Add runtime requirements, gitignore, and fix typos

.gitignore

@@ -0,0 +1,165 @@
+# Generated by project
+outputs/
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# General MacOS
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# IDEs
+.idea/
+.vscode/

README.md

@@ -1,12 +1,34 @@
-# Stable Diffusion 2.0
+# Stable Diffusion Version 2
 ![t2i](assets/stable-samples/txt2img/768/merged-0006.png)
 ![t2i](assets/stable-samples/txt2img/768/merged-0002.png)
 ![t2i](assets/stable-samples/txt2img/768/merged-0005.png)
 
 This repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with
 new checkpoints. The following list provides an overview of all currently available models. More coming soon.
+
 ## News
-**November 2022**
+
+
+**March 24, 2023**
+
+*Stable UnCLIP 2.1*
+
+- New stable diffusion finetune (_Stable unCLIP 2.1_, [Hugging Face](https://huggingface.co/stabilityai/)) at 768x768 resolution,  based on SD2.1-768. This model allows for image variations and mixing operations as described in [*Hierarchical Text-Conditional Image Generation with CLIP Latents*](https://arxiv.org/abs/2204.06125), and, thanks to its modularity, can be combined with other models such as [KARLO](https://github.com/kakaobrain/karlo). Comes in two variants: [*Stable unCLIP-L*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-l.ckpt) and [*Stable unCLIP-H*](https://huggingface.co/stabilityai/stable-diffusion-2-1-unclip/blob/main/sd21-unclip-h.ckpt), which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available [here](doc/UNCLIP.MD).
+
+- A public demo of SD-unCLIP is already available at [clipdrop.co/stable-diffusion-reimagine](https://clipdrop.co/stable-diffusion-reimagine)
+
+
+**December 7, 2022**
+
+*Version 2.1*
+
+- New stable diffusion model (_Stable Diffusion 2.1-v_, [Hugging Face](https://huggingface.co/stabilityai/stable-diffusion-2-1)) at 768x768 resolution and (_Stable Diffusion 2.1-base_, [HuggingFace](https://huggingface.co/stabilityai/stable-diffusion-2-1-base)) at 512x512 resolution, both based on the same number of parameters and architecture as 2.0 and fine-tuned on 2.0, on a less restrictive NSFW filtering of the [LAION-5B](https://laion.ai/blog/laion-5b/) dataset.
+Per default, the attention operation of the model is evaluated at full precision when `xformers` is not installed. To enable fp16 (which can cause numerical instabilities with the vanilla attention module on the v2.1 model) , run your script with `ATTN_PRECISION=fp16 python <thescript.py>`
+
+**November 24, 2022**
+
+*Version 2.0*
+
 - New stable diffusion model (_Stable Diffusion 2.0-v_) at 768x768 resolution. Same number of parameters in the U-Net as 1.5, but uses [OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip) as the text encoder and is trained from scratch. _SD 2.0-v_ is a so-called [v-prediction](https://arxiv.org/abs/2202.00512) model. 
 - The above model is finetuned from _SD 2.0-base_, which was trained as a standard noise-prediction model on 512x512 images and is also made available.
 - Added a [x4 upscaling latent text-guided diffusion model](#image-upscaling-with-stable-diffusion).
@@ -54,7 +76,7 @@ Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those,
 export CUDA_HOME=/usr/local/cuda-11.4
 conda install -c nvidia/label/cuda-11.4.0 cuda-nvcc
 conda install -c conda-forge gcc
-conda install -c conda-forge gxx_linux-64=9.5.0
+conda install -c conda-forge gxx_linux-64==9.5.0
 ```
 
 Then, run the following (compiling takes up to 30 min).
@@ -80,11 +102,11 @@ The weights are available via [the StabilityAI organization at Hugging Face](htt
 
 
 
-## Stable Diffusion v2.0
+## Stable Diffusion v2
 
-Stable Diffusion v2.0 refers to a specific configuration of the model
+Stable Diffusion v2 refers to a specific configuration of the model
 architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet
-and OpenCLIP ViT-H/14 text encoder for the diffusion model. The _SD 2.0-v_ model produces 768x768 px outputs. 
+and OpenCLIP ViT-H/14 text encoder for the diffusion model. The _SD 2-v_ model produces 768x768 px outputs. 
 
 Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
 5.0, 6.0, 7.0, 8.0) and 50 DDIM sampling steps show the relative improvements of the checkpoints:
@@ -97,16 +119,16 @@ Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
 ![txt2img-stable2](assets/stable-samples/txt2img/merged-0003.png)
 ![txt2img-stable2](assets/stable-samples/txt2img/merged-0001.png)
 
-Stable Diffusion 2.0 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder.
+Stable Diffusion 2 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder.
 We provide a [reference script for sampling](#reference-sampling-script).
 #### Reference Sampling Script
 
 This script incorporates an [invisible watermarking](https://github.com/ShieldMnt/invisible-watermark) of the outputs, to help viewers [identify the images as machine-generated](scripts/tests/test_watermark.py).
-We provide the configs for the _SD2.0-v_ (768px) and _SD2.0-base_ (512px) model.
+We provide the configs for the _SD2-v_ (768px) and _SD2-base_ (512px) model.
 
-First, download the weights for [_SD2.0-v_](https://huggingface.co/stabilityai/stable-diffusion-2) and [_SD2.0-base_](https://huggingface.co/stabilityai/stable-diffusion-2-base). 
+First, download the weights for [_SD2.1-v_](https://huggingface.co/stabilityai/stable-diffusion-2-1) and [_SD2.1-base_](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). 
 
-To sample from the _SD2.0-v_ model, run the following:
+To sample from the _SD2.1-v_ model, run the following:
 
 ```
 python scripts/txt2img.py --prompt "a professional photograph of an astronaut riding a horse" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/v2-inference-v.yaml --H 768 --W 768  
@@ -125,6 +147,41 @@ Note: The inference config for all model versions is designed to be used with EM
 For this reason `use_ema=False` is set in the configuration, otherwise the code will try to switch from
 non-EMA to EMA weights. 
 
+#### Enable Intel® Extension for PyTorch* optimizations in Text-to-Image script
+
+If you're planning on running Text-to-Image on Intel® CPU, try to sample an image with TorchScript and Intel® Extension for PyTorch* optimizations. Intel® Extension for PyTorch* extends PyTorch by enabling up-to-date features optimizations for an extra performance boost on Intel® hardware. It can optimize memory layout of the operators to Channel Last memory format, which is generally beneficial for Intel CPUs, take advantage of the most advanced instruction set available on a machine, optimize operators and many more.
+
+**Prerequisites**
+
+Before running the script, make sure you have all needed libraries installed. (the optimization was checked on `Ubuntu 20.04`). Install [jemalloc](https://github.com/jemalloc/jemalloc), [numactl](https://linux.die.net/man/8/numactl), Intel® OpenMP and Intel® Extension for PyTorch*.
+
+```bash
+apt-get install numactl libjemalloc-dev
+pip install intel-openmp
+pip install intel_extension_for_pytorch -f https://software.intel.com/ipex-whl-stable
+```
+
+To sample from the _SD2.1-v_ model with TorchScript+IPEX optimizations, run the following. Remember to specify desired number of instances you want to run the program on ([more](https://github.com/intel/intel-extension-for-pytorch/blob/master/intel_extension_for_pytorch/cpu/launch.py#L48)).
+
+```
+MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-fp32.yaml  --H 768 --W 768 --precision full --device cpu --torchscript --ipex
+```
+
+To sample from the base model with IPEX optimizations, use
+
+```
+MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-fp32.yaml  --n_samples 1 --n_iter 4 --precision full --device cpu --torchscript --ipex
+```
+
+If you're using a CPU that supports `bfloat16`, consider sample from the model with bfloat16 enabled for a performance boost, like so
+
+```bash
+# SD2.1-v
+MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/768model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-v-bf16.yaml --H 768 --W 768 --precision full --device cpu --torchscript --ipex --bf16
+# SD2.1-base
+MALLOC_CONF=oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000 python -m intel_extension_for_pytorch.cpu.launch --ninstance <number of an instance> --enable_jemalloc scripts/txt2img.py --prompt \"a corgi is playing guitar, oil on canvas\" --ckpt <path/to/model.ckpt/> --config configs/stable-diffusion/intel/v2-inference-bf16.yaml --precision full --device cpu --torchscript --ipex --bf16
+```
+
 ### Image Modification with Stable Diffusion
 
 ![depth2img-stable2](assets/stable-samples/depth2img/merged-0000.png)
@@ -152,7 +209,7 @@ and the diffusion model is then conditioned on the (relative) depth output.
 
 <p align="center">
 <b> depth2image </b><br/>
-<img src=assets/stable-samples/depth2img/d2i.gif/>
+<img src=assets/stable-samples/depth2img/d2i.gif>
 </p>
 
 This model is particularly useful for a photorealistic style; see the [examples](assets/stable-samples/depth2img).

checkpoints/checkpoints.txt

@@ -0,0 +1 @@
+Put unCLIP checkpoints here.

configs/karlo/decoder_900M_vit_l.yaml

@@ -0,0 +1,37 @@
+model:
+  type: t2i-decoder
+  diffusion_sampler: uniform
+  hparams:
+    image_size: 64
+    num_channels: 320
+    num_res_blocks: 3
+    channel_mult: ''
+    attention_resolutions: 32,16,8
+    num_heads: -1
+    num_head_channels: 64
+    num_heads_upsample: -1
+    use_scale_shift_norm: true
+    dropout: 0.1
+    clip_dim: 768
+    clip_emb_mult: 4
+    text_ctx: 77
+    xf_width: 1536
+    xf_layers: 0
+    xf_heads: 0
+    xf_final_ln: false
+    resblock_updown: true
+    learn_sigma: true
+    text_drop: 0.3
+    clip_emb_type: image
+    clip_emb_drop: 0.1
+    use_plm: true
+
+diffusion:
+  steps: 1000
+  learn_sigma: true
+  sigma_small: false
+  noise_schedule: squaredcos_cap_v2
+  use_kl: false
+  predict_xstart: false
+  rescale_learned_sigmas: true
+  timestep_respacing: ''

configs/karlo/improved_sr_64_256_1.4B.yaml

@@ -0,0 +1,27 @@
+model:
+  type: improved_sr_64_256
+  diffusion_sampler: uniform
+  hparams:
+    channels: 320
+    depth: 3
+    channels_multiple:
+    - 1
+    - 2
+    - 3
+    - 4
+    dropout: 0.0
+
+diffusion:
+  steps: 1000
+  learn_sigma: false
+  sigma_small: true
+  noise_schedule: squaredcos_cap_v2
+  use_kl: false
+  predict_xstart: false
+  rescale_learned_sigmas: true
+  timestep_respacing: '7'
+
+
+sampling:
+  timestep_respacing: '7' # fix
+  clip_denoise: true

configs/karlo/prior_1B_vit_l.yaml

@@ -0,0 +1,21 @@
+model:
+  type: prior
+  diffusion_sampler: uniform
+  hparams:
+    text_ctx: 77
+    xf_width: 2048
+    xf_layers: 20
+    xf_heads: 32
+    xf_final_ln: true
+    text_drop: 0.2
+    clip_dim: 768
+
+diffusion:
+  steps: 1000
+  learn_sigma: false
+  sigma_small: true
+  noise_schedule: squaredcos_cap_v2
+  use_kl: false
+  predict_xstart: true
+  rescale_learned_sigmas: false
+  timestep_respacing: ''