYahoo Flickr Creative Commons 100 Million.It also contains PyTorch ports of the four models fromv-diffusion-jax,danbooru_128,imagenet_128,wikiart_128,wikiart_256:Danbooru SFW 128x128, SHA-2561728940d3531504246dbdc75748205fd8a24238a17e90feb82a64d7c8078c449ImageNet 128x128, SHA-256cac117cd0ed80390b2ae7f3d48bf226fd8ee0799d3262c13439517da7c214a67WikiArt 128x128, SHA-256b3ca8d0cf8bd47dcbf92863d0ab6e90e5be3999ab176b294c093431abdce19c1WikiArt 256x256, SHA-256da45c38aa31cd0d2680d29a3aaf2f50537a4146d80bba2ca3e7a18d227d9b627SamplingExampleIf the model checkpoint for cc12m_1_cfg is stored incheckpoints/, the following will generate four images:./cfg_sample.py "the rise of consciousness":5 -n 4 -bs 4 --seed 0If they are somewhere else, you need to specify the path to the checkpoint with--checkpoint.ColabThere is a cc12m_1_cfg Colab (a simplified version ofcfg_sample.py)here, which can be used for free.CFG sampling (best, but only cc12m_1_cfg supports it)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 ddimfor 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)CLIP guided sampling (all models)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 ddimfor 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)