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README.md

@@ -1,12 +1,292 @@
----
-title: Stable Video Diffusion
-emoji: 🐨
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.5.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Generative Models by Stability AI
+
+![sample1](assets/000.jpg)
+
+## News
+
+**November 21, 2023**
+
+- We are releasing Stable Video Diffusion, an image-to-video model, for research purposes:
+    - [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid): This model was trained to generate 14
+      frames at resolution 576x1024 given a context frame of the same size. 
+      We use the standard image encoder from SD 2.1, but replace the decoder with a temporally-aware `deflickering decoder`.
+    - [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt): Same architecture as `SVD` but finetuned
+      for 25 frame generation.
+    - We provide a streamlit demo `scripts/demo/video_sampling.py` and a standalone python script `scripts/sampling/simple_video_sample.py` for inference of both models.
+    - Alongside the model, we will release a technical report shortly. Stay tuned.
+
+  ![tile](assets/tile.gif)
+
+**July 26, 2023**
+
+- We are releasing two new open models with a
+  permissive [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0) (see [Inference](#inference) for file
+  hashes):
+    - [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0): An improved version
+      over `SDXL-base-0.9`.
+    - [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0): An improved version
+      over `SDXL-refiner-0.9`.
+
+![sample2](assets/001_with_eval.png)
+
+**July 4, 2023**
+
+- A technical report on SDXL is now available [here](https://arxiv.org/abs/2307.01952).
+
+**June 22, 2023**
+
+- We are releasing two new diffusion models for research purposes:
+    - `SDXL-base-0.9`: The base model was trained on a variety of aspect ratios on images with resolution 1024^2. The
+      base model uses [OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip)
+      and [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) for text encoding whereas the refiner model only uses
+      the OpenCLIP model.
+    - `SDXL-refiner-0.9`: The refiner has been trained to denoise small noise levels of high quality data and as such is
+      not expected to work as a text-to-image model; instead, it should only be used as an image-to-image model.
+
+If you would like to access these models for your research, please apply using one of the following links:
+[SDXL-0.9-Base model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9),
+and [SDXL-0.9-Refiner](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
+This means that you can apply for any of the two links - and if you are granted - you can access both.
+Please log in to your Hugging Face Account with your organization email to request access.
+**We plan to do a full release soon (July).**
+
+## The codebase
+
+### General Philosophy
+
+Modularity is king. This repo implements a config-driven approach where we build and combine submodules by
+calling `instantiate_from_config()` on objects defined in yaml configs. See `configs/` for many examples.
+
+### Changelog from the old `ldm` codebase
+
+For training, we use [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/), but it should be easy to use other
+training wrappers around the base modules. The core diffusion model class (formerly `LatentDiffusion`,
+now `DiffusionEngine`) has been cleaned up:
+
+- No more extensive subclassing! We now handle all types of conditioning inputs (vectors, sequences and spatial
+  conditionings, and all combinations thereof) in a single class: `GeneralConditioner`,
+  see `sgm/modules/encoders/modules.py`.
+- We separate guiders (such as classifier-free guidance, see `sgm/modules/diffusionmodules/guiders.py`) from the
+  samplers (`sgm/modules/diffusionmodules/sampling.py`), and the samplers are independent of the model.
+- We adopt the ["denoiser framework"](https://arxiv.org/abs/2206.00364) for both training and inference (most notable
+  change is probably now the option to train continuous time models):
+    * Discrete times models (denoisers) are simply a special case of continuous time models (denoisers);
+      see `sgm/modules/diffusionmodules/denoiser.py`.
+    * The following features are now independent: weighting of the diffusion loss
+      function (`sgm/modules/diffusionmodules/denoiser_weighting.py`), preconditioning of the
+      network (`sgm/modules/diffusionmodules/denoiser_scaling.py`), and sampling of noise levels during
+      training (`sgm/modules/diffusionmodules/sigma_sampling.py`).
+- Autoencoding models have also been cleaned up.
+
+## Installation:
+
+<a name="installation"></a>
+
+#### 1. Clone the repo
+
+```shell
+git clone [email protected]:Stability-AI/generative-models.git
+cd generative-models
+```
+
+#### 2. Setting up the virtualenv
+
+This is assuming you have navigated to the `generative-models` root after cloning it.
+
+**NOTE:** This is tested under `python3.10`. For other python versions, you might encounter version conflicts.
+
+**PyTorch 2.0**
+
+```shell
+# install required packages from pypi
+python3 -m venv .pt2
+source .pt2/bin/activate
+pip3 install -r requirements/pt2.txt
+```
+
+#### 3. Install `sgm`
+
+```shell
+pip3 install .
+```
+
+#### 4. Install `sdata` for training
+
+```shell
+pip3 install -e git+https://github.com/Stability-AI/datapipelines.git@main#egg=sdata
+```
+
+## Packaging
+
+This repository uses PEP 517 compliant packaging using [Hatch](https://hatch.pypa.io/latest/).
+
+To build a distributable wheel, install `hatch` and run `hatch build`
+(specifying `-t wheel` will skip building a sdist, which is not necessary).
+
+```
+pip install hatch
+hatch build -t wheel
+```
+
+You will find the built package in `dist/`. You can install the wheel with `pip install dist/*.whl`.
+
+Note that the package does **not** currently specify dependencies; you will need to install the required packages,
+depending on your use case and PyTorch version, manually.
+
+## Inference
+
+We provide a [streamlit](https://streamlit.io/) demo for text-to-image and image-to-image sampling
+in `scripts/demo/sampling.py`.
+We provide file hashes for the complete file as well as for only the saved tensors in the file (
+see [Model Spec](https://github.com/Stability-AI/ModelSpec) for a script to evaluate that).
+The following models are currently supported:
+
+- [SDXL-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
+  ```
+  File Hash (sha256): 31e35c80fc4829d14f90153f4c74cd59c90b779f6afe05a74cd6120b893f7e5b
+  Tensordata Hash (sha256): 0xd7a9105a900fd52748f20725fe52fe52b507fd36bee4fc107b1550a26e6ee1d7
+  ```
+- [SDXL-refiner-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0)
+  ```
+  File Hash (sha256): 7440042bbdc8a24813002c09b6b69b64dc90fded4472613437b7f55f9b7d9c5f
+  Tensordata Hash (sha256): 0x1a77d21bebc4b4de78c474a90cb74dc0d2217caf4061971dbfa75ad406b75d81
+  ```
+- [SDXL-base-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9)
+- [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9)
+- [SD-2.1-512](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/blob/main/v2-1_512-ema-pruned.safetensors)
+- [SD-2.1-768](https://huggingface.co/stabilityai/stable-diffusion-2-1/blob/main/v2-1_768-ema-pruned.safetensors)
+
+**Weights for SDXL**:
+
+**SDXL-1.0:**
+The weights of SDXL-1.0 are available (subject to
+a [`CreativeML Open RAIL++-M` license](model_licenses/LICENSE-SDXL1.0)) here:
+
+- base model: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/
+- refiner model: https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/
+
+**SDXL-0.9:**
+The weights of SDXL-0.9 are available and subject to a [research license](model_licenses/LICENSE-SDXL0.9).
+If you would like to access these models for your research, please apply using one of the following links:
+[SDXL-base-0.9 model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-0.9),
+and [SDXL-refiner-0.9](https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-0.9).
+This means that you can apply for any of the two links - and if you are granted - you can access both.
+Please log in to your Hugging Face Account with your organization email to request access.
+
+After obtaining the weights, place them into `checkpoints/`.
+Next, start the demo using
+
+```
+streamlit run scripts/demo/sampling.py --server.port <your_port>
+```
+
+### Invisible Watermark Detection
+
+Images generated with our code use the
+[invisible-watermark](https://github.com/ShieldMnt/invisible-watermark/)
+library to embed an invisible watermark into the model output. We also provide
+a script to easily detect that watermark. Please note that this watermark is
+not the same as in previous Stable Diffusion 1.x/2.x versions.
+
+To run the script you need to either have a working installation as above or
+try an _experimental_ import using only a minimal amount of packages:
+
+```bash
+python -m venv .detect
+source .detect/bin/activate
+
+pip install "numpy>=1.17" "PyWavelets>=1.1.1" "opencv-python>=4.1.0.25"
+pip install --no-deps invisible-watermark
+```
+
+To run the script you need to have a working installation as above. The script
+is then useable in the following ways (don't forget to activate your
+virtual environment beforehand, e.g. `source .pt1/bin/activate`):
+
+```bash
+# test a single file
+python scripts/demo/detect.py <your filename here>
+# test multiple files at once
+python scripts/demo/detect.py <filename 1> <filename 2> ... <filename n>
+# test all files in a specific folder
+python scripts/demo/detect.py <your folder name here>/*
+```
+
+## Training:
+
+We are providing example training configs in `configs/example_training`. To launch a training, run
+
+```
+python main.py --base configs/<config1.yaml> configs/<config2.yaml>
+```
+
+where configs are merged from left to right (later configs overwrite the same values).
+This can be used to combine model, training and data configs. However, all of them can also be
+defined in a single config. For example, to run a class-conditional pixel-based diffusion model training on MNIST,
+run
+
+```bash
+python main.py --base configs/example_training/toy/mnist_cond.yaml
+```
+
+**NOTE 1:** Using the non-toy-dataset
+configs `configs/example_training/imagenet-f8_cond.yaml`, `configs/example_training/txt2img-clipl.yaml`
+and `configs/example_training/txt2img-clipl-legacy-ucg-training.yaml` for training will require edits depending on the
+used dataset (which is expected to stored in tar-file in
+the [webdataset-format](https://github.com/webdataset/webdataset)). To find the parts which have to be adapted, search
+for comments containing `USER:` in the respective config.
+
+**NOTE 2:** This repository supports both `pytorch1.13` and `pytorch2`for training generative models. However for
+autoencoder training as e.g. in `configs/example_training/autoencoder/kl-f4/imagenet-attnfree-logvar.yaml`,
+only `pytorch1.13` is supported.
+
+**NOTE 3:** Training latent generative models (as e.g. in `configs/example_training/imagenet-f8_cond.yaml`) requires
+retrieving the checkpoint from [Hugging Face](https://huggingface.co/stabilityai/sdxl-vae/tree/main) and replacing
+the `CKPT_PATH` placeholder in [this line](configs/example_training/imagenet-f8_cond.yaml#81). The same is to be done
+for the provided text-to-image configs.
+
+### Building New Diffusion Models
+
+#### Conditioner
+
+The `GeneralConditioner` is configured through the `conditioner_config`. Its only attribute is `emb_models`, a list of
+different embedders (all inherited from `AbstractEmbModel`) that are used to condition the generative model.
+All embedders should define whether or not they are trainable (`is_trainable`, default `False`), a classifier-free
+guidance dropout rate is used (`ucg_rate`, default `0`), and an input key (`input_key`), for example, `txt` for
+text-conditioning or `cls` for class-conditioning.
+When computing conditionings, the embedder will get `batch[input_key]` as input.
+We currently support two to four dimensional conditionings and conditionings of different embedders are concatenated
+appropriately.
+Note that the order of the embedders in the `conditioner_config` is important.
+
+#### Network
+
+The neural network is set through the `network_config`. This used to be called `unet_config`, which is not general
+enough as we plan to experiment with transformer-based diffusion backbones.
+
+#### Loss
+
+The loss is configured through `loss_config`. For standard diffusion model training, you will have to
+set `sigma_sampler_config`.
+
+#### Sampler config
+
+As discussed above, the sampler is independent of the model. In the `sampler_config`, we set the type of numerical
+solver, number of steps, type of discretization, as well as, for example, guidance wrappers for classifier-free
+guidance.
+
+### Dataset Handling
+
+For large scale training we recommend using the data pipelines from
+our [data pipelines](https://github.com/Stability-AI/datapipelines) project. The project is contained in the requirement
+and automatically included when following the steps from the [Installation section](#installation).
+Small map-style datasets should be defined here in the repository (e.g., MNIST, CIFAR-10, ...), and return a dict of
+data keys/values,
+e.g.,
+
+```python
+example = {"jpg": x,  # this is a tensor -1...1 chw
+           "txt": "a beautiful image"}
+```
+
+where we expect images in -1...1, channel-first format.

configs/inference/sd_2_1.yaml

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+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+            params:
+              freeze: true
+              layer: penultimate
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

configs/inference/sd_2_1_768.yaml

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+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+            params:
+              freeze: true
+              layer: penultimate
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

configs/inference/sd_xl_base.yaml

@@ -0,0 +1,93 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2816
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: [1, 2, 10]
+        context_dim: 2048
+        spatial_transformer_attn_type: softmax-xformers
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
+            params:
+              layer: hidden
+              layer_idx: 11
+
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+              legacy: False
+
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+          - is_trainable: False
+            input_key: target_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

configs/inference/sd_xl_refiner.yaml

@@ -0,0 +1,86 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.13025
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
+      params:
+        num_idx: 1000
+
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
+
+    network_config:
+      target: sgm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        adm_in_channels: 2560
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 4
+        out_channels: 4
+        model_channels: 384
+        attention_resolutions: [4, 2]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 4
+        context_dim: [1280, 1280, 1280, 1280]
+        spatial_transformer_attn_type: softmax-xformers
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+          - is_trainable: False
+            input_key: txt
+            target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
+            params:
+              arch: ViT-bigG-14
+              version: laion2b_s39b_b160k
+              legacy: False
+              freeze: True
+              layer: penultimate
+              always_return_pooled: True
+
+          - is_trainable: False
+            input_key: original_size_as_tuple
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+          - is_trainable: False
+            input_key: crop_coords_top_left
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+          - is_trainable: False
+            input_key: aesthetic_score
+            target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+            params:
+              outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

configs/inference/svd.yaml

@@ -0,0 +1,131 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]

configs/inference/svd_image_decoder.yaml

@@ -0,0 +1,114 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity

requirements/pt2.txt

@@ -0,0 +1,39 @@
+black==23.7.0
+chardet==5.1.0
+clip @ git+https://github.com/openai/CLIP.git
+einops>=0.6.1
+fairscale>=0.4.13
+fire>=0.5.0
+fsspec>=2023.6.0
+invisible-watermark>=0.2.0
+kornia==0.6.9
+matplotlib>=3.7.2
+natsort>=8.4.0
+ninja>=1.11.1
+numpy>=1.24.4
+omegaconf>=2.3.0
+open-clip-torch>=2.20.0
+opencv-python==4.6.0.66
+pandas>=2.0.3
+pillow>=9.5.0
+pudb>=2022.1.3
+pytorch-lightning==2.0.1
+pyyaml>=6.0.1
+scipy>=1.10.1
+streamlit>=0.73.1
+tensorboardx==2.6
+timm>=0.9.2
+tokenizers==0.12.1
+torch>=2.0.1
+torchaudio>=2.0.2
+torchdata==0.6.1
+torchmetrics>=1.0.1
+torchvision>=0.15.2
+tqdm>=4.65.0
+transformers==4.19.1
+triton==2.0.0
+urllib3<1.27,>=1.25.4
+wandb>=0.15.6
+webdataset>=0.2.33
+wheel>=0.41.0
+xformers>=0.0.20

scripts/demo/detect.py

@@ -0,0 +1,156 @@
+import argparse
+
+import cv2
+import numpy as np
+
+try:
+    from imwatermark import WatermarkDecoder
+except ImportError as e:
+    try:
+        # Assume some of the other dependencies such as torch are not fulfilled
+        # import file without loading unnecessary libraries.
+        import importlib.util
+        import sys
+
+        spec = importlib.util.find_spec("imwatermark.maxDct")
+        assert spec is not None
+        maxDct = importlib.util.module_from_spec(spec)
+        sys.modules["maxDct"] = maxDct
+        spec.loader.exec_module(maxDct)
+
+        class WatermarkDecoder(object):
+            """A minimal version of
+            https://github.com/ShieldMnt/invisible-watermark/blob/main/imwatermark/watermark.py
+            to only reconstruct bits using dwtDct"""
+
+            def __init__(self, wm_type="bytes", length=0):
+                assert wm_type == "bits", "Only bits defined in minimal import"
+                self._wmType = wm_type
+                self._wmLen = length
+
+            def reconstruct(self, bits):
+                if len(bits) != self._wmLen:
+                    raise RuntimeError("bits are not matched with watermark length")
+
+                return bits
+
+            def decode(self, cv2Image, method="dwtDct", **configs):
+                (r, c, channels) = cv2Image.shape
+                if r * c < 256 * 256:
+                    raise RuntimeError("image too small, should be larger than 256x256")
+
+                bits = []
+                assert method == "dwtDct"
+                embed = maxDct.EmbedMaxDct(watermarks=[], wmLen=self._wmLen, **configs)
+                bits = embed.decode(cv2Image)
+                return self.reconstruct(bits)
+
+    except:
+        raise e
+
+
+# A fixed 48-bit message that was choosen at random
+# WATERMARK_MESSAGE = 0xB3EC907BB19E
+WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
+# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
+WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
+MATCH_VALUES = [
+    [27, "No watermark detected"],
+    [33, "Partial watermark match. Cannot determine with certainty."],
+    [
+        35,
+        (
+            "Likely watermarked. In our test 0.02% of real images were "
+            'falsely detected as "Likely watermarked"'
+        ),
+    ],
+    [
+        49,
+        (
+            "Very likely watermarked. In our test no real images were "
+            'falsely detected as "Very likely watermarked"'
+        ),
+    ],
+]
+
+
+class GetWatermarkMatch:
+    def __init__(self, watermark):
+        self.watermark = watermark
+        self.num_bits = len(self.watermark)
+        self.decoder = WatermarkDecoder("bits", self.num_bits)
+
+    def __call__(self, x: np.ndarray) -> np.ndarray:
+        """
+        Detects the number of matching bits the predefined watermark with one
+        or multiple images. Images should be in cv2 format, e.g. h x w x c BGR.
+
+        Args:
+            x: ([B], h w, c) in range [0, 255]
+
+        Returns:
+           number of matched bits ([B],)
+        """
+        squeeze = len(x.shape) == 3
+        if squeeze:
+            x = x[None, ...]
+
+        bs = x.shape[0]
+        detected = np.empty((bs, self.num_bits), dtype=bool)
+        for k in range(bs):
+            detected[k] = self.decoder.decode(x[k], "dwtDct")
+        result = np.sum(detected == self.watermark, axis=-1)
+        if squeeze:
+            return result[0]
+        else:
+            return result
+
+
+get_watermark_match = GetWatermarkMatch(WATERMARK_BITS)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "filename",
+        nargs="+",
+        type=str,
+        help="Image files to check for watermarks",
+    )
+    opts = parser.parse_args()
+
+    print(
+        """
+        This script tries to detect watermarked images. Please be aware of
+        the following:
+        - As the watermark is supposed to be invisible, there is the risk that
+          watermarked images may not be detected.
+        - To maximize the chance of detection make sure that the image has the same
+          dimensions as when the watermark was applied (most likely 1024x1024
+          or 512x512).
+        - Specific image manipulation may drastically decrease the chance that
+          watermarks can be detected.
+        - There is also the chance that an image has the characteristics of the
+          watermark by chance.
+        - The watermark script is public, anybody may watermark any images, and
+          could therefore claim it to be generated.
+        - All numbers below are based on a test using 10,000 images without any
+          modifications after applying the watermark.
+        """
+    )
+
+    for fn in opts.filename:
+        image = cv2.imread(fn)
+        if image is None:
+            print(f"Couldn't read {fn}. Skipping")
+            continue
+
+        num_bits = get_watermark_match(image)
+        k = 0
+        while num_bits > MATCH_VALUES[k][0]:
+            k += 1
+        print(
+            f"{fn}: {MATCH_VALUES[k][1]}",
+            f"Bits that matched the watermark {num_bits} from {len(WATERMARK_BITS)}\n",
+            sep="\n\t",
+        )

scripts/demo/discretization.py

@@ -0,0 +1,59 @@
+import torch
+
+from sgm.modules.diffusionmodules.discretizer import Discretization
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization: Discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+class Txt2NoisyDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 0.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(
+        self, discretization: Discretization, strength: float = 0.0, original_steps=None
+    ):
+        self.discretization = discretization
+        self.strength = strength
+        self.original_steps = original_steps
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        if self.original_steps is None:
+            steps = len(sigmas)
+        else:
+            steps = self.original_steps + 1
+        prune_index = max(min(int(self.strength * steps) - 1, steps - 1), 0)
+        sigmas = sigmas[prune_index:]
+        print("prune index:", prune_index)
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas

scripts/demo/sampling.py

@@ -0,0 +1,364 @@
+from pytorch_lightning import seed_everything
+
+from scripts.demo.streamlit_helpers import *
+
+SAVE_PATH = "outputs/demo/txt2img/"
+
+SD_XL_BASE_RATIOS = {
+    "0.5": (704, 1408),
+    "0.52": (704, 1344),
+    "0.57": (768, 1344),
+    "0.6": (768, 1280),
+    "0.68": (832, 1216),
+    "0.72": (832, 1152),
+    "0.78": (896, 1152),
+    "0.82": (896, 1088),
+    "0.88": (960, 1088),
+    "0.94": (960, 1024),
+    "1.0": (1024, 1024),
+    "1.07": (1024, 960),
+    "1.13": (1088, 960),
+    "1.21": (1088, 896),
+    "1.29": (1152, 896),
+    "1.38": (1152, 832),
+    "1.46": (1216, 832),
+    "1.67": (1280, 768),
+    "1.75": (1344, 768),
+    "1.91": (1344, 704),
+    "2.0": (1408, 704),
+    "2.09": (1472, 704),
+    "2.4": (1536, 640),
+    "2.5": (1600, 640),
+    "2.89": (1664, 576),
+    "3.0": (1728, 576),
+}
+
+VERSION2SPECS = {
+    "SDXL-base-1.0": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_base_1.0.safetensors",
+    },
+    "SDXL-base-0.9": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": False,
+        "config": "configs/inference/sd_xl_base.yaml",
+        "ckpt": "checkpoints/sd_xl_base_0.9.safetensors",
+    },
+    "SD-2.1": {
+        "H": 512,
+        "W": 512,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_2_1.yaml",
+        "ckpt": "checkpoints/v2-1_512-ema-pruned.safetensors",
+    },
+    "SD-2.1-768": {
+        "H": 768,
+        "W": 768,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_2_1_768.yaml",
+        "ckpt": "checkpoints/v2-1_768-ema-pruned.safetensors",
+    },
+    "SDXL-refiner-0.9": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_xl_refiner.yaml",
+        "ckpt": "checkpoints/sd_xl_refiner_0.9.safetensors",
+    },
+    "SDXL-refiner-1.0": {
+        "H": 1024,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "is_legacy": True,
+        "config": "configs/inference/sd_xl_refiner.yaml",
+        "ckpt": "checkpoints/sd_xl_refiner_1.0.safetensors",
+    },
+}
+
+
+def load_img(display=True, key=None, device="cuda"):
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+    width, height = map(
+        lambda x: x - x % 64, (w, h)
+    )  # resize to integer multiple of 64
+    image = image.resize((width, height))
+    image = np.array(image.convert("RGB"))
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+    return image.to(device)
+
+
+def run_txt2img(
+    state,
+    version,
+    version_dict,
+    is_legacy=False,
+    return_latents=False,
+    filter=None,
+    stage2strength=None,
+):
+    if version.startswith("SDXL-base"):
+        W, H = st.selectbox("Resolution:", list(SD_XL_BASE_RATIOS.values()), 10)
+    else:
+        H = st.number_input("H", value=version_dict["H"], min_value=64, max_value=2048)
+        W = st.number_input("W", value=version_dict["W"], min_value=64, max_value=2048)
+    C = version_dict["C"]
+    F = version_dict["f"]
+
+    init_dict = {
+        "orig_width": W,
+        "orig_height": H,
+        "target_width": W,
+        "target_height": H,
+    }
+    value_dict = init_embedder_options(
+        get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
+        init_dict,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+    )
+    sampler, num_rows, num_cols = init_sampling(stage2strength=stage2strength)
+    num_samples = num_rows * num_cols
+
+    if st.button("Sample"):
+        st.write(f"**Model I:** {version}")
+        out = do_sample(
+            state["model"],
+            sampler,
+            value_dict,
+            num_samples,
+            H,
+            W,
+            C,
+            F,
+            force_uc_zero_embeddings=["txt"] if not is_legacy else [],
+            return_latents=return_latents,
+            filter=filter,
+        )
+        return out
+
+
+def run_img2img(
+    state,
+    version_dict,
+    is_legacy=False,
+    return_latents=False,
+    filter=None,
+    stage2strength=None,
+):
+    img = load_img()
+    if img is None:
+        return None
+    H, W = img.shape[2], img.shape[3]
+
+    init_dict = {
+        "orig_width": W,
+        "orig_height": H,
+        "target_width": W,
+        "target_height": H,
+    }
+    value_dict = init_embedder_options(
+        get_unique_embedder_keys_from_conditioner(state["model"].conditioner),
+        init_dict,
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+    )
+    strength = st.number_input(
+        "**Img2Img Strength**", value=0.75, min_value=0.0, max_value=1.0
+    )
+    sampler, num_rows, num_cols = init_sampling(
+        img2img_strength=strength,
+        stage2strength=stage2strength,
+    )
+    num_samples = num_rows * num_cols
+
+    if st.button("Sample"):
+        out = do_img2img(
+            repeat(img, "1 ... -> n ...", n=num_samples),
+            state["model"],
+            sampler,
+            value_dict,
+            num_samples,
+            force_uc_zero_embeddings=["txt"] if not is_legacy else [],
+            return_latents=return_latents,
+            filter=filter,
+        )
+        return out
+
+
+def apply_refiner(
+    input,
+    state,
+    sampler,
+    num_samples,
+    prompt,
+    negative_prompt,
+    filter=None,
+    finish_denoising=False,
+):
+    init_dict = {
+        "orig_width": input.shape[3] * 8,
+        "orig_height": input.shape[2] * 8,
+        "target_width": input.shape[3] * 8,
+        "target_height": input.shape[2] * 8,
+    }
+
+    value_dict = init_dict
+    value_dict["prompt"] = prompt
+    value_dict["negative_prompt"] = negative_prompt
+
+    value_dict["crop_coords_top"] = 0
+    value_dict["crop_coords_left"] = 0
+
+    value_dict["aesthetic_score"] = 6.0
+    value_dict["negative_aesthetic_score"] = 2.5
+
+    st.warning(f"refiner input shape: {input.shape}")
+    samples = do_img2img(
+        input,
+        state["model"],
+        sampler,
+        value_dict,
+        num_samples,
+        skip_encode=True,
+        filter=filter,
+        add_noise=not finish_denoising,
+    )
+
+    return samples
+
+
+if __name__ == "__main__":
+    st.title("Stable Diffusion")
+    version = st.selectbox("Model Version", list(VERSION2SPECS.keys()), 0)
+    version_dict = VERSION2SPECS[version]
+    if st.checkbox("Load Model"):
+        mode = st.radio("Mode", ("txt2img", "img2img"), 0)
+    else:
+        mode = "skip"
+    st.write("__________________________")
+
+    set_lowvram_mode(st.checkbox("Low vram mode", True))
+
+    if version.startswith("SDXL-base"):
+        add_pipeline = st.checkbox("Load SDXL-refiner?", False)
+        st.write("__________________________")
+    else:
+        add_pipeline = False
+
+    seed = st.sidebar.number_input("seed", value=42, min_value=0, max_value=int(1e9))
+    seed_everything(seed)
+
+    save_locally, save_path = init_save_locally(os.path.join(SAVE_PATH, version))
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+
+    is_legacy = version_dict["is_legacy"]
+
+    prompt = st.text_input(
+        "prompt",
+        "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    )
+    if is_legacy:
+        negative_prompt = st.text_input("negative prompt", "")
+    else:
+        negative_prompt = ""  # which is unused
+
+    stage2strength = None
+    finish_denoising = False
+
+    if add_pipeline:
+        st.write("__________________________")
+        version2 = st.selectbox("Refiner:", ["SDXL-refiner-1.0", "SDXL-refiner-0.9"])
+        st.warning(
+            f"Running with {version2} as the second stage model. Make sure to provide (V)RAM :) "
+        )
+        st.write("**Refiner Options:**")
+
+        version_dict2 = VERSION2SPECS[version2]
+        state2 = init_st(version_dict2, load_filter=False)
+        st.info(state2["msg"])
+
+        stage2strength = st.number_input(
+            "**Refinement strength**", value=0.15, min_value=0.0, max_value=1.0
+        )
+
+        sampler2, *_ = init_sampling(
+            key=2,
+            img2img_strength=stage2strength,
+            specify_num_samples=False,
+        )
+        st.write("__________________________")
+        finish_denoising = st.checkbox("Finish denoising with refiner.", True)
+        if not finish_denoising:
+            stage2strength = None
+
+    if mode == "txt2img":
+        out = run_txt2img(
+            state,
+            version,
+            version_dict,
+            is_legacy=is_legacy,
+            return_latents=add_pipeline,
+            filter=state.get("filter"),
+            stage2strength=stage2strength,
+        )
+    elif mode == "img2img":
+        out = run_img2img(
+            state,
+            version_dict,
+            is_legacy=is_legacy,
+            return_latents=add_pipeline,
+            filter=state.get("filter"),
+            stage2strength=stage2strength,
+        )
+    elif mode == "skip":
+        out = None
+    else:
+        raise ValueError(f"unknown mode {mode}")
+    if isinstance(out, (tuple, list)):
+        samples, samples_z = out
+    else:
+        samples = out
+        samples_z = None
+
+    if add_pipeline and samples_z is not None:
+        st.write("**Running Refinement Stage**")
+        samples = apply_refiner(
+            samples_z,
+            state2,
+            sampler2,
+            samples_z.shape[0],
+            prompt=prompt,
+            negative_prompt=negative_prompt if is_legacy else "",
+            filter=state.get("filter"),
+            finish_denoising=finish_denoising,
+        )
+
+    if save_locally and samples is not None:
+        perform_save_locally(save_path, samples)

scripts/demo/streamlit_helpers.py

@@ -0,0 +1,928 @@
+import copy
+import math
+import os
+from glob import glob
+from typing import Dict, List, Optional, Tuple, Union
+
+import cv2
+import numpy as np
+import streamlit as st
+import torch
+import torch.nn as nn
+import torchvision.transforms as TT
+from einops import rearrange, repeat
+from imwatermark import WatermarkEncoder
+from omegaconf import ListConfig, OmegaConf
+from PIL import Image
+from safetensors.torch import load_file as load_safetensors
+from torch import autocast
+from torchvision import transforms
+from torchvision.utils import make_grid, save_image
+
+from scripts.demo.discretization import (Img2ImgDiscretizationWrapper,
+                                         Txt2NoisyDiscretizationWrapper)
+from scripts.util.detection.nsfw_and_watermark_dectection import \
+    DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.modules.diffusionmodules.guiders import (LinearPredictionGuider,
+                                                  VanillaCFG)
+from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
+                                                   DPMPP2SAncestralSampler,
+                                                   EulerAncestralSampler,
+                                                   EulerEDMSampler,
+                                                   HeunEDMSampler,
+                                                   LinearMultistepSampler)
+from sgm.util import append_dims, default, instantiate_from_config
+
+
+@st.cache_resource()
+def init_st(version_dict, load_ckpt=True, load_filter=True):
+    state = dict()
+    if not "model" in state:
+        config = version_dict["config"]
+        ckpt = version_dict["ckpt"]
+
+        config = OmegaConf.load(config)
+        model, msg = load_model_from_config(config, ckpt if load_ckpt else None)
+
+        state["msg"] = msg
+        state["model"] = model
+        state["ckpt"] = ckpt if load_ckpt else None
+        state["config"] = config
+        if load_filter:
+            state["filter"] = DeepFloydDataFiltering(verbose=False)
+    return state
+
+
+def load_model(model):
+    model.cuda()
+
+
+lowvram_mode = False
+
+
+def set_lowvram_mode(mode):
+    global lowvram_mode
+    lowvram_mode = mode
+
+
+def initial_model_load(model):
+    global lowvram_mode
+    if lowvram_mode:
+        model.model.half()
+    else:
+        model.cuda()
+    return model
+
+
+def unload_model(model):
+    global lowvram_mode
+    if lowvram_mode:
+        model.cpu()
+        torch.cuda.empty_cache()
+
+
+def load_model_from_config(config, ckpt=None, verbose=True):
+    model = instantiate_from_config(config.model)
+
+    if ckpt is not None:
+        print(f"Loading model from {ckpt}")
+        if ckpt.endswith("ckpt"):
+            pl_sd = torch.load(ckpt, map_location="cpu")
+            if "global_step" in pl_sd:
+                global_step = pl_sd["global_step"]
+                st.info(f"loaded ckpt from global step {global_step}")
+                print(f"Global Step: {pl_sd['global_step']}")
+            sd = pl_sd["state_dict"]
+        elif ckpt.endswith("safetensors"):
+            sd = load_safetensors(ckpt)
+        else:
+            raise NotImplementedError
+
+        msg = None
+
+        m, u = model.load_state_dict(sd, strict=False)
+
+        if len(m) > 0 and verbose:
+            print("missing keys:")
+            print(m)
+        if len(u) > 0 and verbose:
+            print("unexpected keys:")
+            print(u)
+    else:
+        msg = None
+
+    model = initial_model_load(model)
+    model.eval()
+    return model, msg
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list(set([x.input_key for x in conditioner.embedders]))
+
+
+def init_embedder_options(keys, init_dict, prompt=None, negative_prompt=None):
+    # Hardcoded demo settings; might undergo some changes in the future
+
+    value_dict = {}
+    for key in keys:
+        if key == "txt":
+            if prompt is None:
+                prompt = "A professional photograph of an astronaut riding a pig"
+            if negative_prompt is None:
+                negative_prompt = ""
+
+            prompt = st.text_input("Prompt", prompt)
+            negative_prompt = st.text_input("Negative prompt", negative_prompt)
+
+            value_dict["prompt"] = prompt
+            value_dict["negative_prompt"] = negative_prompt
+
+        if key == "original_size_as_tuple":
+            orig_width = st.number_input(
+                "orig_width",
+                value=init_dict["orig_width"],
+                min_value=16,
+            )
+            orig_height = st.number_input(
+                "orig_height",
+                value=init_dict["orig_height"],
+                min_value=16,
+            )
+
+            value_dict["orig_width"] = orig_width
+            value_dict["orig_height"] = orig_height
+
+        if key == "crop_coords_top_left":
+            crop_coord_top = st.number_input("crop_coords_top", value=0, min_value=0)
+            crop_coord_left = st.number_input("crop_coords_left", value=0, min_value=0)
+
+            value_dict["crop_coords_top"] = crop_coord_top
+            value_dict["crop_coords_left"] = crop_coord_left
+
+        if key == "aesthetic_score":
+            value_dict["aesthetic_score"] = 6.0
+            value_dict["negative_aesthetic_score"] = 2.5
+
+        if key == "target_size_as_tuple":
+            value_dict["target_width"] = init_dict["target_width"]
+            value_dict["target_height"] = init_dict["target_height"]
+
+        if key in ["fps_id", "fps"]:
+            fps = st.number_input("fps", value=6, min_value=1)
+
+            value_dict["fps"] = fps
+            value_dict["fps_id"] = fps - 1
+
+        if key == "motion_bucket_id":
+            mb_id = st.number_input("motion bucket id", 0, 511, value=127)
+            value_dict["motion_bucket_id"] = mb_id
+
+        if key == "pool_image":
+            st.text("Image for pool conditioning")
+            image = load_img(
+                key="pool_image_input",
+                size=224,
+                center_crop=True,
+            )
+            if image is None:
+                st.info("Need an image here")
+                image = torch.zeros(1, 3, 224, 224)
+            value_dict["pool_image"] = image
+
+    return value_dict
+
+
+def perform_save_locally(save_path, samples):
+    os.makedirs(os.path.join(save_path), exist_ok=True)
+    base_count = len(os.listdir(os.path.join(save_path)))
+    samples = embed_watermark(samples)
+    for sample in samples:
+        sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
+        Image.fromarray(sample.astype(np.uint8)).save(
+            os.path.join(save_path, f"{base_count:09}.png")
+        )
+        base_count += 1
+
+
+def init_save_locally(_dir, init_value: bool = False):
+    save_locally = st.sidebar.checkbox("Save images locally", value=init_value)
+    if save_locally:
+        save_path = st.text_input("Save path", value=os.path.join(_dir, "samples"))
+    else:
+        save_path = None
+
+    return save_locally, save_path
+
+
+def get_guider(options, key):
+    guider = st.sidebar.selectbox(
+        f"Discretization #{key}",
+        [
+            "VanillaCFG",
+            "IdentityGuider",
+            "LinearPredictionGuider",
+        ],
+        options.get("guider", 0),
+    )
+
+    additional_guider_kwargs = options.pop("additional_guider_kwargs", {})
+
+    if guider == "IdentityGuider":
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
+        }
+    elif guider == "VanillaCFG":
+        scale_schedule = st.sidebar.selectbox(
+            f"Scale schedule #{key}",
+            ["Identity", "Oscillating"],
+        )
+
+        if scale_schedule == "Identity":
+            scale = st.number_input(
+                f"cfg-scale #{key}",
+                value=options.get("cfg", 5.0),
+                min_value=0.0,
+            )
+
+            scale_schedule_config = {
+                "target": "sgm.modules.diffusionmodules.guiders.IdentitySchedule",
+                "params": {"scale": scale},
+            }
+
+        elif scale_schedule == "Oscillating":
+            small_scale = st.number_input(
+                f"small cfg-scale #{key}",
+                value=4.0,
+                min_value=0.0,
+            )
+
+            large_scale = st.number_input(
+                f"large cfg-scale #{key}",
+                value=16.0,
+                min_value=0.0,
+            )
+
+            sigma_cutoff = st.number_input(
+                f"sigma cutoff #{key}",
+                value=1.0,
+                min_value=0.0,
+            )
+
+            scale_schedule_config = {
+                "target": "sgm.modules.diffusionmodules.guiders.OscillatingSchedule",
+                "params": {
+                    "small_scale": small_scale,
+                    "large_scale": large_scale,
+                    "sigma_cutoff": sigma_cutoff,
+                },
+            }
+        else:
+            raise NotImplementedError
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
+            "params": {
+                "scale_schedule_config": scale_schedule_config,
+                **additional_guider_kwargs,
+            },
+        }
+    elif guider == "LinearPredictionGuider":
+        max_scale = st.number_input(
+            f"max-cfg-scale #{key}",
+            value=options.get("cfg", 1.5),
+            min_value=1.0,
+        )
+        min_scale = st.number_input(
+            f"min guidance scale",
+            value=options.get("min_cfg", 1.0),
+            min_value=1.0,
+            max_value=10.0,
+        )
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.LinearPredictionGuider",
+            "params": {
+                "max_scale": max_scale,
+                "min_scale": min_scale,
+                "num_frames": options["num_frames"],
+                **additional_guider_kwargs,
+            },
+        }
+    else:
+        raise NotImplementedError
+    return guider_config
+
+
+def init_sampling(
+    key=1,
+    img2img_strength: Optional[float] = None,
+    specify_num_samples: bool = True,
+    stage2strength: Optional[float] = None,
+    options: Optional[Dict[str, int]] = None,
+):
+    options = {} if options is None else options
+
+    num_rows, num_cols = 1, 1
+    if specify_num_samples:
+        num_cols = st.number_input(
+            f"num cols #{key}", value=num_cols, min_value=1, max_value=10
+        )
+
+    steps = st.sidebar.number_input(
+        f"steps #{key}", value=options.get("num_steps", 40), min_value=1, max_value=1000
+    )
+    sampler = st.sidebar.selectbox(
+        f"Sampler #{key}",
+        [
+            "EulerEDMSampler",
+            "HeunEDMSampler",
+            "EulerAncestralSampler",
+            "DPMPP2SAncestralSampler",
+            "DPMPP2MSampler",
+            "LinearMultistepSampler",
+        ],
+        options.get("sampler", 0),
+    )
+    discretization = st.sidebar.selectbox(
+        f"Discretization #{key}",
+        [
+            "LegacyDDPMDiscretization",
+            "EDMDiscretization",
+        ],
+        options.get("discretization", 0),
+    )
+
+    discretization_config = get_discretization(discretization, options=options, key=key)
+
+    guider_config = get_guider(options=options, key=key)
+
+    sampler = get_sampler(sampler, steps, discretization_config, guider_config, key=key)
+    if img2img_strength is not None:
+        st.warning(
+            f"Wrapping {sampler.__class__.__name__} with Img2ImgDiscretizationWrapper"
+        )
+        sampler.discretization = Img2ImgDiscretizationWrapper(
+            sampler.discretization, strength=img2img_strength
+        )
+    if stage2strength is not None:
+        sampler.discretization = Txt2NoisyDiscretizationWrapper(
+            sampler.discretization, strength=stage2strength, original_steps=steps
+        )
+    return sampler, num_rows, num_cols
+
+
+def get_discretization(discretization, options, key=1):
+    if discretization == "LegacyDDPMDiscretization":
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
+        }
+    elif discretization == "EDMDiscretization":
+        sigma_min = st.number_input(
+            f"sigma_min #{key}", value=options.get("sigma_min", 0.03)
+        )  # 0.0292
+        sigma_max = st.number_input(
+            f"sigma_max #{key}", value=options.get("sigma_max", 14.61)
+        )  # 14.6146
+        rho = st.number_input(f"rho #{key}", value=options.get("rho", 3.0))
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
+            "params": {
+                "sigma_min": sigma_min,
+                "sigma_max": sigma_max,
+                "rho": rho,
+            },
+        }
+
+    return discretization_config
+
+
+def get_sampler(sampler_name, steps, discretization_config, guider_config, key=1):
+    if sampler_name == "EulerEDMSampler" or sampler_name == "HeunEDMSampler":
+        s_churn = st.sidebar.number_input(f"s_churn #{key}", value=0.0, min_value=0.0)
+        s_tmin = st.sidebar.number_input(f"s_tmin #{key}", value=0.0, min_value=0.0)
+        s_tmax = st.sidebar.number_input(f"s_tmax #{key}", value=999.0, min_value=0.0)
+        s_noise = st.sidebar.number_input(f"s_noise #{key}", value=1.0, min_value=0.0)
+
+        if sampler_name == "EulerEDMSampler":
+            sampler = EulerEDMSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                s_churn=s_churn,
+                s_tmin=s_tmin,
+                s_tmax=s_tmax,
+                s_noise=s_noise,
+                verbose=True,
+            )
+        elif sampler_name == "HeunEDMSampler":
+            sampler = HeunEDMSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                s_churn=s_churn,
+                s_tmin=s_tmin,
+                s_tmax=s_tmax,
+                s_noise=s_noise,
+                verbose=True,
+            )
+    elif (
+        sampler_name == "EulerAncestralSampler"
+        or sampler_name == "DPMPP2SAncestralSampler"
+    ):
+        s_noise = st.sidebar.number_input("s_noise", value=1.0, min_value=0.0)
+        eta = st.sidebar.number_input("eta", value=1.0, min_value=0.0)
+
+        if sampler_name == "EulerAncestralSampler":
+            sampler = EulerAncestralSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                eta=eta,
+                s_noise=s_noise,
+                verbose=True,
+            )
+        elif sampler_name == "DPMPP2SAncestralSampler":
+            sampler = DPMPP2SAncestralSampler(
+                num_steps=steps,
+                discretization_config=discretization_config,
+                guider_config=guider_config,
+                eta=eta,
+                s_noise=s_noise,
+                verbose=True,
+            )
+    elif sampler_name == "DPMPP2MSampler":
+        sampler = DPMPP2MSampler(
+            num_steps=steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            verbose=True,
+        )
+    elif sampler_name == "LinearMultistepSampler":
+        order = st.sidebar.number_input("order", value=4, min_value=1)
+        sampler = LinearMultistepSampler(
+            num_steps=steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            order=order,
+            verbose=True,
+        )
+    else:
+        raise ValueError(f"unknown sampler {sampler_name}!")
+
+    return sampler
+
+
+def get_interactive_image() -> Image.Image:
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"])
+    if image is not None:
+        image = Image.open(image)
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        return image
+
+
+def load_img(
+    display: bool = True,
+    size: Union[None, int, Tuple[int, int]] = None,
+    center_crop: bool = False,
+):
+    image = get_interactive_image()
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+
+    transform = []
+    if size is not None:
+        transform.append(transforms.Resize(size))
+    if center_crop:
+        transform.append(transforms.CenterCrop(size))
+    transform.append(transforms.ToTensor())
+    transform.append(transforms.Lambda(lambda x: 2.0 * x - 1.0))
+
+    transform = transforms.Compose(transform)
+    img = transform(image)[None, ...]
+    st.text(f"input min/max/mean: {img.min():.3f}/{img.max():.3f}/{img.mean():.3f}")
+    return img
+
+
+def get_init_img(batch_size=1, key=None):
+    init_image = load_img(key=key).cuda()
+    init_image = repeat(init_image, "1 ... -> b ...", b=batch_size)
+    return init_image
+
+
+def do_sample(
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    H,
+    W,
+    C,
+    F,
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
+    batch2model_input: List = None,
+    return_latents=False,
+    filter=None,
+    T=None,
+    additional_batch_uc_fields=None,
+    decoding_t=None,
+):
+    force_uc_zero_embeddings = default(force_uc_zero_embeddings, [])
+    batch2model_input = default(batch2model_input, [])
+    additional_batch_uc_fields = default(additional_batch_uc_fields, [])
+
+    st.text("Sampling")
+
+    outputs = st.empty()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            with model.ema_scope():
+                if T is not None:
+                    num_samples = [num_samples, T]
+                else:
+                    num_samples = [num_samples]
+
+                load_model(model.conditioner)
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    num_samples,
+                    T=T,
+                    additional_batch_uc_fields=additional_batch_uc_fields,
+                )
+
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
+                )
+                unload_model(model.conditioner)
+
+                for k in c:
+                    if not k == "crossattn":
+                        c[k], uc[k] = map(
+                            lambda y: y[k][: math.prod(num_samples)].to("cuda"), (c, uc)
+                        )
+                    if k in ["crossattn", "concat"] and T is not None:
+                        uc[k] = repeat(uc[k], "b ... -> b t ...", t=T)
+                        uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=T)
+                        c[k] = repeat(c[k], "b ... -> b t ...", t=T)
+                        c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=T)
+
+                additional_model_inputs = {}
+                for k in batch2model_input:
+                    if k == "image_only_indicator":
+                        assert T is not None
+
+                        if isinstance(
+                            sampler.guider, (VanillaCFG, LinearPredictionGuider)
+                        ):
+                            additional_model_inputs[k] = torch.zeros(
+                                num_samples[0] * 2, num_samples[1]
+                            ).to("cuda")
+                        else:
+                            additional_model_inputs[k] = torch.zeros(num_samples).to(
+                                "cuda"
+                            )
+                    else:
+                        additional_model_inputs[k] = batch[k]
+
+                shape = (math.prod(num_samples), C, H // F, W // F)
+                randn = torch.randn(shape).to("cuda")
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                load_model(model.denoiser)
+                load_model(model.model)
+                samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+                unload_model(model.model)
+                unload_model(model.denoiser)
+
+                load_model(model.first_stage_model)
+                model.en_and_decode_n_samples_a_time = (
+                    decoding_t  # Decode n frames at a time
+                )
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+                unload_model(model.first_stage_model)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if T is None:
+                    grid = torch.stack([samples])
+                    grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
+                    outputs.image(grid.cpu().numpy())
+                else:
+                    as_vids = rearrange(samples, "(b t) c h w -> b t c h w", t=T)
+                    for i, vid in enumerate(as_vids):
+                        grid = rearrange(make_grid(vid, nrow=4), "c h w -> h w c")
+                        st.image(
+                            grid.cpu().numpy(),
+                            f"Sample #{i} as image",
+                        )
+
+                if return_latents:
+                    return samples, samples_z
+                return samples
+
+
+def get_batch(
+    keys,
+    value_dict: dict,
+    N: Union[List, ListConfig],
+    device: str = "cuda",
+    T: int = None,
+    additional_batch_uc_fields: List[str] = [],
+):
+    # Hardcoded demo setups; might undergo some changes in the future
+
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "txt":
+            batch["txt"] = [value_dict["prompt"]] * math.prod(N)
+
+            batch_uc["txt"] = [value_dict["negative_prompt"]] * math.prod(N)
+
+        elif key == "original_size_as_tuple":
+            batch["original_size_as_tuple"] = (
+                torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "crop_coords_top_left":
+            batch["crop_coords_top_left"] = (
+                torch.tensor(
+                    [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
+                )
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "aesthetic_score":
+            batch["aesthetic_score"] = (
+                torch.tensor([value_dict["aesthetic_score"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+            batch_uc["aesthetic_score"] = (
+                torch.tensor([value_dict["negative_aesthetic_score"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+
+        elif key == "target_size_as_tuple":
+            batch["target_size_as_tuple"] = (
+                torch.tensor([value_dict["target_height"], value_dict["target_width"]])
+                .to(device)
+                .repeat(math.prod(N), 1)
+            )
+        elif key == "fps":
+            batch[key] = (
+                torch.tensor([value_dict["fps"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "fps_id":
+            batch[key] = (
+                torch.tensor([value_dict["fps_id"]]).to(device).repeat(math.prod(N))
+            )
+        elif key == "motion_bucket_id":
+            batch[key] = (
+                torch.tensor([value_dict["motion_bucket_id"]])
+                .to(device)
+                .repeat(math.prod(N))
+            )
+        elif key == "pool_image":
+            batch[key] = repeat(value_dict[key], "1 ... -> b ...", b=math.prod(N)).to(
+                device, dtype=torch.half
+            )
+        elif key == "cond_aug":
+            batch[key] = repeat(
+                torch.tensor([value_dict["cond_aug"]]).to("cuda"),
+                "1 -> b",
+                b=math.prod(N),
+            )
+        elif key == "cond_frames":
+            batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
+        elif key == "cond_frames_without_noise":
+            batch[key] = repeat(
+                value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
+            )
+        else:
+            batch[key] = value_dict[key]
+
+    if T is not None:
+        batch["num_video_frames"] = T
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+        elif key in additional_batch_uc_fields and key not in batch_uc:
+            batch_uc[key] = copy.copy(batch[key])
+    return batch, batch_uc
+
+
+@torch.no_grad()
+def do_img2img(
+    img,
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    force_uc_zero_embeddings: Optional[List] = None,
+    force_cond_zero_embeddings: Optional[List] = None,
+    additional_kwargs={},
+    offset_noise_level: int = 0.0,
+    return_latents=False,
+    skip_encode=False,
+    filter=None,
+    add_noise=True,
+):
+    st.text("Sampling")
+
+    outputs = st.empty()
+    precision_scope = autocast
+    with torch.no_grad():
+        with precision_scope("cuda"):
+            with model.ema_scope():
+                load_model(model.conditioner)
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [num_samples],
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                    force_cond_zero_embeddings=force_cond_zero_embeddings,
+                )
+                unload_model(model.conditioner)
+                for k in c:
+                    c[k], uc[k] = map(lambda y: y[k][:num_samples].to("cuda"), (c, uc))
+
+                for k in additional_kwargs:
+                    c[k] = uc[k] = additional_kwargs[k]
+                if skip_encode:
+                    z = img
+                else:
+                    load_model(model.first_stage_model)
+                    z = model.encode_first_stage(img)
+                    unload_model(model.first_stage_model)
+
+                noise = torch.randn_like(z)
+
+                sigmas = sampler.discretization(sampler.num_steps).cuda()
+                sigma = sigmas[0]
+
+                st.info(f"all sigmas: {sigmas}")
+                st.info(f"noising sigma: {sigma}")
+                if offset_noise_level > 0.0:
+                    noise = noise + offset_noise_level * append_dims(
+                        torch.randn(z.shape[0], device=z.device), z.ndim
+                    )
+                if add_noise:
+                    noised_z = z + noise * append_dims(sigma, z.ndim).cuda()
+                    noised_z = noised_z / torch.sqrt(
+                        1.0 + sigmas[0] ** 2.0
+                    )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
+                else:
+                    noised_z = z / torch.sqrt(1.0 + sigmas[0] ** 2.0)
+
+                def denoiser(x, sigma, c):
+                    return model.denoiser(model.model, x, sigma, c)
+
+                load_model(model.denoiser)
+                load_model(model.model)
+                samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
+                unload_model(model.model)
+                unload_model(model.denoiser)
+
+                load_model(model.first_stage_model)
+                samples_x = model.decode_first_stage(samples_z)
+                unload_model(model.first_stage_model)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                grid = rearrange(grid, "n b c h w -> (n h) (b w) c")
+                outputs.image(grid.cpu().numpy())
+                if return_latents:
+                    return samples, samples_z
+                return samples
+
+
+def get_resizing_factor(
+    desired_shape: Tuple[int, int], current_shape: Tuple[int, int]
+) -> float:
+    r_bound = desired_shape[1] / desired_shape[0]
+    aspect_r = current_shape[1] / current_shape[0]
+    if r_bound >= 1.0:
+        if aspect_r >= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r < 1.0:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+    else:
+        if aspect_r <= r_bound:
+            factor = min(desired_shape) / min(current_shape)
+        else:
+            if aspect_r > 1:
+                factor = max(desired_shape) / min(current_shape)
+            else:
+                factor = max(desired_shape) / max(current_shape)
+
+    return factor
+
+
+def get_interactive_image(key=None) -> Image.Image:
+    image = st.file_uploader("Input", type=["jpg", "JPEG", "png"], key=key)
+    if image is not None:
+        image = Image.open(image)
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        return image
+
+
+def load_img_for_prediction(
+    W: int, H: int, display=True, key=None, device="cuda"
+) -> torch.Tensor:
+    image = get_interactive_image(key=key)
+    if image is None:
+        return None
+    if display:
+        st.image(image)
+    w, h = image.size
+
+    image = np.array(image).transpose(2, 0, 1)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 255.0
+    image = image.unsqueeze(0)
+
+    rfs = get_resizing_factor((H, W), (h, w))
+    resize_size = [int(np.ceil(rfs * s)) for s in (h, w)]
+    top = (resize_size[0] - H) // 2
+    left = (resize_size[1] - W) // 2
+
+    image = torch.nn.functional.interpolate(
+        image, resize_size, mode="area", antialias=False
+    )
+    image = TT.functional.crop(image, top=top, left=left, height=H, width=W)
+
+    if display:
+        numpy_img = np.transpose(image[0].numpy(), (1, 2, 0))
+        pil_image = Image.fromarray((numpy_img * 255).astype(np.uint8))
+        st.image(pil_image)
+    return image.to(device) * 2.0 - 1.0
+
+
+def save_video_as_grid_and_mp4(
+    video_batch: torch.Tensor, save_path: str, T: int, fps: int = 5
+):
+    os.makedirs(save_path, exist_ok=True)
+    base_count = len(glob(os.path.join(save_path, "*.mp4")))
+
+    video_batch = rearrange(video_batch, "(b t) c h w -> b t c h w", t=T)
+    video_batch = embed_watermark(video_batch)
+    for vid in video_batch:
+        save_image(vid, fp=os.path.join(save_path, f"{base_count:06d}.png"), nrow=4)
+
+        video_path = os.path.join(save_path, f"{base_count:06d}.mp4")
+
+        writer = cv2.VideoWriter(
+            video_path,
+            cv2.VideoWriter_fourcc(*"MP4V"),
+            fps,
+            (vid.shape[-1], vid.shape[-2]),
+        )
+
+        vid = (
+            (rearrange(vid, "t c h w -> t h w c") * 255).cpu().numpy().astype(np.uint8)
+        )
+        for frame in vid:
+            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+            writer.write(frame)
+
+        writer.release()
+
+        video_path_h264 = video_path[:-4] + "_h264.mp4"
+        os.system(f"ffmpeg -i {video_path} -c:v libx264 {video_path_h264}")
+
+        with open(video_path_h264, "rb") as f:
+            video_bytes = f.read()
+        st.video(video_bytes)
+
+        base_count += 1

scripts/demo/video_sampling.py

@@ -0,0 +1,200 @@
+import os
+
+from pytorch_lightning import seed_everything
+
+from scripts.demo.streamlit_helpers import *
+
+SAVE_PATH = "outputs/demo/vid/"
+
+VERSION2SPECS = {
+    "svd": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_image_decoder": {
+        "T": 14,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 2.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 25,
+        },
+    },
+    "svd_xt": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd.yaml",
+        "ckpt": "checkpoints/svd_xt.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+    "svd_xt_image_decoder": {
+        "T": 25,
+        "H": 576,
+        "W": 1024,
+        "C": 4,
+        "f": 8,
+        "config": "configs/inference/svd_image_decoder.yaml",
+        "ckpt": "checkpoints/svd_xt_image_decoder.safetensors",
+        "options": {
+            "discretization": 1,
+            "cfg": 3.0,
+            "min_cfg": 1.5,
+            "sigma_min": 0.002,
+            "sigma_max": 700.0,
+            "rho": 7.0,
+            "guider": 2,
+            "force_uc_zero_embeddings": ["cond_frames", "cond_frames_without_noise"],
+            "num_steps": 30,
+            "decoding_t": 14,
+        },
+    },
+}
+
+
+if __name__ == "__main__":
+    st.title("Stable Video Diffusion")
+    version = st.selectbox(
+        "Model Version",
+        [k for k in VERSION2SPECS.keys()],
+        0,
+    )
+    version_dict = VERSION2SPECS[version]
+    if st.checkbox("Load Model"):
+        mode = "img2vid"
+    else:
+        mode = "skip"
+
+    H = st.sidebar.number_input(
+        "H", value=version_dict["H"], min_value=64, max_value=2048
+    )
+    W = st.sidebar.number_input(
+        "W", value=version_dict["W"], min_value=64, max_value=2048
+    )
+    T = st.sidebar.number_input(
+        "T", value=version_dict["T"], min_value=0, max_value=128
+    )
+    C = version_dict["C"]
+    F = version_dict["f"]
+    options = version_dict["options"]
+
+    if mode != "skip":
+        state = init_st(version_dict, load_filter=True)
+        if state["msg"]:
+            st.info(state["msg"])
+        model = state["model"]
+
+        ukeys = set(
+            get_unique_embedder_keys_from_conditioner(state["model"].conditioner)
+        )
+
+        value_dict = init_embedder_options(
+            ukeys,
+            {},
+        )
+
+        value_dict["image_only_indicator"] = 0
+
+        if mode == "img2vid":
+            img = load_img_for_prediction(W, H)
+            cond_aug = st.number_input(
+                "Conditioning augmentation:", value=0.02, min_value=0.0
+            )
+            value_dict["cond_frames_without_noise"] = img
+            value_dict["cond_frames"] = img + cond_aug * torch.randn_like(img)
+            value_dict["cond_aug"] = cond_aug
+
+        seed = st.sidebar.number_input(
+            "seed", value=23, min_value=0, max_value=int(1e9)
+        )
+        seed_everything(seed)
+
+        save_locally, save_path = init_save_locally(
+            os.path.join(SAVE_PATH, version), init_value=True
+        )
+
+        options["num_frames"] = T
+
+        sampler, num_rows, num_cols = init_sampling(options=options)
+        num_samples = num_rows * num_cols
+
+        decoding_t = st.number_input(
+            "Decode t frames at a time (set small if you are low on VRAM)",
+            value=options.get("decoding_t", T),
+            min_value=1,
+            max_value=int(1e9),
+        )
+
+        if st.checkbox("Overwrite fps in mp4 generator", False):
+            saving_fps = st.number_input(
+                f"saving video at fps:", value=value_dict["fps"], min_value=1
+            )
+        else:
+            saving_fps = value_dict["fps"]
+
+        if st.button("Sample"):
+            out = do_sample(
+                model,
+                sampler,
+                value_dict,
+                num_samples,
+                H,
+                W,
+                C,
+                F,
+                T=T,
+                batch2model_input=["num_video_frames", "image_only_indicator"],
+                force_uc_zero_embeddings=options.get("force_uc_zero_embeddings", None),
+                force_cond_zero_embeddings=options.get(
+                    "force_cond_zero_embeddings", None
+                ),
+                return_latents=False,
+                decoding_t=decoding_t,
+            )
+
+            if isinstance(out, (tuple, list)):
+                samples, samples_z = out
+            else:
+                samples = out
+                samples_z = None
+
+            if save_locally:
+                save_video_as_grid_and_mp4(samples, save_path, T, fps=saving_fps)

scripts/sampling/configs/svd.yaml

@@ -0,0 +1,146 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 2.5
+            min_scale: 1.0

scripts/sampling/configs/svd_image_decoder.yaml

@@ -0,0 +1,129 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_image_decoder.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 2.5
+            min_scale: 1.0

scripts/sampling/configs/svd_xt.yaml

@@ -0,0 +1,146 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_xt.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencodingEngine
+      params:
+        loss_config:
+          target: torch.nn.Identity
+        regularizer_config:
+          target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer
+        encoder_config: 
+          target: sgm.modules.diffusionmodules.model.Encoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+        decoder_config:
+          target: sgm.modules.autoencoding.temporal_ae.VideoDecoder
+          params:
+            attn_type: vanilla
+            double_z: True
+            z_channels: 4
+            resolution: 256
+            in_channels: 3
+            out_ch: 3
+            ch: 128
+            ch_mult: [1, 2, 4, 4]
+            num_res_blocks: 2
+            attn_resolutions: []
+            dropout: 0.0
+            video_kernel_size: [3, 1, 1]
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 3.0
+            min_scale: 1.5

scripts/sampling/configs/svd_xt_image_decoder.yaml

@@ -0,0 +1,129 @@
+model:
+  target: sgm.models.diffusion.DiffusionEngine
+  params:
+    scale_factor: 0.18215
+    disable_first_stage_autocast: True
+    ckpt_path: checkpoints/svd_xt_image_decoder.safetensors
+
+    denoiser_config:
+      target: sgm.modules.diffusionmodules.denoiser.Denoiser
+      params:
+        scaling_config:
+          target: sgm.modules.diffusionmodules.denoiser_scaling.VScalingWithEDMcNoise
+
+    network_config:
+      target: sgm.modules.diffusionmodules.video_model.VideoUNet
+      params:
+        adm_in_channels: 768
+        num_classes: sequential
+        use_checkpoint: True
+        in_channels: 8
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [4, 2, 1]
+        num_res_blocks: 2
+        channel_mult: [1, 2, 4, 4]
+        num_head_channels: 64
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        spatial_transformer_attn_type: softmax-xformers
+        extra_ff_mix_layer: True
+        use_spatial_context: True
+        merge_strategy: learned_with_images
+        video_kernel_size: [3, 1, 1]
+
+    conditioner_config:
+      target: sgm.modules.GeneralConditioner
+      params:
+        emb_models:
+        - is_trainable: False
+          input_key: cond_frames_without_noise
+          target: sgm.modules.encoders.modules.FrozenOpenCLIPImagePredictionEmbedder
+          params:
+            n_cond_frames: 1
+            n_copies: 1
+            open_clip_embedding_config:
+              target: sgm.modules.encoders.modules.FrozenOpenCLIPImageEmbedder
+              params:
+                freeze: True
+
+        - input_key: fps_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: motion_bucket_id
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+        - input_key: cond_frames
+          is_trainable: False
+          target: sgm.modules.encoders.modules.VideoPredictionEmbedderWithEncoder
+          params:
+            disable_encoder_autocast: True
+            n_cond_frames: 1
+            n_copies: 1
+            is_ae: True
+            encoder_config:
+              target: sgm.models.autoencoder.AutoencoderKLModeOnly
+              params:
+                embed_dim: 4
+                monitor: val/rec_loss
+                ddconfig:
+                  attn_type: vanilla-xformers
+                  double_z: True
+                  z_channels: 4
+                  resolution: 256
+                  in_channels: 3
+                  out_ch: 3
+                  ch: 128
+                  ch_mult: [1, 2, 4, 4]
+                  num_res_blocks: 2
+                  attn_resolutions: []
+                  dropout: 0.0
+                lossconfig:
+                  target: torch.nn.Identity
+
+        - input_key: cond_aug
+          is_trainable: False
+          target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
+          params:
+            outdim: 256
+
+    first_stage_config:
+      target: sgm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          attn_type: vanilla-xformers
+          double_z: True
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult: [1, 2, 4, 4]
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    sampler_config:
+      target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler
+      params:
+        discretization_config:
+          target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization
+          params:
+            sigma_max: 700.0
+
+        guider_config:
+          target: sgm.modules.diffusionmodules.guiders.LinearPredictionGuider
+          params:
+            max_scale: 3.0
+            min_scale: 1.5

scripts/sampling/simple_video_sample.py

@@ -0,0 +1,278 @@
+import math
+import os
+from glob import glob
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from fire import Fire
+from omegaconf import OmegaConf
+from PIL import Image
+from torchvision.transforms import ToTensor
+
+from scripts.util.detection.nsfw_and_watermark_dectection import \
+    DeepFloydDataFiltering
+from sgm.inference.helpers import embed_watermark
+from sgm.util import default, instantiate_from_config
+
+
+def sample(
+    input_path: str = "assets/test_image.png",  # Can either be image file or folder with image files
+    num_frames: Optional[int] = None,
+    num_steps: Optional[int] = None,
+    version: str = "svd",
+    fps_id: int = 6,
+    motion_bucket_id: int = 127,
+    cond_aug: float = 0.02,
+    seed: int = 23,
+    decoding_t: int = 14,  # Number of frames decoded at a time! This eats most VRAM. Reduce if necessary.
+    device: str = "cuda",
+    output_folder: Optional[str] = None,
+):
+    """
+    Simple script to generate a single sample conditioned on an image `input_path` or multiple images, one for each
+    image file in folder `input_path`. If you run out of VRAM, try decreasing `decoding_t`.
+    """
+
+    if version == "svd":
+        num_frames = default(num_frames, 14)
+        num_steps = default(num_steps, 25)
+        output_folder = default(output_folder, "outputs/simple_video_sample/svd/")
+        model_config = "scripts/sampling/configs/svd.yaml"
+    elif version == "svd_xt":
+        num_frames = default(num_frames, 25)
+        num_steps = default(num_steps, 30)
+        output_folder = default(output_folder, "outputs/simple_video_sample/svd_xt/")
+        model_config = "scripts/sampling/configs/svd_xt.yaml"
+    elif version == "svd_image_decoder":
+        num_frames = default(num_frames, 14)
+        num_steps = default(num_steps, 25)
+        output_folder = default(
+            output_folder, "outputs/simple_video_sample/svd_image_decoder/"
+        )
+        model_config = "scripts/sampling/configs/svd_image_decoder.yaml"
+    elif version == "svd_xt_image_decoder":
+        num_frames = default(num_frames, 25)
+        num_steps = default(num_steps, 30)
+        output_folder = default(
+            output_folder, "outputs/simple_video_sample/svd_xt_image_decoder/"
+        )
+        model_config = "scripts/sampling/configs/svd_xt_image_decoder.yaml"
+    else:
+        raise ValueError(f"Version {version} does not exist.")
+
+    model, filter = load_model(
+        model_config,
+        device,
+        num_frames,
+        num_steps,
+    )
+    torch.manual_seed(seed)
+
+    path = Path(input_path)
+    all_img_paths = []
+    if path.is_file():
+        if any([input_path.endswith(x) for x in ["jpg", "jpeg", "png"]]):
+            all_img_paths = [input_path]
+        else:
+            raise ValueError("Path is not valid image file.")
+    elif path.is_dir():
+        all_img_paths = sorted(
+            [
+                f
+                for f in path.iterdir()
+                if f.is_file() and f.suffix.lower() in [".jpg", ".jpeg", ".png"]
+            ]
+        )
+        if len(all_img_paths) == 0:
+            raise ValueError("Folder does not contain any images.")
+    else:
+        raise ValueError
+
+    for input_img_path in all_img_paths:
+        with Image.open(input_img_path) as image:
+            if image.mode == "RGBA":
+                image = image.convert("RGB")
+            w, h = image.size
+
+            if h % 64 != 0 or w % 64 != 0:
+                width, height = map(lambda x: x - x % 64, (w, h))
+                image = image.resize((width, height))
+                print(
+                    f"WARNING: Your image is of size {h}x{w} which is not divisible by 64. We are resizing to {height}x{width}!"
+                )
+
+            image = ToTensor()(image)
+            image = image * 2.0 - 1.0
+
+        image = image.unsqueeze(0).to(device)
+        H, W = image.shape[2:]
+        assert image.shape[1] == 3
+        F = 8
+        C = 4
+        shape = (num_frames, C, H // F, W // F)
+        if (H, W) != (576, 1024):
+            print(
+                "WARNING: The conditioning frame you provided is not 576x1024. This leads to suboptimal performance as model was only trained on 576x1024. Consider increasing `cond_aug`."
+            )
+        if motion_bucket_id > 255:
+            print(
+                "WARNING: High motion bucket! This may lead to suboptimal performance."
+            )
+
+        if fps_id < 5:
+            print("WARNING: Small fps value! This may lead to suboptimal performance.")
+
+        if fps_id > 30:
+            print("WARNING: Large fps value! This may lead to suboptimal performance.")
+
+        value_dict = {}
+        value_dict["motion_bucket_id"] = motion_bucket_id
+        value_dict["fps_id"] = fps_id
+        value_dict["cond_aug"] = cond_aug
+        value_dict["cond_frames_without_noise"] = image
+        value_dict["cond_frames"] = image + cond_aug * torch.randn_like(image)
+        value_dict["cond_aug"] = cond_aug
+
+        with torch.no_grad():
+            with torch.autocast(device):
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [1, num_frames],
+                    T=num_frames,
+                    device=device,
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=[
+                        "cond_frames",
+                        "cond_frames_without_noise",
+                    ],
+                )
+
+                for k in ["crossattn", "concat"]:
+                    uc[k] = repeat(uc[k], "b ... -> b t ...", t=num_frames)
+                    uc[k] = rearrange(uc[k], "b t ... -> (b t) ...", t=num_frames)
+                    c[k] = repeat(c[k], "b ... -> b t ...", t=num_frames)
+                    c[k] = rearrange(c[k], "b t ... -> (b t) ...", t=num_frames)
+
+                randn = torch.randn(shape, device=device)
+
+                additional_model_inputs = {}
+                additional_model_inputs["image_only_indicator"] = torch.zeros(
+                    2, num_frames
+                ).to(device)
+                additional_model_inputs["num_video_frames"] = batch["num_video_frames"]
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = model.sampler(denoiser, randn, cond=c, uc=uc)
+                model.en_and_decode_n_samples_a_time = decoding_t
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                os.makedirs(output_folder, exist_ok=True)
+                base_count = len(glob(os.path.join(output_folder, "*.mp4")))
+                video_path = os.path.join(output_folder, f"{base_count:06d}.mp4")
+                writer = cv2.VideoWriter(
+                    video_path,
+                    cv2.VideoWriter_fourcc(*"MP4V"),
+                    fps_id + 1,
+                    (samples.shape[-1], samples.shape[-2]),
+                )
+
+                samples = embed_watermark(samples)
+                samples = filter(samples)
+                vid = (
+                    (rearrange(samples, "t c h w -> t h w c") * 255)
+                    .cpu()
+                    .numpy()
+                    .astype(np.uint8)
+                )
+                for frame in vid:
+                    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+                    writer.write(frame)
+                writer.release()
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list(set([x.input_key for x in conditioner.embedders]))
+
+
+def get_batch(keys, value_dict, N, T, device):
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "fps_id":
+            batch[key] = (
+                torch.tensor([value_dict["fps_id"]])
+                .to(device)
+                .repeat(int(math.prod(N)))
+            )
+        elif key == "motion_bucket_id":
+            batch[key] = (
+                torch.tensor([value_dict["motion_bucket_id"]])
+                .to(device)
+                .repeat(int(math.prod(N)))
+            )
+        elif key == "cond_aug":
+            batch[key] = repeat(
+                torch.tensor([value_dict["cond_aug"]]).to(device),
+                "1 -> b",
+                b=math.prod(N),
+            )
+        elif key == "cond_frames":
+            batch[key] = repeat(value_dict["cond_frames"], "1 ... -> b ...", b=N[0])
+        elif key == "cond_frames_without_noise":
+            batch[key] = repeat(
+                value_dict["cond_frames_without_noise"], "1 ... -> b ...", b=N[0]
+            )
+        else:
+            batch[key] = value_dict[key]
+
+    if T is not None:
+        batch["num_video_frames"] = T
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+    return batch, batch_uc
+
+
+def load_model(
+    config: str,
+    device: str,
+    num_frames: int,
+    num_steps: int,
+):
+    config = OmegaConf.load(config)
+    if device == "cuda":
+        config.model.params.conditioner_config.params.emb_models[
+            0
+        ].params.open_clip_embedding_config.params.init_device = device
+
+    config.model.params.sampler_config.params.num_steps = num_steps
+    config.model.params.sampler_config.params.guider_config.params.num_frames = (
+        num_frames
+    )
+    if device == "cuda":
+        with torch.device(device):
+            model = instantiate_from_config(config.model).to(device).eval()
+    else:
+        model = instantiate_from_config(config.model).to(device).eval()
+
+    filter = DeepFloydDataFiltering(verbose=False, device=device)
+    return model, filter
+
+
+if __name__ == "__main__":
+    Fire(sample)

scripts/tests/attention.py

@@ -0,0 +1,319 @@
+import einops
+import torch
+import torch.nn.functional as F
+import torch.utils.benchmark as benchmark
+from torch.backends.cuda import SDPBackend
+
+from sgm.modules.attention import BasicTransformerBlock, SpatialTransformer
+
+
+def benchmark_attn():
+    # Lets define a helpful benchmarking function:
+    # https://pytorch.org/tutorials/intermediate/scaled_dot_product_attention_tutorial.html
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
+        t0 = benchmark.Timer(
+            stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
+        )
+        return t0.blocked_autorange().mean * 1e6
+
+    # Lets define the hyper-parameters of our input
+    batch_size = 32
+    max_sequence_len = 1024
+    num_heads = 32
+    embed_dimension = 32
+
+    dtype = torch.float16
+
+    query = torch.rand(
+        batch_size,
+        num_heads,
+        max_sequence_len,
+        embed_dimension,
+        device=device,
+        dtype=dtype,
+    )
+    key = torch.rand(
+        batch_size,
+        num_heads,
+        max_sequence_len,
+        embed_dimension,
+        device=device,
+        dtype=dtype,
+    )
+    value = torch.rand(
+        batch_size,
+        num_heads,
+        max_sequence_len,
+        embed_dimension,
+        device=device,
+        dtype=dtype,
+    )
+
+    print(f"q/k/v shape:", query.shape, key.shape, value.shape)
+
+    # Lets explore the speed of each of the 3 implementations
+    from torch.backends.cuda import SDPBackend, sdp_kernel
+
+    # Helpful arguments mapper
+    backend_map = {
+        SDPBackend.MATH: {
+            "enable_math": True,
+            "enable_flash": False,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.FLASH_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": True,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.EFFICIENT_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": False,
+            "enable_mem_efficient": True,
+        },
+    }
+
+    from torch.profiler import ProfilerActivity, profile, record_function
+
+    activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA]
+
+    print(
+        f"The default implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+    )
+    with profile(
+        activities=activities, record_shapes=False, profile_memory=True
+    ) as prof:
+        with record_function("Default detailed stats"):
+            for _ in range(25):
+                o = F.scaled_dot_product_attention(query, key, value)
+    print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    print(
+        f"The math implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+    )
+    with sdp_kernel(**backend_map[SDPBackend.MATH]):
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("Math implmentation stats"):
+                for _ in range(25):
+                    o = F.scaled_dot_product_attention(query, key, value)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    with sdp_kernel(**backend_map[SDPBackend.FLASH_ATTENTION]):
+        try:
+            print(
+                f"The flash attention implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+            )
+        except RuntimeError:
+            print("FlashAttention is not supported. See warnings for reasons.")
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("FlashAttention stats"):
+                for _ in range(25):
+                    o = F.scaled_dot_product_attention(query, key, value)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+    with sdp_kernel(**backend_map[SDPBackend.EFFICIENT_ATTENTION]):
+        try:
+            print(
+                f"The memory efficient implementation runs in {benchmark_torch_function_in_microseconds(F.scaled_dot_product_attention, query, key, value):.3f} microseconds"
+            )
+        except RuntimeError:
+            print("EfficientAttention is not supported. See warnings for reasons.")
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("EfficientAttention stats"):
+                for _ in range(25):
+                    o = F.scaled_dot_product_attention(query, key, value)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+
+
+def run_model(model, x, context):
+    return model(x, context)
+
+
+def benchmark_transformer_blocks():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    import torch.utils.benchmark as benchmark
+
+    def benchmark_torch_function_in_microseconds(f, *args, **kwargs):
+        t0 = benchmark.Timer(
+            stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
+        )
+        return t0.blocked_autorange().mean * 1e6
+
+    checkpoint = True
+    compile = False
+
+    batch_size = 32
+    h, w = 64, 64
+    context_len = 77
+    embed_dimension = 1024
+    context_dim = 1024
+    d_head = 64
+
+    transformer_depth = 4
+
+    n_heads = embed_dimension // d_head
+
+    dtype = torch.float16
+
+    model_native = SpatialTransformer(
+        embed_dimension,
+        n_heads,
+        d_head,
+        context_dim=context_dim,
+        use_linear=True,
+        use_checkpoint=checkpoint,
+        attn_type="softmax",
+        depth=transformer_depth,
+        sdp_backend=SDPBackend.FLASH_ATTENTION,
+    ).to(device)
+    model_efficient_attn = SpatialTransformer(
+        embed_dimension,
+        n_heads,
+        d_head,
+        context_dim=context_dim,
+        use_linear=True,
+        depth=transformer_depth,
+        use_checkpoint=checkpoint,
+        attn_type="softmax-xformers",
+    ).to(device)
+    if not checkpoint and compile:
+        print("compiling models")
+        model_native = torch.compile(model_native)
+        model_efficient_attn = torch.compile(model_efficient_attn)
+
+    x = torch.rand(batch_size, embed_dimension, h, w, device=device, dtype=dtype)
+    c = torch.rand(batch_size, context_len, context_dim, device=device, dtype=dtype)
+
+    from torch.profiler import ProfilerActivity, profile, record_function
+
+    activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA]
+
+    with torch.autocast("cuda"):
+        print(
+            f"The native model runs in {benchmark_torch_function_in_microseconds(model_native.forward, x, c):.3f} microseconds"
+        )
+        print(
+            f"The efficientattn model runs in {benchmark_torch_function_in_microseconds(model_efficient_attn.forward, x, c):.3f} microseconds"
+        )
+
+        print(75 * "+")
+        print("NATIVE")
+        print(75 * "+")
+        torch.cuda.reset_peak_memory_stats()
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("NativeAttention stats"):
+                for _ in range(25):
+                    model_native(x, c)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+        print(torch.cuda.max_memory_allocated() * 1e-9, "GB used by native block")
+
+        print(75 * "+")
+        print("Xformers")
+        print(75 * "+")
+        torch.cuda.reset_peak_memory_stats()
+        with profile(
+            activities=activities, record_shapes=False, profile_memory=True
+        ) as prof:
+            with record_function("xformers stats"):
+                for _ in range(25):
+                    model_efficient_attn(x, c)
+        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))
+        print(torch.cuda.max_memory_allocated() * 1e-9, "GB used by xformers block")
+
+
+def test01():
+    # conv1x1 vs linear
+    from sgm.util import count_params
+
+    conv = torch.nn.Conv2d(3, 32, kernel_size=1).cuda()
+    print(count_params(conv))
+    linear = torch.nn.Linear(3, 32).cuda()
+    print(count_params(linear))
+
+    print(conv.weight.shape)
+
+    # use same initialization
+    linear.weight = torch.nn.Parameter(conv.weight.squeeze(-1).squeeze(-1))
+    linear.bias = torch.nn.Parameter(conv.bias)
+
+    print(linear.weight.shape)
+
+    x = torch.randn(11, 3, 64, 64).cuda()
+
+    xr = einops.rearrange(x, "b c h w -> b (h w) c").contiguous()
+    print(xr.shape)
+    out_linear = linear(xr)
+    print(out_linear.mean(), out_linear.shape)
+
+    out_conv = conv(x)
+    print(out_conv.mean(), out_conv.shape)
+    print("done with test01.\n")
+
+
+def test02():
+    # try cosine flash attention
+    import time
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.backends.cudnn.benchmark = True
+    print("testing cosine flash attention...")
+    DIM = 1024
+    SEQLEN = 4096
+    BS = 16
+
+    print(" softmax (vanilla) first...")
+    model = BasicTransformerBlock(
+        dim=DIM,
+        n_heads=16,
+        d_head=64,
+        dropout=0.0,
+        context_dim=None,
+        attn_mode="softmax",
+    ).cuda()
+    try:
+        x = torch.randn(BS, SEQLEN, DIM).cuda()
+        tic = time.time()
+        y = model(x)
+        toc = time.time()
+        print(y.shape, toc - tic)
+    except RuntimeError as e:
+        # likely oom
+        print(str(e))
+
+    print("\n now flash-cosine...")
+    model = BasicTransformerBlock(
+        dim=DIM,
+        n_heads=16,
+        d_head=64,
+        dropout=0.0,
+        context_dim=None,
+        attn_mode="flash-cosine",
+    ).cuda()
+    x = torch.randn(BS, SEQLEN, DIM).cuda()
+    tic = time.time()
+    y = model(x)
+    toc = time.time()
+    print(y.shape, toc - tic)
+    print("done with test02.\n")
+
+
+if __name__ == "__main__":
+    # test01()
+    # test02()
+    # test03()
+
+    # benchmark_attn()
+    benchmark_transformer_blocks()
+
+    print("done.")

scripts/util/detection/nsfw_and_watermark_dectection.py

@@ -0,0 +1,110 @@
+import os
+
+import clip
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+
+RESOURCES_ROOT = "scripts/util/detection/"
+
+
+def predict_proba(X, weights, biases):
+    logits = X @ weights.T + biases
+    proba = np.where(
+        logits >= 0, 1 / (1 + np.exp(-logits)), np.exp(logits) / (1 + np.exp(logits))
+    )
+    return proba.T
+
+
+def load_model_weights(path: str):
+    model_weights = np.load(path)
+    return model_weights["weights"], model_weights["biases"]
+
+
+def clip_process_images(images: torch.Tensor) -> torch.Tensor:
+    min_size = min(images.shape[-2:])
+    return T.Compose(
+        [
+            T.CenterCrop(min_size),  # TODO: this might affect the watermark, check this
+            T.Resize(224, interpolation=T.InterpolationMode.BICUBIC, antialias=True),
+            T.Normalize(
+                (0.48145466, 0.4578275, 0.40821073),
+                (0.26862954, 0.26130258, 0.27577711),
+            ),
+        ]
+    )(images)
+
+
+class DeepFloydDataFiltering(object):
+    def __init__(
+        self, verbose: bool = False, device: torch.device = torch.device("cpu")
+    ):
+        super().__init__()
+        self.verbose = verbose
+        self._device = None
+        self.clip_model, _ = clip.load("ViT-L/14", device=device)
+        self.clip_model.eval()
+
+        self.cpu_w_weights, self.cpu_w_biases = load_model_weights(
+            os.path.join(RESOURCES_ROOT, "w_head_v1.npz")
+        )
+        self.cpu_p_weights, self.cpu_p_biases = load_model_weights(
+            os.path.join(RESOURCES_ROOT, "p_head_v1.npz")
+        )
+        self.w_threshold, self.p_threshold = 0.5, 0.5
+
+    @torch.inference_mode()
+    def __call__(self, images: torch.Tensor) -> torch.Tensor:
+        imgs = clip_process_images(images)
+        if self._device is None:
+            self._device = next(p for p in self.clip_model.parameters()).device
+        image_features = self.clip_model.encode_image(imgs.to(self._device))
+        image_features = image_features.detach().cpu().numpy().astype(np.float16)
+        p_pred = predict_proba(image_features, self.cpu_p_weights, self.cpu_p_biases)
+        w_pred = predict_proba(image_features, self.cpu_w_weights, self.cpu_w_biases)
+        print(f"p_pred = {p_pred}, w_pred = {w_pred}") if self.verbose else None
+        query = p_pred > self.p_threshold
+        if query.sum() > 0:
+            print(f"Hit for p_threshold: {p_pred}") if self.verbose else None
+            images[query] = T.GaussianBlur(99, sigma=(100.0, 100.0))(images[query])
+        query = w_pred > self.w_threshold
+        if query.sum() > 0:
+            print(f"Hit for w_threshold: {w_pred}") if self.verbose else None
+            images[query] = T.GaussianBlur(99, sigma=(100.0, 100.0))(images[query])
+        return images
+
+
+def load_img(path: str) -> torch.Tensor:
+    image = Image.open(path)
+    if not image.mode == "RGB":
+        image = image.convert("RGB")
+    image_transforms = T.Compose(
+        [
+            T.ToTensor(),
+        ]
+    )
+    return image_transforms(image)[None, ...]
+
+
+def test(root):
+    from einops import rearrange
+
+    filter = DeepFloydDataFiltering(verbose=True)
+    for p in os.listdir((root)):
+        print(f"running on {p}...")
+        img = load_img(os.path.join(root, p))
+        filtered_img = filter(img)
+        filtered_img = rearrange(
+            255.0 * (filtered_img.numpy())[0], "c h w -> h w c"
+        ).astype(np.uint8)
+        Image.fromarray(filtered_img).save(
+            os.path.join(root, f"{os.path.splitext(p)[0]}-filtered.jpg")
+        )
+
+
+if __name__ == "__main__":
+    import fire
+
+    fire.Fire(test)
+    print("done.")

scripts/util/detection/p_head_v1.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4653a64d5f85d8d4c5f6c5ec175f1c5c5e37db8f38d39b2ed8b5979da7fdc76
+size 3588

scripts/util/detection/w_head_v1.npz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6af23687aa347073e692025f405ccc48c14aadc5dbe775b3312041006d496d1
+size 3588

sgm/__init__.py

@@ -0,0 +1,4 @@
+from .models import AutoencodingEngine, DiffusionEngine
+from .util import get_configs_path, instantiate_from_config
+
+__version__ = "0.1.0"

sgm/data/__init__.py

@@ -0,0 +1 @@
+from .dataset import StableDataModuleFromConfig

sgm/data/cifar10.py

@@ -0,0 +1,67 @@
+import pytorch_lightning as pl
+import torchvision
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+
+
+class CIFAR10DataDictWrapper(Dataset):
+    def __init__(self, dset):
+        super().__init__()
+        self.dset = dset
+
+    def __getitem__(self, i):
+        x, y = self.dset[i]
+        return {"jpg": x, "cls": y}
+
+    def __len__(self):
+        return len(self.dset)
+
+
+class CIFAR10Loader(pl.LightningDataModule):
+    def __init__(self, batch_size, num_workers=0, shuffle=True):
+        super().__init__()
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+        )
+
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.shuffle = shuffle
+        self.train_dataset = CIFAR10DataDictWrapper(
+            torchvision.datasets.CIFAR10(
+                root=".data/", train=True, download=True, transform=transform
+            )
+        )
+        self.test_dataset = CIFAR10DataDictWrapper(
+            torchvision.datasets.CIFAR10(
+                root=".data/", train=False, download=True, transform=transform
+            )
+        )
+
+    def prepare_data(self):
+        pass
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+        )

sgm/data/dataset.py

@@ -0,0 +1,80 @@
+from typing import Optional
+
+import torchdata.datapipes.iter
+import webdataset as wds
+from omegaconf import DictConfig
+from pytorch_lightning import LightningDataModule
+
+try:
+    from sdata import create_dataset, create_dummy_dataset, create_loader
+except ImportError as e:
+    print("#" * 100)
+    print("Datasets not yet available")
+    print("to enable, we need to add stable-datasets as a submodule")
+    print("please use ``git submodule update --init --recursive``")
+    print("and do ``pip install -e stable-datasets/`` from the root of this repo")
+    print("#" * 100)
+    exit(1)
+
+
+class StableDataModuleFromConfig(LightningDataModule):
+    def __init__(
+        self,
+        train: DictConfig,
+        validation: Optional[DictConfig] = None,
+        test: Optional[DictConfig] = None,
+        skip_val_loader: bool = False,
+        dummy: bool = False,
+    ):
+        super().__init__()
+        self.train_config = train
+        assert (
+            "datapipeline" in self.train_config and "loader" in self.train_config
+        ), "train config requires the fields `datapipeline` and `loader`"
+
+        self.val_config = validation
+        if not skip_val_loader:
+            if self.val_config is not None:
+                assert (
+                    "datapipeline" in self.val_config and "loader" in self.val_config
+                ), "validation config requires the fields `datapipeline` and `loader`"
+            else:
+                print(
+                    "Warning: No Validation datapipeline defined, using that one from training"
+                )
+                self.val_config = train
+
+        self.test_config = test
+        if self.test_config is not None:
+            assert (
+                "datapipeline" in self.test_config and "loader" in self.test_config
+            ), "test config requires the fields `datapipeline` and `loader`"
+
+        self.dummy = dummy
+        if self.dummy:
+            print("#" * 100)
+            print("USING DUMMY DATASET: HOPE YOU'RE DEBUGGING ;)")
+            print("#" * 100)
+
+    def setup(self, stage: str) -> None:
+        print("Preparing datasets")
+        if self.dummy:
+            data_fn = create_dummy_dataset
+        else:
+            data_fn = create_dataset
+
+        self.train_datapipeline = data_fn(**self.train_config.datapipeline)
+        if self.val_config:
+            self.val_datapipeline = data_fn(**self.val_config.datapipeline)
+        if self.test_config:
+            self.test_datapipeline = data_fn(**self.test_config.datapipeline)
+
+    def train_dataloader(self) -> torchdata.datapipes.iter.IterDataPipe:
+        loader = create_loader(self.train_datapipeline, **self.train_config.loader)
+        return loader
+
+    def val_dataloader(self) -> wds.DataPipeline:
+        return create_loader(self.val_datapipeline, **self.val_config.loader)
+
+    def test_dataloader(self) -> wds.DataPipeline:
+        return create_loader(self.test_datapipeline, **self.test_config.loader)

sgm/data/mnist.py

@@ -0,0 +1,85 @@
+import pytorch_lightning as pl
+import torchvision
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+
+
+class MNISTDataDictWrapper(Dataset):
+    def __init__(self, dset):
+        super().__init__()
+        self.dset = dset
+
+    def __getitem__(self, i):
+        x, y = self.dset[i]
+        return {"jpg": x, "cls": y}
+
+    def __len__(self):
+        return len(self.dset)
+
+
+class MNISTLoader(pl.LightningDataModule):
+    def __init__(self, batch_size, num_workers=0, prefetch_factor=2, shuffle=True):
+        super().__init__()
+
+        transform = transforms.Compose(
+            [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+        )
+
+        self.batch_size = batch_size
+        self.num_workers = num_workers
+        self.prefetch_factor = prefetch_factor if num_workers > 0 else 0
+        self.shuffle = shuffle
+        self.train_dataset = MNISTDataDictWrapper(
+            torchvision.datasets.MNIST(
+                root=".data/", train=True, download=True, transform=transform
+            )
+        )
+        self.test_dataset = MNISTDataDictWrapper(
+            torchvision.datasets.MNIST(
+                root=".data/", train=False, download=True, transform=transform
+            )
+        )
+
+    def prepare_data(self):
+        pass
+
+    def train_dataloader(self):
+        return DataLoader(
+            self.train_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+    def test_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+    def val_dataloader(self):
+        return DataLoader(
+            self.test_dataset,
+            batch_size=self.batch_size,
+            shuffle=self.shuffle,
+            num_workers=self.num_workers,
+            prefetch_factor=self.prefetch_factor,
+        )
+
+
+if __name__ == "__main__":
+    dset = MNISTDataDictWrapper(
+        torchvision.datasets.MNIST(
+            root=".data/",
+            train=False,
+            download=True,
+            transform=transforms.Compose(
+                [transforms.ToTensor(), transforms.Lambda(lambda x: x * 2.0 - 1.0)]
+            ),
+        )
+    )
+    ex = dset[0]

sgm/inference/api.py

@@ -0,0 +1,386 @@
+import pathlib
+from dataclasses import asdict, dataclass
+from enum import Enum
+from typing import Optional
+
+from omegaconf import OmegaConf
+
+from sgm.inference.helpers import Img2ImgDiscretizationWrapper, do_img2img, do_sample
+from sgm.modules.diffusionmodules.sampling import (
+    DPMPP2MSampler,
+    DPMPP2SAncestralSampler,
+    EulerAncestralSampler,
+    EulerEDMSampler,
+    HeunEDMSampler,
+    LinearMultistepSampler,
+)
+from sgm.util import load_model_from_config
+
+
+class ModelArchitecture(str, Enum):
+    SD_2_1 = "stable-diffusion-v2-1"
+    SD_2_1_768 = "stable-diffusion-v2-1-768"
+    SDXL_V0_9_BASE = "stable-diffusion-xl-v0-9-base"
+    SDXL_V0_9_REFINER = "stable-diffusion-xl-v0-9-refiner"
+    SDXL_V1_BASE = "stable-diffusion-xl-v1-base"
+    SDXL_V1_REFINER = "stable-diffusion-xl-v1-refiner"
+
+
+class Sampler(str, Enum):
+    EULER_EDM = "EulerEDMSampler"
+    HEUN_EDM = "HeunEDMSampler"
+    EULER_ANCESTRAL = "EulerAncestralSampler"
+    DPMPP2S_ANCESTRAL = "DPMPP2SAncestralSampler"
+    DPMPP2M = "DPMPP2MSampler"
+    LINEAR_MULTISTEP = "LinearMultistepSampler"
+
+
+class Discretization(str, Enum):
+    LEGACY_DDPM = "LegacyDDPMDiscretization"
+    EDM = "EDMDiscretization"
+
+
+class Guider(str, Enum):
+    VANILLA = "VanillaCFG"
+    IDENTITY = "IdentityGuider"
+
+
+class Thresholder(str, Enum):
+    NONE = "None"
+
+
+@dataclass
+class SamplingParams:
+    width: int = 1024
+    height: int = 1024
+    steps: int = 50
+    sampler: Sampler = Sampler.DPMPP2M
+    discretization: Discretization = Discretization.LEGACY_DDPM
+    guider: Guider = Guider.VANILLA
+    thresholder: Thresholder = Thresholder.NONE
+    scale: float = 6.0
+    aesthetic_score: float = 5.0
+    negative_aesthetic_score: float = 5.0
+    img2img_strength: float = 1.0
+    orig_width: int = 1024
+    orig_height: int = 1024
+    crop_coords_top: int = 0
+    crop_coords_left: int = 0
+    sigma_min: float = 0.0292
+    sigma_max: float = 14.6146
+    rho: float = 3.0
+    s_churn: float = 0.0
+    s_tmin: float = 0.0
+    s_tmax: float = 999.0
+    s_noise: float = 1.0
+    eta: float = 1.0
+    order: int = 4
+
+
+@dataclass
+class SamplingSpec:
+    width: int
+    height: int
+    channels: int
+    factor: int
+    is_legacy: bool
+    config: str
+    ckpt: str
+    is_guided: bool
+
+
+model_specs = {
+    ModelArchitecture.SD_2_1: SamplingSpec(
+        height=512,
+        width=512,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_2_1.yaml",
+        ckpt="v2-1_512-ema-pruned.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SD_2_1_768: SamplingSpec(
+        height=768,
+        width=768,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_2_1_768.yaml",
+        ckpt="v2-1_768-ema-pruned.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V0_9_BASE: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=False,
+        config="sd_xl_base.yaml",
+        ckpt="sd_xl_base_0.9.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V0_9_REFINER: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_xl_refiner.yaml",
+        ckpt="sd_xl_refiner_0.9.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V1_BASE: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=False,
+        config="sd_xl_base.yaml",
+        ckpt="sd_xl_base_1.0.safetensors",
+        is_guided=True,
+    ),
+    ModelArchitecture.SDXL_V1_REFINER: SamplingSpec(
+        height=1024,
+        width=1024,
+        channels=4,
+        factor=8,
+        is_legacy=True,
+        config="sd_xl_refiner.yaml",
+        ckpt="sd_xl_refiner_1.0.safetensors",
+        is_guided=True,
+    ),
+}
+
+
+class SamplingPipeline:
+    def __init__(
+        self,
+        model_id: ModelArchitecture,
+        model_path="checkpoints",
+        config_path="configs/inference",
+        device="cuda",
+        use_fp16=True,
+    ) -> None:
+        if model_id not in model_specs:
+            raise ValueError(f"Model {model_id} not supported")
+        self.model_id = model_id
+        self.specs = model_specs[self.model_id]
+        self.config = str(pathlib.Path(config_path, self.specs.config))
+        self.ckpt = str(pathlib.Path(model_path, self.specs.ckpt))
+        self.device = device
+        self.model = self._load_model(device=device, use_fp16=use_fp16)
+
+    def _load_model(self, device="cuda", use_fp16=True):
+        config = OmegaConf.load(self.config)
+        model = load_model_from_config(config, self.ckpt)
+        if model is None:
+            raise ValueError(f"Model {self.model_id} could not be loaded")
+        model.to(device)
+        if use_fp16:
+            model.conditioner.half()
+            model.model.half()
+        return model
+
+    def text_to_image(
+        self,
+        params: SamplingParams,
+        prompt: str,
+        negative_prompt: str = "",
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+        value_dict = asdict(params)
+        value_dict["prompt"] = prompt
+        value_dict["negative_prompt"] = negative_prompt
+        value_dict["target_width"] = params.width
+        value_dict["target_height"] = params.height
+        return do_sample(
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            params.height,
+            params.width,
+            self.specs.channels,
+            self.specs.factor,
+            force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
+            return_latents=return_latents,
+            filter=None,
+        )
+
+    def image_to_image(
+        self,
+        params: SamplingParams,
+        image,
+        prompt: str,
+        negative_prompt: str = "",
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+
+        if params.img2img_strength < 1.0:
+            sampler.discretization = Img2ImgDiscretizationWrapper(
+                sampler.discretization,
+                strength=params.img2img_strength,
+            )
+        height, width = image.shape[2], image.shape[3]
+        value_dict = asdict(params)
+        value_dict["prompt"] = prompt
+        value_dict["negative_prompt"] = negative_prompt
+        value_dict["target_width"] = width
+        value_dict["target_height"] = height
+        return do_img2img(
+            image,
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
+            return_latents=return_latents,
+            filter=None,
+        )
+
+    def refiner(
+        self,
+        params: SamplingParams,
+        image,
+        prompt: str,
+        negative_prompt: Optional[str] = None,
+        samples: int = 1,
+        return_latents: bool = False,
+    ):
+        sampler = get_sampler_config(params)
+        value_dict = {
+            "orig_width": image.shape[3] * 8,
+            "orig_height": image.shape[2] * 8,
+            "target_width": image.shape[3] * 8,
+            "target_height": image.shape[2] * 8,
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "crop_coords_top": 0,
+            "crop_coords_left": 0,
+            "aesthetic_score": 6.0,
+            "negative_aesthetic_score": 2.5,
+        }
+
+        return do_img2img(
+            image,
+            self.model,
+            sampler,
+            value_dict,
+            samples,
+            skip_encode=True,
+            return_latents=return_latents,
+            filter=None,
+        )
+
+
+def get_guider_config(params: SamplingParams):
+    if params.guider == Guider.IDENTITY:
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
+        }
+    elif params.guider == Guider.VANILLA:
+        scale = params.scale
+
+        thresholder = params.thresholder
+
+        if thresholder == Thresholder.NONE:
+            dyn_thresh_config = {
+                "target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
+            }
+        else:
+            raise NotImplementedError
+
+        guider_config = {
+            "target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
+            "params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
+        }
+    else:
+        raise NotImplementedError
+    return guider_config
+
+
+def get_discretization_config(params: SamplingParams):
+    if params.discretization == Discretization.LEGACY_DDPM:
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
+        }
+    elif params.discretization == Discretization.EDM:
+        discretization_config = {
+            "target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
+            "params": {
+                "sigma_min": params.sigma_min,
+                "sigma_max": params.sigma_max,
+                "rho": params.rho,
+            },
+        }
+    else:
+        raise ValueError(f"unknown discretization {params.discretization}")
+    return discretization_config
+
+
+def get_sampler_config(params: SamplingParams):
+    discretization_config = get_discretization_config(params)
+    guider_config = get_guider_config(params)
+    sampler = None
+    if params.sampler == Sampler.EULER_EDM:
+        return EulerEDMSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            s_churn=params.s_churn,
+            s_tmin=params.s_tmin,
+            s_tmax=params.s_tmax,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.HEUN_EDM:
+        return HeunEDMSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            s_churn=params.s_churn,
+            s_tmin=params.s_tmin,
+            s_tmax=params.s_tmax,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.EULER_ANCESTRAL:
+        return EulerAncestralSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            eta=params.eta,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.DPMPP2S_ANCESTRAL:
+        return DPMPP2SAncestralSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            eta=params.eta,
+            s_noise=params.s_noise,
+            verbose=True,
+        )
+    if params.sampler == Sampler.DPMPP2M:
+        return DPMPP2MSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            verbose=True,
+        )
+    if params.sampler == Sampler.LINEAR_MULTISTEP:
+        return LinearMultistepSampler(
+            num_steps=params.steps,
+            discretization_config=discretization_config,
+            guider_config=guider_config,
+            order=params.order,
+            verbose=True,
+        )
+
+    raise ValueError(f"unknown sampler {params.sampler}!")

sgm/inference/helpers.py

@@ -0,0 +1,305 @@
+import math
+import os
+from typing import List, Optional, Union
+
+import numpy as np
+import torch
+from einops import rearrange
+from imwatermark import WatermarkEncoder
+from omegaconf import ListConfig
+from PIL import Image
+from torch import autocast
+
+from sgm.util import append_dims
+
+
+class WatermarkEmbedder:
+    def __init__(self, watermark):
+        self.watermark = watermark
+        self.num_bits = len(WATERMARK_BITS)
+        self.encoder = WatermarkEncoder()
+        self.encoder.set_watermark("bits", self.watermark)
+
+    def __call__(self, image: torch.Tensor) -> torch.Tensor:
+        """
+        Adds a predefined watermark to the input image
+
+        Args:
+            image: ([N,] B, RGB, H, W) in range [0, 1]
+
+        Returns:
+            same as input but watermarked
+        """
+        squeeze = len(image.shape) == 4
+        if squeeze:
+            image = image[None, ...]
+        n = image.shape[0]
+        image_np = rearrange(
+            (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
+        ).numpy()[:, :, :, ::-1]
+        # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
+        # watermarking libary expects input as cv2 BGR format
+        for k in range(image_np.shape[0]):
+            image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
+        image = torch.from_numpy(
+            rearrange(image_np[:, :, :, ::-1], "(n b) h w c -> n b c h w", n=n)
+        ).to(image.device)
+        image = torch.clamp(image / 255, min=0.0, max=1.0)
+        if squeeze:
+            image = image[0]
+        return image
+
+
+# A fixed 48-bit message that was choosen at random
+# WATERMARK_MESSAGE = 0xB3EC907BB19E
+WATERMARK_MESSAGE = 0b101100111110110010010000011110111011000110011110
+# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
+WATERMARK_BITS = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
+embed_watermark = WatermarkEmbedder(WATERMARK_BITS)
+
+
+def get_unique_embedder_keys_from_conditioner(conditioner):
+    return list({x.input_key for x in conditioner.embedders})
+
+
+def perform_save_locally(save_path, samples):
+    os.makedirs(os.path.join(save_path), exist_ok=True)
+    base_count = len(os.listdir(os.path.join(save_path)))
+    samples = embed_watermark(samples)
+    for sample in samples:
+        sample = 255.0 * rearrange(sample.cpu().numpy(), "c h w -> h w c")
+        Image.fromarray(sample.astype(np.uint8)).save(
+            os.path.join(save_path, f"{base_count:09}.png")
+        )
+        base_count += 1
+
+
+class Img2ImgDiscretizationWrapper:
+    """
+    wraps a discretizer, and prunes the sigmas
+    params:
+        strength: float between 0.0 and 1.0. 1.0 means full sampling (all sigmas are returned)
+    """
+
+    def __init__(self, discretization, strength: float = 1.0):
+        self.discretization = discretization
+        self.strength = strength
+        assert 0.0 <= self.strength <= 1.0
+
+    def __call__(self, *args, **kwargs):
+        # sigmas start large first, and decrease then
+        sigmas = self.discretization(*args, **kwargs)
+        print(f"sigmas after discretization, before pruning img2img: ", sigmas)
+        sigmas = torch.flip(sigmas, (0,))
+        sigmas = sigmas[: max(int(self.strength * len(sigmas)), 1)]
+        print("prune index:", max(int(self.strength * len(sigmas)), 1))
+        sigmas = torch.flip(sigmas, (0,))
+        print(f"sigmas after pruning: ", sigmas)
+        return sigmas
+
+
+def do_sample(
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    H,
+    W,
+    C,
+    F,
+    force_uc_zero_embeddings: Optional[List] = None,
+    batch2model_input: Optional[List] = None,
+    return_latents=False,
+    filter=None,
+    device="cuda",
+):
+    if force_uc_zero_embeddings is None:
+        force_uc_zero_embeddings = []
+    if batch2model_input is None:
+        batch2model_input = []
+
+    with torch.no_grad():
+        with autocast(device) as precision_scope:
+            with model.ema_scope():
+                num_samples = [num_samples]
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    num_samples,
+                )
+                for key in batch:
+                    if isinstance(batch[key], torch.Tensor):
+                        print(key, batch[key].shape)
+                    elif isinstance(batch[key], list):
+                        print(key, [len(l) for l in batch[key]])
+                    else:
+                        print(key, batch[key])
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                )
+
+                for k in c:
+                    if not k == "crossattn":
+                        c[k], uc[k] = map(
+                            lambda y: y[k][: math.prod(num_samples)].to(device), (c, uc)
+                        )
+
+                additional_model_inputs = {}
+                for k in batch2model_input:
+                    additional_model_inputs[k] = batch[k]
+
+                shape = (math.prod(num_samples), C, H // F, W // F)
+                randn = torch.randn(shape).to(device)
+
+                def denoiser(input, sigma, c):
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
+
+                samples_z = sampler(denoiser, randn, cond=c, uc=uc)
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if return_latents:
+                    return samples, samples_z
+                return samples
+
+
+def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
+    # Hardcoded demo setups; might undergo some changes in the future
+
+    batch = {}
+    batch_uc = {}
+
+    for key in keys:
+        if key == "txt":
+            batch["txt"] = (
+                np.repeat([value_dict["prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+            batch_uc["txt"] = (
+                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
+            )
+        elif key == "original_size_as_tuple":
+            batch["original_size_as_tuple"] = (
+                torch.tensor([value_dict["orig_height"], value_dict["orig_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "crop_coords_top_left":
+            batch["crop_coords_top_left"] = (
+                torch.tensor(
+                    [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
+                )
+                .to(device)
+                .repeat(*N, 1)
+            )
+        elif key == "aesthetic_score":
+            batch["aesthetic_score"] = (
+                torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1)
+            )
+            batch_uc["aesthetic_score"] = (
+                torch.tensor([value_dict["negative_aesthetic_score"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+
+        elif key == "target_size_as_tuple":
+            batch["target_size_as_tuple"] = (
+                torch.tensor([value_dict["target_height"], value_dict["target_width"]])
+                .to(device)
+                .repeat(*N, 1)
+            )
+        else:
+            batch[key] = value_dict[key]
+
+    for key in batch.keys():
+        if key not in batch_uc and isinstance(batch[key], torch.Tensor):
+            batch_uc[key] = torch.clone(batch[key])
+    return batch, batch_uc
+
+
+def get_input_image_tensor(image: Image.Image, device="cuda"):
+    w, h = image.size
+    print(f"loaded input image of size ({w}, {h})")
+    width, height = map(
+        lambda x: x - x % 64, (w, h)
+    )  # resize to integer multiple of 64
+    image = image.resize((width, height))
+    image_array = np.array(image.convert("RGB"))
+    image_array = image_array[None].transpose(0, 3, 1, 2)
+    image_tensor = torch.from_numpy(image_array).to(dtype=torch.float32) / 127.5 - 1.0
+    return image_tensor.to(device)
+
+
+def do_img2img(
+    img,
+    model,
+    sampler,
+    value_dict,
+    num_samples,
+    force_uc_zero_embeddings=[],
+    additional_kwargs={},
+    offset_noise_level: float = 0.0,
+    return_latents=False,
+    skip_encode=False,
+    filter=None,
+    device="cuda",
+):
+    with torch.no_grad():
+        with autocast(device) as precision_scope:
+            with model.ema_scope():
+                batch, batch_uc = get_batch(
+                    get_unique_embedder_keys_from_conditioner(model.conditioner),
+                    value_dict,
+                    [num_samples],
+                )
+                c, uc = model.conditioner.get_unconditional_conditioning(
+                    batch,
+                    batch_uc=batch_uc,
+                    force_uc_zero_embeddings=force_uc_zero_embeddings,
+                )
+
+                for k in c:
+                    c[k], uc[k] = map(lambda y: y[k][:num_samples].to(device), (c, uc))
+
+                for k in additional_kwargs:
+                    c[k] = uc[k] = additional_kwargs[k]
+                if skip_encode:
+                    z = img
+                else:
+                    z = model.encode_first_stage(img)
+                noise = torch.randn_like(z)
+                sigmas = sampler.discretization(sampler.num_steps)
+                sigma = sigmas[0].to(z.device)
+
+                if offset_noise_level > 0.0:
+                    noise = noise + offset_noise_level * append_dims(
+                        torch.randn(z.shape[0], device=z.device), z.ndim
+                    )
+                noised_z = z + noise * append_dims(sigma, z.ndim)
+                noised_z = noised_z / torch.sqrt(
+                    1.0 + sigmas[0] ** 2.0
+                )  # Note: hardcoded to DDPM-like scaling. need to generalize later.
+
+                def denoiser(x, sigma, c):
+                    return model.denoiser(model.model, x, sigma, c)
+
+                samples_z = sampler(denoiser, noised_z, cond=c, uc=uc)
+                samples_x = model.decode_first_stage(samples_z)
+                samples = torch.clamp((samples_x + 1.0) / 2.0, min=0.0, max=1.0)
+
+                if filter is not None:
+                    samples = filter(samples)
+
+                if return_latents:
+                    return samples, samples_z
+                return samples

sgm/lr_scheduler.py

@@ -0,0 +1,135 @@
+import numpy as np
+
+
+class LambdaWarmUpCosineScheduler:
+    """
+    note: use with a base_lr of 1.0
+    """
+
+    def __init__(
+        self,
+        warm_up_steps,
+        lr_min,
+        lr_max,
+        lr_start,
+        max_decay_steps,
+        verbosity_interval=0,
+    ):
+        self.lr_warm_up_steps = warm_up_steps
+        self.lr_start = lr_start
+        self.lr_min = lr_min
+        self.lr_max = lr_max
+        self.lr_max_decay_steps = max_decay_steps
+        self.last_lr = 0.0
+        self.verbosity_interval = verbosity_interval
+
+    def schedule(self, n, **kwargs):
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
+        if n < self.lr_warm_up_steps:
+            lr = (
+                self.lr_max - self.lr_start
+            ) / self.lr_warm_up_steps * n + self.lr_start
+            self.last_lr = lr
+            return lr
+        else:
+            t = (n - self.lr_warm_up_steps) / (
+                self.lr_max_decay_steps - self.lr_warm_up_steps
+            )
+            t = min(t, 1.0)
+            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
+                1 + np.cos(t * np.pi)
+            )
+            self.last_lr = lr
+            return lr
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n, **kwargs)
+
+
+class LambdaWarmUpCosineScheduler2:
+    """
+    supports repeated iterations, configurable via lists
+    note: use with a base_lr of 1.0.
+    """
+
+    def __init__(
+        self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0
+    ):
+        assert (
+            len(warm_up_steps)
+            == len(f_min)
+            == len(f_max)
+            == len(f_start)
+            == len(cycle_lengths)
+        )
+        self.lr_warm_up_steps = warm_up_steps
+        self.f_start = f_start
+        self.f_min = f_min
+        self.f_max = f_max
+        self.cycle_lengths = cycle_lengths
+        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
+        self.last_f = 0.0
+        self.verbosity_interval = verbosity_interval
+
+    def find_in_interval(self, n):
+        interval = 0
+        for cl in self.cum_cycles[1:]:
+            if n <= cl:
+                return interval
+            interval += 1
+
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(
+                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                    f"current cycle {cycle}"
+                )
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
+                cycle
+            ] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            t = (n - self.lr_warm_up_steps[cycle]) / (
+                self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
+            )
+            t = min(t, 1.0)
+            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
+                1 + np.cos(t * np.pi)
+            )
+            self.last_f = f
+            return f
+
+    def __call__(self, n, **kwargs):
+        return self.schedule(n, **kwargs)
+
+
+class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
+    def schedule(self, n, **kwargs):
+        cycle = self.find_in_interval(n)
+        n = n - self.cum_cycles[cycle]
+        if self.verbosity_interval > 0:
+            if n % self.verbosity_interval == 0:
+                print(
+                    f"current step: {n}, recent lr-multiplier: {self.last_f}, "
+                    f"current cycle {cycle}"
+                )
+
+        if n < self.lr_warm_up_steps[cycle]:
+            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[
+                cycle
+            ] * n + self.f_start[cycle]
+            self.last_f = f
+            return f
+        else:
+            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
+                self.cycle_lengths[cycle] - n
+            ) / (self.cycle_lengths[cycle])
+            self.last_f = f
+            return f

sgm/models/__init__.py

@@ -0,0 +1,2 @@
+from .autoencoder import AutoencodingEngine
+from .diffusion import DiffusionEngine

sgm/models/autoencoder.py

@@ -0,0 +1,619 @@
+import logging
+import math
+import re
+from abc import abstractmethod
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import pytorch_lightning as pl
+import torch
+import torch.nn as nn
+from einops import rearrange
+from packaging import version
+
+from ..modules.autoencoding.regularizers import AbstractRegularizer
+from ..modules.ema import LitEma
+from ..util import (
+    default,
+    get_nested_attribute,
+    get_obj_from_str,
+    instantiate_from_config,
+)
+
+logpy = logging.getLogger(__name__)
+
+
+class AbstractAutoencoder(pl.LightningModule):
+    """
+    This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators,
+    unCLIP models, etc. Hence, it is fairly general, and specific features
+    (e.g. discriminator training, encoding, decoding) must be implemented in subclasses.
+    """
+
+    def __init__(
+        self,
+        ema_decay: Union[None, float] = None,
+        monitor: Union[None, str] = None,
+        input_key: str = "jpg",
+    ):
+        super().__init__()
+
+        self.input_key = input_key
+        self.use_ema = ema_decay is not None
+        if monitor is not None:
+            self.monitor = monitor
+
+        if self.use_ema:
+            self.model_ema = LitEma(self, decay=ema_decay)
+            logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if version.parse(torch.__version__) >= version.parse("2.0.0"):
+            self.automatic_optimization = False
+
+    def apply_ckpt(self, ckpt: Union[None, str, dict]):
+        if ckpt is None:
+            return
+        if isinstance(ckpt, str):
+            ckpt = {
+                "target": "sgm.modules.checkpoint.CheckpointEngine",
+                "params": {"ckpt_path": ckpt},
+            }
+        engine = instantiate_from_config(ckpt)
+        engine(self)
+
+    @abstractmethod
+    def get_input(self, batch) -> Any:
+        raise NotImplementedError()
+
+    def on_train_batch_end(self, *args, **kwargs):
+        # for EMA computation
+        if self.use_ema:
+            self.model_ema(self)
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.parameters())
+            self.model_ema.copy_to(self)
+            if context is not None:
+                logpy.info(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.parameters())
+                if context is not None:
+                    logpy.info(f"{context}: Restored training weights")
+
+    @abstractmethod
+    def encode(self, *args, **kwargs) -> torch.Tensor:
+        raise NotImplementedError("encode()-method of abstract base class called")
+
+    @abstractmethod
+    def decode(self, *args, **kwargs) -> torch.Tensor:
+        raise NotImplementedError("decode()-method of abstract base class called")
+
+    def instantiate_optimizer_from_config(self, params, lr, cfg):
+        logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config")
+        return get_obj_from_str(cfg["target"])(
+            params, lr=lr, **cfg.get("params", dict())
+        )
+
+    def configure_optimizers(self) -> Any:
+        raise NotImplementedError()
+
+
+class AutoencodingEngine(AbstractAutoencoder):
+    """
+    Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL
+    (we also restore them explicitly as special cases for legacy reasons).
+    Regularizations such as KL or VQ are moved to the regularizer class.
+    """
+
+    def __init__(
+        self,
+        *args,
+        encoder_config: Dict,
+        decoder_config: Dict,
+        loss_config: Dict,
+        regularizer_config: Dict,
+        optimizer_config: Union[Dict, None] = None,
+        lr_g_factor: float = 1.0,
+        trainable_ae_params: Optional[List[List[str]]] = None,
+        ae_optimizer_args: Optional[List[dict]] = None,
+        trainable_disc_params: Optional[List[List[str]]] = None,
+        disc_optimizer_args: Optional[List[dict]] = None,
+        disc_start_iter: int = 0,
+        diff_boost_factor: float = 3.0,
+        ckpt_engine: Union[None, str, dict] = None,
+        ckpt_path: Optional[str] = None,
+        additional_decode_keys: Optional[List[str]] = None,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.automatic_optimization = False  # pytorch lightning
+
+        self.encoder: torch.nn.Module = instantiate_from_config(encoder_config)
+        self.decoder: torch.nn.Module = instantiate_from_config(decoder_config)
+        self.loss: torch.nn.Module = instantiate_from_config(loss_config)
+        self.regularization: AbstractRegularizer = instantiate_from_config(
+            regularizer_config
+        )
+        self.optimizer_config = default(
+            optimizer_config, {"target": "torch.optim.Adam"}
+        )
+        self.diff_boost_factor = diff_boost_factor
+        self.disc_start_iter = disc_start_iter
+        self.lr_g_factor = lr_g_factor
+        self.trainable_ae_params = trainable_ae_params
+        if self.trainable_ae_params is not None:
+            self.ae_optimizer_args = default(
+                ae_optimizer_args,
+                [{} for _ in range(len(self.trainable_ae_params))],
+            )
+            assert len(self.ae_optimizer_args) == len(self.trainable_ae_params)
+        else:
+            self.ae_optimizer_args = [{}]  # makes type consitent
+
+        self.trainable_disc_params = trainable_disc_params
+        if self.trainable_disc_params is not None:
+            self.disc_optimizer_args = default(
+                disc_optimizer_args,
+                [{} for _ in range(len(self.trainable_disc_params))],
+            )
+            assert len(self.disc_optimizer_args) == len(self.trainable_disc_params)
+        else:
+            self.disc_optimizer_args = [{}]  # makes type consitent
+
+        if ckpt_path is not None:
+            assert ckpt_engine is None, "Can't set ckpt_engine and ckpt_path"
+            logpy.warn("Checkpoint path is deprecated, use `checkpoint_egnine` instead")
+        self.apply_ckpt(default(ckpt_path, ckpt_engine))
+        self.additional_decode_keys = set(default(additional_decode_keys, []))
+
+    def get_input(self, batch: Dict) -> torch.Tensor:
+        # assuming unified data format, dataloader returns a dict.
+        # image tensors should be scaled to -1 ... 1 and in channels-first
+        # format (e.g., bchw instead if bhwc)
+        return batch[self.input_key]
+
+    def get_autoencoder_params(self) -> list:
+        params = []
+        if hasattr(self.loss, "get_trainable_autoencoder_parameters"):
+            params += list(self.loss.get_trainable_autoencoder_parameters())
+        if hasattr(self.regularization, "get_trainable_parameters"):
+            params += list(self.regularization.get_trainable_parameters())
+        params = params + list(self.encoder.parameters())
+        params = params + list(self.decoder.parameters())
+        return params
+
+    def get_discriminator_params(self) -> list:
+        if hasattr(self.loss, "get_trainable_parameters"):
+            params = list(self.loss.get_trainable_parameters())  # e.g., discriminator
+        else:
+            params = []
+        return params
+
+    def get_last_layer(self):
+        return self.decoder.get_last_layer()
+
+    def encode(
+        self,
+        x: torch.Tensor,
+        return_reg_log: bool = False,
+        unregularized: bool = False,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
+        z = self.encoder(x)
+        if unregularized:
+            return z, dict()
+        z, reg_log = self.regularization(z)
+        if return_reg_log:
+            return z, reg_log
+        return z
+
+    def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor:
+        x = self.decoder(z, **kwargs)
+        return x
+
+    def forward(
+        self, x: torch.Tensor, **additional_decode_kwargs
+    ) -> Tuple[torch.Tensor, torch.Tensor, dict]:
+        z, reg_log = self.encode(x, return_reg_log=True)
+        dec = self.decode(z, **additional_decode_kwargs)
+        return z, dec, reg_log
+
+    def inner_training_step(
+        self, batch: dict, batch_idx: int, optimizer_idx: int = 0
+    ) -> torch.Tensor:
+        x = self.get_input(batch)
+        additional_decode_kwargs = {
+            key: batch[key] for key in self.additional_decode_keys.intersection(batch)
+        }
+        z, xrec, regularization_log = self(x, **additional_decode_kwargs)
+        if hasattr(self.loss, "forward_keys"):
+            extra_info = {
+                "z": z,
+                "optimizer_idx": optimizer_idx,
+                "global_step": self.global_step,
+                "last_layer": self.get_last_layer(),
+                "split": "train",
+                "regularization_log": regularization_log,
+                "autoencoder": self,
+            }
+            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
+        else:
+            extra_info = dict()
+
+        if optimizer_idx == 0:
+            # autoencode
+            out_loss = self.loss(x, xrec, **extra_info)
+            if isinstance(out_loss, tuple):
+                aeloss, log_dict_ae = out_loss
+            else:
+                # simple loss function
+                aeloss = out_loss
+                log_dict_ae = {"train/loss/rec": aeloss.detach()}
+
+            self.log_dict(
+                log_dict_ae,
+                prog_bar=False,
+                logger=True,
+                on_step=True,
+                on_epoch=True,
+                sync_dist=False,
+            )
+            self.log(
+                "loss",
+                aeloss.mean().detach(),
+                prog_bar=True,
+                logger=False,
+                on_epoch=False,
+                on_step=True,
+            )
+            return aeloss
+        elif optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
+            # -> discriminator always needs to return a tuple
+            self.log_dict(
+                log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True
+            )
+            return discloss
+        else:
+            raise NotImplementedError(f"Unknown optimizer {optimizer_idx}")
+
+    def training_step(self, batch: dict, batch_idx: int):
+        opts = self.optimizers()
+        if not isinstance(opts, list):
+            # Non-adversarial case
+            opts = [opts]
+        optimizer_idx = batch_idx % len(opts)
+        if self.global_step < self.disc_start_iter:
+            optimizer_idx = 0
+        opt = opts[optimizer_idx]
+        opt.zero_grad()
+        with opt.toggle_model():
+            loss = self.inner_training_step(
+                batch, batch_idx, optimizer_idx=optimizer_idx
+            )
+            self.manual_backward(loss)
+        opt.step()
+
+    def validation_step(self, batch: dict, batch_idx: int) -> Dict:
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
+            log_dict.update(log_dict_ema)
+        return log_dict
+
+    def _validation_step(self, batch: dict, batch_idx: int, postfix: str = "") -> Dict:
+        x = self.get_input(batch)
+
+        z, xrec, regularization_log = self(x)
+        if hasattr(self.loss, "forward_keys"):
+            extra_info = {
+                "z": z,
+                "optimizer_idx": 0,
+                "global_step": self.global_step,
+                "last_layer": self.get_last_layer(),
+                "split": "val" + postfix,
+                "regularization_log": regularization_log,
+                "autoencoder": self,
+            }
+            extra_info = {k: extra_info[k] for k in self.loss.forward_keys}
+        else:
+            extra_info = dict()
+        out_loss = self.loss(x, xrec, **extra_info)
+        if isinstance(out_loss, tuple):
+            aeloss, log_dict_ae = out_loss
+        else:
+            # simple loss function
+            aeloss = out_loss
+            log_dict_ae = {f"val{postfix}/loss/rec": aeloss.detach()}
+        full_log_dict = log_dict_ae
+
+        if "optimizer_idx" in extra_info:
+            extra_info["optimizer_idx"] = 1
+            discloss, log_dict_disc = self.loss(x, xrec, **extra_info)
+            full_log_dict.update(log_dict_disc)
+        self.log(
+            f"val{postfix}/loss/rec",
+            log_dict_ae[f"val{postfix}/loss/rec"],
+            sync_dist=True,
+        )
+        self.log_dict(full_log_dict, sync_dist=True)
+        return full_log_dict
+
+    def get_param_groups(
+        self, parameter_names: List[List[str]], optimizer_args: List[dict]
+    ) -> Tuple[List[Dict[str, Any]], int]:
+        groups = []
+        num_params = 0
+        for names, args in zip(parameter_names, optimizer_args):
+            params = []
+            for pattern_ in names:
+                pattern_params = []
+                pattern = re.compile(pattern_)
+                for p_name, param in self.named_parameters():
+                    if re.match(pattern, p_name):
+                        pattern_params.append(param)
+                        num_params += param.numel()
+                if len(pattern_params) == 0:
+                    logpy.warn(f"Did not find parameters for pattern {pattern_}")
+                params.extend(pattern_params)
+            groups.append({"params": params, **args})
+        return groups, num_params
+
+    def configure_optimizers(self) -> List[torch.optim.Optimizer]:
+        if self.trainable_ae_params is None:
+            ae_params = self.get_autoencoder_params()
+        else:
+            ae_params, num_ae_params = self.get_param_groups(
+                self.trainable_ae_params, self.ae_optimizer_args
+            )
+            logpy.info(f"Number of trainable autoencoder parameters: {num_ae_params:,}")
+        if self.trainable_disc_params is None:
+            disc_params = self.get_discriminator_params()
+        else:
+            disc_params, num_disc_params = self.get_param_groups(
+                self.trainable_disc_params, self.disc_optimizer_args
+            )
+            logpy.info(
+                f"Number of trainable discriminator parameters: {num_disc_params:,}"
+            )
+        opt_ae = self.instantiate_optimizer_from_config(
+            ae_params,
+            default(self.lr_g_factor, 1.0) * self.learning_rate,
+            self.optimizer_config,
+        )
+        opts = [opt_ae]
+        if len(disc_params) > 0:
+            opt_disc = self.instantiate_optimizer_from_config(
+                disc_params, self.learning_rate, self.optimizer_config
+            )
+            opts.append(opt_disc)
+
+        return opts
+
+    @torch.no_grad()
+    def log_images(
+        self, batch: dict, additional_log_kwargs: Optional[Dict] = None, **kwargs
+    ) -> dict:
+        log = dict()
+        additional_decode_kwargs = {}
+        x = self.get_input(batch)
+        additional_decode_kwargs.update(
+            {key: batch[key] for key in self.additional_decode_keys.intersection(batch)}
+        )
+
+        _, xrec, _ = self(x, **additional_decode_kwargs)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        diff = 0.5 * torch.abs(torch.clamp(xrec, -1.0, 1.0) - x)
+        diff.clamp_(0, 1.0)
+        log["diff"] = 2.0 * diff - 1.0
+        # diff_boost shows location of small errors, by boosting their
+        # brightness.
+        log["diff_boost"] = (
+            2.0 * torch.clamp(self.diff_boost_factor * diff, 0.0, 1.0) - 1
+        )
+        if hasattr(self.loss, "log_images"):
+            log.update(self.loss.log_images(x, xrec))
+        with self.ema_scope():
+            _, xrec_ema, _ = self(x, **additional_decode_kwargs)
+            log["reconstructions_ema"] = xrec_ema
+            diff_ema = 0.5 * torch.abs(torch.clamp(xrec_ema, -1.0, 1.0) - x)
+            diff_ema.clamp_(0, 1.0)
+            log["diff_ema"] = 2.0 * diff_ema - 1.0
+            log["diff_boost_ema"] = (
+                2.0 * torch.clamp(self.diff_boost_factor * diff_ema, 0.0, 1.0) - 1
+            )
+        if additional_log_kwargs:
+            additional_decode_kwargs.update(additional_log_kwargs)
+            _, xrec_add, _ = self(x, **additional_decode_kwargs)
+            log_str = "reconstructions-" + "-".join(
+                [f"{key}={additional_log_kwargs[key]}" for key in additional_log_kwargs]
+            )
+            log[log_str] = xrec_add
+        return log
+
+
+class AutoencodingEngineLegacy(AutoencodingEngine):
+    def __init__(self, embed_dim: int, **kwargs):
+        self.max_batch_size = kwargs.pop("max_batch_size", None)
+        ddconfig = kwargs.pop("ddconfig")
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ckpt_engine = kwargs.pop("ckpt_engine", None)
+        super().__init__(
+            encoder_config={
+                "target": "sgm.modules.diffusionmodules.model.Encoder",
+                "params": ddconfig,
+            },
+            decoder_config={
+                "target": "sgm.modules.diffusionmodules.model.Decoder",
+                "params": ddconfig,
+            },
+            **kwargs,
+        )
+        self.quant_conv = torch.nn.Conv2d(
+            (1 + ddconfig["double_z"]) * ddconfig["z_channels"],
+            (1 + ddconfig["double_z"]) * embed_dim,
+            1,
+        )
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        self.embed_dim = embed_dim
+
+        self.apply_ckpt(default(ckpt_path, ckpt_engine))
+
+    def get_autoencoder_params(self) -> list:
+        params = super().get_autoencoder_params()
+        return params
+
+    def encode(
+        self, x: torch.Tensor, return_reg_log: bool = False
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]:
+        if self.max_batch_size is None:
+            z = self.encoder(x)
+            z = self.quant_conv(z)
+        else:
+            N = x.shape[0]
+            bs = self.max_batch_size
+            n_batches = int(math.ceil(N / bs))
+            z = list()
+            for i_batch in range(n_batches):
+                z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs])
+                z_batch = self.quant_conv(z_batch)
+                z.append(z_batch)
+            z = torch.cat(z, 0)
+
+        z, reg_log = self.regularization(z)
+        if return_reg_log:
+            return z, reg_log
+        return z
+
+    def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor:
+        if self.max_batch_size is None:
+            dec = self.post_quant_conv(z)
+            dec = self.decoder(dec, **decoder_kwargs)
+        else:
+            N = z.shape[0]
+            bs = self.max_batch_size
+            n_batches = int(math.ceil(N / bs))
+            dec = list()
+            for i_batch in range(n_batches):
+                dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs])
+                dec_batch = self.decoder(dec_batch, **decoder_kwargs)
+                dec.append(dec_batch)
+            dec = torch.cat(dec, 0)
+
+        return dec
+
+
+class AutoencoderKL(AutoencodingEngineLegacy):
+    def __init__(self, **kwargs):
+        if "lossconfig" in kwargs:
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers"
+                    ".DiagonalGaussianRegularizer"
+                )
+            },
+            **kwargs,
+        )
+
+
+class AutoencoderLegacyVQ(AutoencodingEngineLegacy):
+    def __init__(
+        self,
+        embed_dim: int,
+        n_embed: int,
+        sane_index_shape: bool = False,
+        **kwargs,
+    ):
+        if "lossconfig" in kwargs:
+            logpy.warn(f"Parameter `lossconfig` is deprecated, use `loss_config`.")
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers.quantize" ".VectorQuantizer"
+                ),
+                "params": {
+                    "n_e": n_embed,
+                    "e_dim": embed_dim,
+                    "sane_index_shape": sane_index_shape,
+                },
+            },
+            **kwargs,
+        )
+
+
+class IdentityFirstStage(AbstractAutoencoder):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def get_input(self, x: Any) -> Any:
+        return x
+
+    def encode(self, x: Any, *args, **kwargs) -> Any:
+        return x
+
+    def decode(self, x: Any, *args, **kwargs) -> Any:
+        return x
+
+
+class AEIntegerWrapper(nn.Module):
+    def __init__(
+        self,
+        model: nn.Module,
+        shape: Union[None, Tuple[int, int], List[int]] = (16, 16),
+        regularization_key: str = "regularization",
+        encoder_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        super().__init__()
+        self.model = model
+        assert hasattr(model, "encode") and hasattr(
+            model, "decode"
+        ), "Need AE interface"
+        self.regularization = get_nested_attribute(model, regularization_key)
+        self.shape = shape
+        self.encoder_kwargs = default(encoder_kwargs, {"return_reg_log": True})
+
+    def encode(self, x) -> torch.Tensor:
+        assert (
+            not self.training
+        ), f"{self.__class__.__name__} only supports inference currently"
+        _, log = self.model.encode(x, **self.encoder_kwargs)
+        assert isinstance(log, dict)
+        inds = log["min_encoding_indices"]
+        return rearrange(inds, "b ... -> b (...)")
+
+    def decode(
+        self, inds: torch.Tensor, shape: Union[None, tuple, list] = None
+    ) -> torch.Tensor:
+        # expect inds shape (b, s) with s = h*w
+        shape = default(shape, self.shape)  # Optional[(h, w)]
+        if shape is not None:
+            assert len(shape) == 2, f"Unhandeled shape {shape}"
+            inds = rearrange(inds, "b (h w) -> b h w", h=shape[0], w=shape[1])
+        h = self.regularization.get_codebook_entry(inds)  # (b, h, w, c)
+        h = rearrange(h, "b h w c -> b c h w")
+        return self.model.decode(h)
+
+
+class AutoencoderKLModeOnly(AutoencodingEngineLegacy):
+    def __init__(self, **kwargs):
+        if "lossconfig" in kwargs:
+            kwargs["loss_config"] = kwargs.pop("lossconfig")
+        super().__init__(
+            regularizer_config={
+                "target": (
+                    "sgm.modules.autoencoding.regularizers"
+                    ".DiagonalGaussianRegularizer"
+                ),
+                "params": {"sample": False},
+            },
+            **kwargs,
+        )

sgm/models/diffusion.py

@@ -0,0 +1,346 @@
+import math
+from contextlib import contextmanager
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import pytorch_lightning as pl
+import torch
+from omegaconf import ListConfig, OmegaConf
+from safetensors.torch import load_file as load_safetensors
+from torch.optim.lr_scheduler import LambdaLR
+
+from ..modules import UNCONDITIONAL_CONFIG
+from ..modules.autoencoding.temporal_ae import VideoDecoder
+from ..modules.diffusionmodules.wrappers import OPENAIUNETWRAPPER
+from ..modules.ema import LitEma
+from ..util import (
+    default,
+    disabled_train,
+    get_obj_from_str,
+    instantiate_from_config,
+    log_txt_as_img,
+)
+
+
+class DiffusionEngine(pl.LightningModule):
+    def __init__(
+        self,
+        network_config,
+        denoiser_config,
+        first_stage_config,
+        conditioner_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        sampler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        optimizer_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        scheduler_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        loss_fn_config: Union[None, Dict, ListConfig, OmegaConf] = None,
+        network_wrapper: Union[None, str] = None,
+        ckpt_path: Union[None, str] = None,
+        use_ema: bool = False,
+        ema_decay_rate: float = 0.9999,
+        scale_factor: float = 1.0,
+        disable_first_stage_autocast=False,
+        input_key: str = "jpg",
+        log_keys: Union[List, None] = None,
+        no_cond_log: bool = False,
+        compile_model: bool = False,
+        en_and_decode_n_samples_a_time: Optional[int] = None,
+    ):
+        super().__init__()
+        self.log_keys = log_keys
+        self.input_key = input_key
+        self.optimizer_config = default(
+            optimizer_config, {"target": "torch.optim.AdamW"}
+        )
+        model = instantiate_from_config(network_config)
+        self.model = get_obj_from_str(default(network_wrapper, OPENAIUNETWRAPPER))(
+            model, compile_model=compile_model
+        )
+
+        self.denoiser = instantiate_from_config(denoiser_config)
+        self.sampler = (
+            instantiate_from_config(sampler_config)
+            if sampler_config is not None
+            else None
+        )
+        self.conditioner = instantiate_from_config(
+            default(conditioner_config, UNCONDITIONAL_CONFIG)
+        )
+        self.scheduler_config = scheduler_config
+        self._init_first_stage(first_stage_config)
+
+        self.loss_fn = (
+            instantiate_from_config(loss_fn_config)
+            if loss_fn_config is not None
+            else None
+        )
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model, decay=ema_decay_rate)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.scale_factor = scale_factor
+        self.disable_first_stage_autocast = disable_first_stage_autocast
+        self.no_cond_log = no_cond_log
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path)
+
+        self.en_and_decode_n_samples_a_time = en_and_decode_n_samples_a_time
+
+    def init_from_ckpt(
+        self,
+        path: str,
+    ) -> None:
+        if path.endswith("ckpt"):
+            sd = torch.load(path, map_location="cpu")["state_dict"]
+        elif path.endswith("safetensors"):
+            sd = load_safetensors(path)
+        else:
+            raise NotImplementedError
+
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(
+            f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys"
+        )
+        if len(missing) > 0:
+            print(f"Missing Keys: {missing}")
+        if len(unexpected) > 0:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def _init_first_stage(self, config):
+        model = instantiate_from_config(config).eval()
+        model.train = disabled_train
+        for param in model.parameters():
+            param.requires_grad = False
+        self.first_stage_model = model
+
+    def get_input(self, batch):
+        # assuming unified data format, dataloader returns a dict.
+        # image tensors should be scaled to -1 ... 1 and in bchw format
+        return batch[self.input_key]
+
+    @torch.no_grad()
+    def decode_first_stage(self, z):
+        z = 1.0 / self.scale_factor * z
+        n_samples = default(self.en_and_decode_n_samples_a_time, z.shape[0])
+
+        n_rounds = math.ceil(z.shape[0] / n_samples)
+        all_out = []
+        with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
+            for n in range(n_rounds):
+                if isinstance(self.first_stage_model.decoder, VideoDecoder):
+                    kwargs = {"timesteps": len(z[n * n_samples : (n + 1) * n_samples])}
+                else:
+                    kwargs = {}
+                out = self.first_stage_model.decode(
+                    z[n * n_samples : (n + 1) * n_samples], **kwargs
+                )
+                all_out.append(out)
+        out = torch.cat(all_out, dim=0)
+        return out
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        n_samples = default(self.en_and_decode_n_samples_a_time, x.shape[0])
+        n_rounds = math.ceil(x.shape[0] / n_samples)
+        all_out = []
+        with torch.autocast("cuda", enabled=not self.disable_first_stage_autocast):
+            for n in range(n_rounds):
+                out = self.first_stage_model.encode(
+                    x[n * n_samples : (n + 1) * n_samples]
+                )
+                all_out.append(out)
+        z = torch.cat(all_out, dim=0)
+        z = self.scale_factor * z
+        return z
+
+    def forward(self, x, batch):
+        loss = self.loss_fn(self.model, self.denoiser, self.conditioner, x, batch)
+        loss_mean = loss.mean()
+        loss_dict = {"loss": loss_mean}
+        return loss_mean, loss_dict
+
+    def shared_step(self, batch: Dict) -> Any:
+        x = self.get_input(batch)
+        x = self.encode_first_stage(x)
+        batch["global_step"] = self.global_step
+        loss, loss_dict = self(x, batch)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(
+            loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=False
+        )
+
+        self.log(
+            "global_step",
+            self.global_step,
+            prog_bar=True,
+            logger=True,
+            on_step=True,
+            on_epoch=False,
+        )
+
+        if self.scheduler_config is not None:
+            lr = self.optimizers().param_groups[0]["lr"]
+            self.log(
+                "lr_abs", lr, prog_bar=True, logger=True, on_step=True, on_epoch=False
+            )
+
+        return loss
+
+    def on_train_start(self, *args, **kwargs):
+        if self.sampler is None or self.loss_fn is None:
+            raise ValueError("Sampler and loss function need to be set for training.")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def instantiate_optimizer_from_config(self, params, lr, cfg):
+        return get_obj_from_str(cfg["target"])(
+            params, lr=lr, **cfg.get("params", dict())
+        )
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        for embedder in self.conditioner.embedders:
+            if embedder.is_trainable:
+                params = params + list(embedder.parameters())
+        opt = self.instantiate_optimizer_from_config(params, lr, self.optimizer_config)
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    "scheduler": LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    "interval": "step",
+                    "frequency": 1,
+                }
+            ]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def sample(
+        self,
+        cond: Dict,
+        uc: Union[Dict, None] = None,
+        batch_size: int = 16,
+        shape: Union[None, Tuple, List] = None,
+        **kwargs,
+    ):
+        randn = torch.randn(batch_size, *shape).to(self.device)
+
+        denoiser = lambda input, sigma, c: self.denoiser(
+            self.model, input, sigma, c, **kwargs
+        )
+        samples = self.sampler(denoiser, randn, cond, uc=uc)
+        return samples
+
+    @torch.no_grad()
+    def log_conditionings(self, batch: Dict, n: int) -> Dict:
+        """
+        Defines heuristics to log different conditionings.
+        These can be lists of strings (text-to-image), tensors, ints, ...
+        """
+        image_h, image_w = batch[self.input_key].shape[2:]
+        log = dict()
+
+        for embedder in self.conditioner.embedders:
+            if (
+                (self.log_keys is None) or (embedder.input_key in self.log_keys)
+            ) and not self.no_cond_log:
+                x = batch[embedder.input_key][:n]
+                if isinstance(x, torch.Tensor):
+                    if x.dim() == 1:
+                        # class-conditional, convert integer to string
+                        x = [str(x[i].item()) for i in range(x.shape[0])]
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 4)
+                    elif x.dim() == 2:
+                        # size and crop cond and the like
+                        x = [
+                            "x".join([str(xx) for xx in x[i].tolist()])
+                            for i in range(x.shape[0])
+                        ]
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
+                    else:
+                        raise NotImplementedError()
+                elif isinstance(x, (List, ListConfig)):
+                    if isinstance(x[0], str):
+                        # strings
+                        xc = log_txt_as_img((image_h, image_w), x, size=image_h // 20)
+                    else:
+                        raise NotImplementedError()
+                else:
+                    raise NotImplementedError()
+                log[embedder.input_key] = xc
+        return log
+
+    @torch.no_grad()
+    def log_images(
+        self,
+        batch: Dict,
+        N: int = 8,
+        sample: bool = True,
+        ucg_keys: List[str] = None,
+        **kwargs,
+    ) -> Dict:
+        conditioner_input_keys = [e.input_key for e in self.conditioner.embedders]
+        if ucg_keys:
+            assert all(map(lambda x: x in conditioner_input_keys, ucg_keys)), (
+                "Each defined ucg key for sampling must be in the provided conditioner input keys,"
+                f"but we have {ucg_keys} vs. {conditioner_input_keys}"
+            )
+        else:
+            ucg_keys = conditioner_input_keys
+        log = dict()
+
+        x = self.get_input(batch)
+
+        c, uc = self.conditioner.get_unconditional_conditioning(
+            batch,
+            force_uc_zero_embeddings=ucg_keys
+            if len(self.conditioner.embedders) > 0
+            else [],
+        )
+
+        sampling_kwargs = {}
+
+        N = min(x.shape[0], N)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+        z = self.encode_first_stage(x)
+        log["reconstructions"] = self.decode_first_stage(z)
+        log.update(self.log_conditionings(batch, N))
+
+        for k in c:
+            if isinstance(c[k], torch.Tensor):
+                c[k], uc[k] = map(lambda y: y[k][:N].to(self.device), (c, uc))
+
+        if sample:
+            with self.ema_scope("Plotting"):
+                samples = self.sample(
+                    c, shape=z.shape[1:], uc=uc, batch_size=N, **sampling_kwargs
+                )
+            samples = self.decode_first_stage(samples)
+            log["samples"] = samples
+        return log

sgm/modules/__init__.py

@@ -0,0 +1,6 @@
+from .encoders.modules import GeneralConditioner
+
+UNCONDITIONAL_CONFIG = {
+    "target": "sgm.modules.GeneralConditioner",
+    "params": {"emb_models": []},
+}

sgm/modules/attention.py

@@ -0,0 +1,759 @@
+import logging
+import math
+from inspect import isfunction
+from typing import Any, Optional
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from packaging import version
+from torch import nn
+from torch.utils.checkpoint import checkpoint
+
+logpy = logging.getLogger(__name__)
+
+if version.parse(torch.__version__) >= version.parse("2.0.0"):
+    SDP_IS_AVAILABLE = True
+    from torch.backends.cuda import SDPBackend, sdp_kernel
+
+    BACKEND_MAP = {
+        SDPBackend.MATH: {
+            "enable_math": True,
+            "enable_flash": False,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.FLASH_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": True,
+            "enable_mem_efficient": False,
+        },
+        SDPBackend.EFFICIENT_ATTENTION: {
+            "enable_math": False,
+            "enable_flash": False,
+            "enable_mem_efficient": True,
+        },
+        None: {"enable_math": True, "enable_flash": True, "enable_mem_efficient": True},
+    }
+else:
+    from contextlib import nullcontext
+
+    SDP_IS_AVAILABLE = False
+    sdp_kernel = nullcontext
+    BACKEND_MAP = {}
+    logpy.warn(
+        f"No SDP backend available, likely because you are running in pytorch "
+        f"versions < 2.0. In fact, you are using PyTorch {torch.__version__}. "
+        f"You might want to consider upgrading."
+    )
+
+try:
+    import xformers
+    import xformers.ops
+
+    XFORMERS_IS_AVAILABLE = True
+except:
+    XFORMERS_IS_AVAILABLE = False
+    logpy.warn("no module 'xformers'. Processing without...")
+
+# from .diffusionmodules.util import mixed_checkpoint as checkpoint
+
+
+def exists(val):
+    return val is not None
+
+
+def uniq(arr):
+    return {el: True for el in arr}.keys()
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+    dim = tensor.shape[-1]
+    std = 1 / math.sqrt(dim)
+    tensor.uniform_(-std, std)
+    return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        project_in = (
+            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
+            if not glu
+            else GEGLU(dim, inner_dim)
+        )
+
+        self.net = nn.Sequential(
+            project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+def Normalize(in_channels):
+    return torch.nn.GroupNorm(
+        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
+    )
+
+
+class LinearAttention(nn.Module):
+    def __init__(self, dim, heads=4, dim_head=32):
+        super().__init__()
+        self.heads = heads
+        hidden_dim = dim_head * heads
+        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
+        self.to_out = nn.Conv2d(hidden_dim, dim, 1)
+
+    def forward(self, x):
+        b, c, h, w = x.shape
+        qkv = self.to_qkv(x)
+        q, k, v = rearrange(
+            qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3
+        )
+        k = k.softmax(dim=-1)
+        context = torch.einsum("bhdn,bhen->bhde", k, v)
+        out = torch.einsum("bhde,bhdn->bhen", context, q)
+        out = rearrange(
+            out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w
+        )
+        return self.to_out(out)
+
+
+class SelfAttention(nn.Module):
+    ATTENTION_MODES = ("xformers", "torch", "math")
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_scale: Optional[float] = None,
+        attn_drop: float = 0.0,
+        proj_drop: float = 0.0,
+        attn_mode: str = "xformers",
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+        assert attn_mode in self.ATTENTION_MODES
+        self.attn_mode = attn_mode
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        B, L, C = x.shape
+
+        qkv = self.qkv(x)
+        if self.attn_mode == "torch":
+            qkv = rearrange(
+                qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads
+            ).float()
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
+            x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+            x = rearrange(x, "B H L D -> B L (H D)")
+        elif self.attn_mode == "xformers":
+            qkv = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads)
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B L H D
+            x = xformers.ops.memory_efficient_attention(q, k, v)
+            x = rearrange(x, "B L H D -> B L (H D)", H=self.num_heads)
+        elif self.attn_mode == "math":
+            qkv = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+            q, k, v = qkv[0], qkv[1], qkv[2]  # B H L D
+            attn = (q @ k.transpose(-2, -1)) * self.scale
+            attn = attn.softmax(dim=-1)
+            attn = self.attn_drop(attn)
+            x = (attn @ v).transpose(1, 2).reshape(B, L, C)
+        else:
+            raise NotImplemented
+
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SpatialSelfAttention(nn.Module):
+    def __init__(self, in_channels):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = Normalize(in_channels)
+        self.q = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.k = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.v = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.proj_out = torch.nn.Conv2d(
+            in_channels, in_channels, kernel_size=1, stride=1, padding=0
+        )
+
+    def forward(self, x):
+        h_ = x
+        h_ = self.norm(h_)
+        q = self.q(h_)
+        k = self.k(h_)
+        v = self.v(h_)
+
+        # compute attention
+        b, c, h, w = q.shape
+        q = rearrange(q, "b c h w -> b (h w) c")
+        k = rearrange(k, "b c h w -> b c (h w)")
+        w_ = torch.einsum("bij,bjk->bik", q, k)
+
+        w_ = w_ * (int(c) ** (-0.5))
+        w_ = torch.nn.functional.softmax(w_, dim=2)
+
+        # attend to values
+        v = rearrange(v, "b c h w -> b c (h w)")
+        w_ = rearrange(w_, "b i j -> b j i")
+        h_ = torch.einsum("bij,bjk->bik", v, w_)
+        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
+        h_ = self.proj_out(h_)
+
+        return x + h_
+
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        query_dim,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        backend=None,
+    ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.scale = dim_head**-0.5
+        self.heads = heads
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
+        )
+        self.backend = backend
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        additional_tokens=None,
+        n_times_crossframe_attn_in_self=0,
+    ):
+        h = self.heads
+
+        if additional_tokens is not None:
+            # get the number of masked tokens at the beginning of the output sequence
+            n_tokens_to_mask = additional_tokens.shape[1]
+            # add additional token
+            x = torch.cat([additional_tokens, x], dim=1)
+
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        if n_times_crossframe_attn_in_self:
+            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
+            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
+            n_cp = x.shape[0] // n_times_crossframe_attn_in_self
+            k = repeat(
+                k[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+            v = repeat(
+                v[::n_times_crossframe_attn_in_self], "b ... -> (b n) ...", n=n_cp
+            )
+
+        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> b h n d", h=h), (q, k, v))
+
+        ## old
+        """
+        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+        del q, k
+
+        if exists(mask):
+            mask = rearrange(mask, 'b ... -> b (...)')
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = repeat(mask, 'b j -> (b h) () j', h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+
+        # attention, what we cannot get enough of
+        sim = sim.softmax(dim=-1)
+
+        out = einsum('b i j, b j d -> b i d', sim, v)
+        """
+        ## new
+        with sdp_kernel(**BACKEND_MAP[self.backend]):
+            # print("dispatching into backend", self.backend, "q/k/v shape: ", q.shape, k.shape, v.shape)
+            out = F.scaled_dot_product_attention(
+                q, k, v, attn_mask=mask
+            )  # scale is dim_head ** -0.5 per default
+
+        del q, k, v
+        out = rearrange(out, "b h n d -> b n (h d)", h=h)
+
+        if additional_tokens is not None:
+            # remove additional token
+            out = out[:, n_tokens_to_mask:]
+        return self.to_out(out)
+
+
+class MemoryEfficientCrossAttention(nn.Module):
+    # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    def __init__(
+        self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, **kwargs
+    ):
+        super().__init__()
+        logpy.debug(
+            f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, "
+            f"context_dim is {context_dim} and using {heads} heads with a "
+            f"dimension of {dim_head}."
+        )
+        inner_dim = dim_head * heads
+        context_dim = default(context_dim, query_dim)
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)
+        )
+        self.attention_op: Optional[Any] = None
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        additional_tokens=None,
+        n_times_crossframe_attn_in_self=0,
+    ):
+        if additional_tokens is not None:
+            # get the number of masked tokens at the beginning of the output sequence
+            n_tokens_to_mask = additional_tokens.shape[1]
+            # add additional token
+            x = torch.cat([additional_tokens, x], dim=1)
+        q = self.to_q(x)
+        context = default(context, x)
+        k = self.to_k(context)
+        v = self.to_v(context)
+
+        if n_times_crossframe_attn_in_self:
+            # reprogramming cross-frame attention as in https://arxiv.org/abs/2303.13439
+            assert x.shape[0] % n_times_crossframe_attn_in_self == 0
+            # n_cp = x.shape[0]//n_times_crossframe_attn_in_self
+            k = repeat(
+                k[::n_times_crossframe_attn_in_self],
+                "b ... -> (b n) ...",
+                n=n_times_crossframe_attn_in_self,
+            )
+            v = repeat(
+                v[::n_times_crossframe_attn_in_self],
+                "b ... -> (b n) ...",
+                n=n_times_crossframe_attn_in_self,
+            )
+
+        b, _, _ = q.shape
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, t.shape[1], self.heads, self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * self.heads, t.shape[1], self.dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
+
+        # actually compute the attention, what we cannot get enough of
+        if version.parse(xformers.__version__) >= version.parse("0.0.21"):
+            # NOTE: workaround for
+            # https://github.com/facebookresearch/xformers/issues/845
+            max_bs = 32768
+            N = q.shape[0]
+            n_batches = math.ceil(N / max_bs)
+            out = list()
+            for i_batch in range(n_batches):
+                batch = slice(i_batch * max_bs, (i_batch + 1) * max_bs)
+                out.append(
+                    xformers.ops.memory_efficient_attention(
+                        q[batch],
+                        k[batch],
+                        v[batch],
+                        attn_bias=None,
+                        op=self.attention_op,
+                    )
+                )
+            out = torch.cat(out, 0)
+        else:
+            out = xformers.ops.memory_efficient_attention(
+                q, k, v, attn_bias=None, op=self.attention_op
+            )
+
+        # TODO: Use this directly in the attention operation, as a bias
+        if exists(mask):
+            raise NotImplementedError
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, self.heads, out.shape[1], self.dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, out.shape[1], self.heads * self.dim_head)
+        )
+        if additional_tokens is not None:
+            # remove additional token
+            out = out[:, n_tokens_to_mask:]
+        return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention,  # ampere
+    }
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+        disable_self_attn=False,
+        attn_mode="softmax",
+        sdp_backend=None,
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        if attn_mode != "softmax" and not XFORMERS_IS_AVAILABLE:
+            logpy.warn(
+                f"Attention mode '{attn_mode}' is not available. Falling "
+                f"back to native attention. This is not a problem in "
+                f"Pytorch >= 2.0. FYI, you are running with PyTorch "
+                f"version {torch.__version__}."
+            )
+            attn_mode = "softmax"
+        elif attn_mode == "softmax" and not SDP_IS_AVAILABLE:
+            logpy.warn(
+                "We do not support vanilla attention anymore, as it is too "
+                "expensive. Sorry."
+            )
+            if not XFORMERS_IS_AVAILABLE:
+                assert (
+                    False
+                ), "Please install xformers via e.g. 'pip install xformers==0.0.16'"
+            else:
+                logpy.info("Falling back to xformers efficient attention.")
+                attn_mode = "softmax-xformers"
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        if version.parse(torch.__version__) >= version.parse("2.0.0"):
+            assert sdp_backend is None or isinstance(sdp_backend, SDPBackend)
+        else:
+            assert sdp_backend is None
+        self.disable_self_attn = disable_self_attn
+        self.attn1 = attn_cls(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            context_dim=context_dim if self.disable_self_attn else None,
+            backend=sdp_backend,
+        )  # is a self-attention if not self.disable_self_attn
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.attn2 = attn_cls(
+            query_dim=dim,
+            context_dim=context_dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            backend=sdp_backend,
+        )  # is self-attn if context is none
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.norm3 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+        if self.checkpoint:
+            logpy.debug(f"{self.__class__.__name__} is using checkpointing")
+
+    def forward(
+        self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
+    ):
+        kwargs = {"x": x}
+
+        if context is not None:
+            kwargs.update({"context": context})
+
+        if additional_tokens is not None:
+            kwargs.update({"additional_tokens": additional_tokens})
+
+        if n_times_crossframe_attn_in_self:
+            kwargs.update(
+                {"n_times_crossframe_attn_in_self": n_times_crossframe_attn_in_self}
+            )
+
+        # return mixed_checkpoint(self._forward, kwargs, self.parameters(), self.checkpoint)
+        if self.checkpoint:
+            # inputs = {"x": x, "context": context}
+            return checkpoint(self._forward, x, context)
+            # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint)
+        else:
+            return self._forward(**kwargs)
+
+    def _forward(
+        self, x, context=None, additional_tokens=None, n_times_crossframe_attn_in_self=0
+    ):
+        x = (
+            self.attn1(
+                self.norm1(x),
+                context=context if self.disable_self_attn else None,
+                additional_tokens=additional_tokens,
+                n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self
+                if not self.disable_self_attn
+                else 0,
+            )
+            + x
+        )
+        x = (
+            self.attn2(
+                self.norm2(x), context=context, additional_tokens=additional_tokens
+            )
+            + x
+        )
+        x = self.ff(self.norm3(x)) + x
+        return x
+
+
+class BasicTransformerSingleLayerBlock(nn.Module):
+    ATTENTION_MODES = {
+        "softmax": CrossAttention,  # vanilla attention
+        "softmax-xformers": MemoryEfficientCrossAttention  # on the A100s not quite as fast as the above version
+        # (todo might depend on head_dim, check, falls back to semi-optimized kernels for dim!=[16,32,64,128])
+    }
+
+    def __init__(
+        self,
+        dim,
+        n_heads,
+        d_head,
+        dropout=0.0,
+        context_dim=None,
+        gated_ff=True,
+        checkpoint=True,
+        attn_mode="softmax",
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        attn_cls = self.ATTENTION_MODES[attn_mode]
+        self.attn1 = attn_cls(
+            query_dim=dim,
+            heads=n_heads,
+            dim_head=d_head,
+            dropout=dropout,
+            context_dim=context_dim,
+        )
+        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+        self.norm1 = nn.LayerNorm(dim)
+        self.norm2 = nn.LayerNorm(dim)
+        self.checkpoint = checkpoint
+
+    def forward(self, x, context=None):
+        # inputs = {"x": x, "context": context}
+        # return checkpoint(self._forward, inputs, self.parameters(), self.checkpoint)
+        return checkpoint(self._forward, x, context)
+
+    def _forward(self, x, context=None):
+        x = self.attn1(self.norm1(x), context=context) + x
+        x = self.ff(self.norm2(x)) + x
+        return x
+
+
+class SpatialTransformer(nn.Module):
+    """
+    Transformer block for image-like data.
+    First, project the input (aka embedding)
+    and reshape to b, t, d.
+    Then apply standard transformer action.
+    Finally, reshape to image
+    NEW: use_linear for more efficiency instead of the 1x1 convs
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        n_heads,
+        d_head,
+        depth=1,
+        dropout=0.0,
+        context_dim=None,
+        disable_self_attn=False,
+        use_linear=False,
+        attn_type="softmax",
+        use_checkpoint=True,
+        # sdp_backend=SDPBackend.FLASH_ATTENTION
+        sdp_backend=None,
+    ):
+        super().__init__()
+        logpy.debug(
+            f"constructing {self.__class__.__name__} of depth {depth} w/ "
+            f"{in_channels} channels and {n_heads} heads."
+        )
+
+        if exists(context_dim) and not isinstance(context_dim, list):
+            context_dim = [context_dim]
+        if exists(context_dim) and isinstance(context_dim, list):
+            if depth != len(context_dim):
+                logpy.warn(
+                    f"{self.__class__.__name__}: Found context dims "
+                    f"{context_dim} of depth {len(context_dim)}, which does not "
+                    f"match the specified 'depth' of {depth}. Setting context_dim "
+                    f"to {depth * [context_dim[0]]} now."
+                )
+                # depth does not match context dims.
+                assert all(
+                    map(lambda x: x == context_dim[0], context_dim)
+                ), "need homogenous context_dim to match depth automatically"
+                context_dim = depth * [context_dim[0]]
+        elif context_dim is None:
+            context_dim = [None] * depth
+        self.in_channels = in_channels
+        inner_dim = n_heads * d_head
+        self.norm = Normalize(in_channels)
+        if not use_linear:
+            self.proj_in = nn.Conv2d(
+                in_channels, inner_dim, kernel_size=1, stride=1, padding=0
+            )
+        else:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    n_heads,
+                    d_head,
+                    dropout=dropout,
+                    context_dim=context_dim[d],
+                    disable_self_attn=disable_self_attn,
+                    attn_mode=attn_type,
+                    checkpoint=use_checkpoint,
+                    sdp_backend=sdp_backend,
+                )
+                for d in range(depth)
+            ]
+        )
+        if not use_linear:
+            self.proj_out = zero_module(
+                nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+            )
+        else:
+            # self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+            self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
+        self.use_linear = use_linear
+
+    def forward(self, x, context=None):
+        # note: if no context is given, cross-attention defaults to self-attention
+        if not isinstance(context, list):
+            context = [context]
+        b, c, h, w = x.shape
+        x_in = x
+        x = self.norm(x)
+        if not self.use_linear:
+            x = self.proj_in(x)
+        x = rearrange(x, "b c h w -> b (h w) c").contiguous()
+        if self.use_linear:
+            x = self.proj_in(x)
+        for i, block in enumerate(self.transformer_blocks):
+            if i > 0 and len(context) == 1:
+                i = 0  # use same context for each block
+            x = block(x, context=context[i])
+        if self.use_linear:
+            x = self.proj_out(x)
+        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
+        if not self.use_linear:
+            x = self.proj_out(x)
+        return x + x_in
+
+
+class SimpleTransformer(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        depth: int,
+        heads: int,
+        dim_head: int,
+        context_dim: Optional[int] = None,
+        dropout: float = 0.0,
+        checkpoint: bool = True,
+    ):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(
+                BasicTransformerBlock(
+                    dim,
+                    heads,
+                    dim_head,
+                    dropout=dropout,
+                    context_dim=context_dim,
+                    attn_mode="softmax-xformers",
+                    checkpoint=checkpoint,
+                )
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        for layer in self.layers:
+            x = layer(x, context)
+        return x

sgm/modules/autoencoding/losses/__init__.py

@@ -0,0 +1,7 @@
+__all__ = [
+    "GeneralLPIPSWithDiscriminator",
+    "LatentLPIPS",
+]
+
+from .discriminator_loss import GeneralLPIPSWithDiscriminator
+from .lpips import LatentLPIPS

sgm/modules/autoencoding/losses/discriminator_loss.py

@@ -0,0 +1,306 @@
+from typing import Dict, Iterator, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision
+from einops import rearrange
+from matplotlib import colormaps
+from matplotlib import pyplot as plt
+
+from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+from ..lpips.model.model import weights_init
+from ..lpips.vqperceptual import hinge_d_loss, vanilla_d_loss
+
+
+class GeneralLPIPSWithDiscriminator(nn.Module):
+    def __init__(
+        self,
+        disc_start: int,
+        logvar_init: float = 0.0,
+        disc_num_layers: int = 3,
+        disc_in_channels: int = 3,
+        disc_factor: float = 1.0,
+        disc_weight: float = 1.0,
+        perceptual_weight: float = 1.0,
+        disc_loss: str = "hinge",
+        scale_input_to_tgt_size: bool = False,
+        dims: int = 2,
+        learn_logvar: bool = False,
+        regularization_weights: Union[None, Dict[str, float]] = None,
+        additional_log_keys: Optional[List[str]] = None,
+        discriminator_config: Optional[Dict] = None,
+    ):
+        super().__init__()
+        self.dims = dims
+        if self.dims > 2:
+            print(
+                f"running with dims={dims}. This means that for perceptual loss "
+                f"calculation, the LPIPS loss will be applied to each frame "
+                f"independently."
+            )
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        assert disc_loss in ["hinge", "vanilla"]
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        # output log variance
+        self.logvar = nn.Parameter(
+            torch.full((), logvar_init), requires_grad=learn_logvar
+        )
+        self.learn_logvar = learn_logvar
+
+        discriminator_config = default(
+            discriminator_config,
+            {
+                "target": "sgm.modules.autoencoding.lpips.model.model.NLayerDiscriminator",
+                "params": {
+                    "input_nc": disc_in_channels,
+                    "n_layers": disc_num_layers,
+                    "use_actnorm": False,
+                },
+            },
+        )
+
+        self.discriminator = instantiate_from_config(discriminator_config).apply(
+            weights_init
+        )
+        self.discriminator_iter_start = disc_start
+        self.disc_loss = hinge_d_loss if disc_loss == "hinge" else vanilla_d_loss
+        self.disc_factor = disc_factor
+        self.discriminator_weight = disc_weight
+        self.regularization_weights = default(regularization_weights, {})
+
+        self.forward_keys = [
+            "optimizer_idx",
+            "global_step",
+            "last_layer",
+            "split",
+            "regularization_log",
+        ]
+
+        self.additional_log_keys = set(default(additional_log_keys, []))
+        self.additional_log_keys.update(set(self.regularization_weights.keys()))
+
+    def get_trainable_parameters(self) -> Iterator[nn.Parameter]:
+        return self.discriminator.parameters()
+
+    def get_trainable_autoencoder_parameters(self) -> Iterator[nn.Parameter]:
+        if self.learn_logvar:
+            yield self.logvar
+        yield from ()
+
+    @torch.no_grad()
+    def log_images(
+        self, inputs: torch.Tensor, reconstructions: torch.Tensor
+    ) -> Dict[str, torch.Tensor]:
+        # calc logits of real/fake
+        logits_real = self.discriminator(inputs.contiguous().detach())
+        if len(logits_real.shape) < 4:
+            # Non patch-discriminator
+            return dict()
+        logits_fake = self.discriminator(reconstructions.contiguous().detach())
+        # -> (b, 1, h, w)
+
+        # parameters for colormapping
+        high = max(logits_fake.abs().max(), logits_real.abs().max()).item()
+        cmap = colormaps["PiYG"]  # diverging colormap
+
+        def to_colormap(logits: torch.Tensor) -> torch.Tensor:
+            """(b, 1, ...) -> (b, 3, ...)"""
+            logits = (logits + high) / (2 * high)
+            logits_np = cmap(logits.cpu().numpy())[..., :3]  # truncate alpha channel
+            # -> (b, 1, ..., 3)
+            logits = torch.from_numpy(logits_np).to(logits.device)
+            return rearrange(logits, "b 1 ... c -> b c ...")
+
+        logits_real = torch.nn.functional.interpolate(
+            logits_real,
+            size=inputs.shape[-2:],
+            mode="nearest",
+            antialias=False,
+        )
+        logits_fake = torch.nn.functional.interpolate(
+            logits_fake,
+            size=reconstructions.shape[-2:],
+            mode="nearest",
+            antialias=False,
+        )
+
+        # alpha value of logits for overlay
+        alpha_real = torch.abs(logits_real) / high
+        alpha_fake = torch.abs(logits_fake) / high
+        # -> (b, 1, h, w) in range [0, 0.5]
+        # alpha value of lines don't really matter, since the values are the same
+        # for both images and logits anyway
+        grid_alpha_real = torchvision.utils.make_grid(alpha_real, nrow=4)
+        grid_alpha_fake = torchvision.utils.make_grid(alpha_fake, nrow=4)
+        grid_alpha = 0.8 * torch.cat((grid_alpha_real, grid_alpha_fake), dim=1)
+        # -> (1, h, w)
+        # blend logits and images together
+
+        # prepare logits for plotting
+        logits_real = to_colormap(logits_real)
+        logits_fake = to_colormap(logits_fake)
+        # resize logits
+        # -> (b, 3, h, w)
+
+        # make some grids
+        # add all logits to one plot
+        logits_real = torchvision.utils.make_grid(logits_real, nrow=4)
+        logits_fake = torchvision.utils.make_grid(logits_fake, nrow=4)
+        # I just love how torchvision calls the number of columns `nrow`
+        grid_logits = torch.cat((logits_real, logits_fake), dim=1)
+        # -> (3, h, w)
+
+        grid_images_real = torchvision.utils.make_grid(0.5 * inputs + 0.5, nrow=4)
+        grid_images_fake = torchvision.utils.make_grid(
+            0.5 * reconstructions + 0.5, nrow=4
+        )
+        grid_images = torch.cat((grid_images_real, grid_images_fake), dim=1)
+        # -> (3, h, w) in range [0, 1]
+
+        grid_blend = grid_alpha * grid_logits + (1 - grid_alpha) * grid_images
+
+        # Create labeled colorbar
+        dpi = 100
+        height = 128 / dpi
+        width = grid_logits.shape[2] / dpi
+        fig, ax = plt.subplots(figsize=(width, height), dpi=dpi)
+        img = ax.imshow(np.array([[-high, high]]), cmap=cmap)
+        plt.colorbar(
+            img,
+            cax=ax,
+            orientation="horizontal",
+            fraction=0.9,
+            aspect=width / height,
+            pad=0.0,
+        )
+        img.set_visible(False)
+        fig.tight_layout()
+        fig.canvas.draw()
+        # manually convert figure to numpy
+        cbar_np = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+        cbar_np = cbar_np.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+        cbar = torch.from_numpy(cbar_np.copy()).to(grid_logits.dtype) / 255.0
+        cbar = rearrange(cbar, "h w c -> c h w").to(grid_logits.device)
+
+        # Add colorbar to plot
+        annotated_grid = torch.cat((grid_logits, cbar), dim=1)
+        blended_grid = torch.cat((grid_blend, cbar), dim=1)
+        return {
+            "vis_logits": 2 * annotated_grid[None, ...] - 1,
+            "vis_logits_blended": 2 * blended_grid[None, ...] - 1,
+        }
+
+    def calculate_adaptive_weight(
+        self, nll_loss: torch.Tensor, g_loss: torch.Tensor, last_layer: torch.Tensor
+    ) -> torch.Tensor:
+        nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
+        g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
+
+        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
+        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
+        d_weight = d_weight * self.discriminator_weight
+        return d_weight
+
+    def forward(
+        self,
+        inputs: torch.Tensor,
+        reconstructions: torch.Tensor,
+        *,  # added because I changed the order here
+        regularization_log: Dict[str, torch.Tensor],
+        optimizer_idx: int,
+        global_step: int,
+        last_layer: torch.Tensor,
+        split: str = "train",
+        weights: Union[None, float, torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, dict]:
+        if self.scale_input_to_tgt_size:
+            inputs = torch.nn.functional.interpolate(
+                inputs, reconstructions.shape[2:], mode="bicubic", antialias=True
+            )
+
+        if self.dims > 2:
+            inputs, reconstructions = map(
+                lambda x: rearrange(x, "b c t h w -> (b t) c h w"),
+                (inputs, reconstructions),
+            )
+
+        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
+        if self.perceptual_weight > 0:
+            p_loss = self.perceptual_loss(
+                inputs.contiguous(), reconstructions.contiguous()
+            )
+            rec_loss = rec_loss + self.perceptual_weight * p_loss
+
+        nll_loss, weighted_nll_loss = self.get_nll_loss(rec_loss, weights)
+
+        # now the GAN part
+        if optimizer_idx == 0:
+            # generator update
+            if global_step >= self.discriminator_iter_start or not self.training:
+                logits_fake = self.discriminator(reconstructions.contiguous())
+                g_loss = -torch.mean(logits_fake)
+                if self.training:
+                    d_weight = self.calculate_adaptive_weight(
+                        nll_loss, g_loss, last_layer=last_layer
+                    )
+                else:
+                    d_weight = torch.tensor(1.0)
+            else:
+                d_weight = torch.tensor(0.0)
+                g_loss = torch.tensor(0.0, requires_grad=True)
+
+            loss = weighted_nll_loss + d_weight * self.disc_factor * g_loss
+            log = dict()
+            for k in regularization_log:
+                if k in self.regularization_weights:
+                    loss = loss + self.regularization_weights[k] * regularization_log[k]
+                if k in self.additional_log_keys:
+                    log[f"{split}/{k}"] = regularization_log[k].detach().float().mean()
+
+            log.update(
+                {
+                    f"{split}/loss/total": loss.clone().detach().mean(),
+                    f"{split}/loss/nll": nll_loss.detach().mean(),
+                    f"{split}/loss/rec": rec_loss.detach().mean(),
+                    f"{split}/loss/g": g_loss.detach().mean(),
+                    f"{split}/scalars/logvar": self.logvar.detach(),
+                    f"{split}/scalars/d_weight": d_weight.detach(),
+                }
+            )
+
+            return loss, log
+        elif optimizer_idx == 1:
+            # second pass for discriminator update
+            logits_real = self.discriminator(inputs.contiguous().detach())
+            logits_fake = self.discriminator(reconstructions.contiguous().detach())
+
+            if global_step >= self.discriminator_iter_start or not self.training:
+                d_loss = self.disc_factor * self.disc_loss(logits_real, logits_fake)
+            else:
+                d_loss = torch.tensor(0.0, requires_grad=True)
+
+            log = {
+                f"{split}/loss/disc": d_loss.clone().detach().mean(),
+                f"{split}/logits/real": logits_real.detach().mean(),
+                f"{split}/logits/fake": logits_fake.detach().mean(),
+            }
+            return d_loss, log
+        else:
+            raise NotImplementedError(f"Unknown optimizer_idx {optimizer_idx}")
+
+    def get_nll_loss(
+        self,
+        rec_loss: torch.Tensor,
+        weights: Optional[Union[float, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
+        weighted_nll_loss = nll_loss
+        if weights is not None:
+            weighted_nll_loss = weights * nll_loss
+        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
+        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
+
+        return nll_loss, weighted_nll_loss

sgm/modules/autoencoding/losses/lpips.py

@@ -0,0 +1,73 @@
+import torch
+import torch.nn as nn
+
+from ....util import default, instantiate_from_config
+from ..lpips.loss.lpips import LPIPS
+
+
+class LatentLPIPS(nn.Module):
+    def __init__(
+        self,
+        decoder_config,
+        perceptual_weight=1.0,
+        latent_weight=1.0,
+        scale_input_to_tgt_size=False,
+        scale_tgt_to_input_size=False,
+        perceptual_weight_on_inputs=0.0,
+    ):
+        super().__init__()
+        self.scale_input_to_tgt_size = scale_input_to_tgt_size
+        self.scale_tgt_to_input_size = scale_tgt_to_input_size
+        self.init_decoder(decoder_config)
+        self.perceptual_loss = LPIPS().eval()
+        self.perceptual_weight = perceptual_weight
+        self.latent_weight = latent_weight
+        self.perceptual_weight_on_inputs = perceptual_weight_on_inputs
+
+    def init_decoder(self, config):
+        self.decoder = instantiate_from_config(config)
+        if hasattr(self.decoder, "encoder"):
+            del self.decoder.encoder
+
+    def forward(self, latent_inputs, latent_predictions, image_inputs, split="train"):
+        log = dict()
+        loss = (latent_inputs - latent_predictions) ** 2
+        log[f"{split}/latent_l2_loss"] = loss.mean().detach()
+        image_reconstructions = None
+        if self.perceptual_weight > 0.0:
+            image_reconstructions = self.decoder.decode(latent_predictions)
+            image_targets = self.decoder.decode(latent_inputs)
+            perceptual_loss = self.perceptual_loss(
+                image_targets.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = (
+                self.latent_weight * loss.mean()
+                + self.perceptual_weight * perceptual_loss.mean()
+            )
+            log[f"{split}/perceptual_loss"] = perceptual_loss.mean().detach()
+
+        if self.perceptual_weight_on_inputs > 0.0:
+            image_reconstructions = default(
+                image_reconstructions, self.decoder.decode(latent_predictions)
+            )
+            if self.scale_input_to_tgt_size:
+                image_inputs = torch.nn.functional.interpolate(
+                    image_inputs,
+                    image_reconstructions.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+            elif self.scale_tgt_to_input_size:
+                image_reconstructions = torch.nn.functional.interpolate(
+                    image_reconstructions,
+                    image_inputs.shape[2:],
+                    mode="bicubic",
+                    antialias=True,
+                )
+
+            perceptual_loss2 = self.perceptual_loss(
+                image_inputs.contiguous(), image_reconstructions.contiguous()
+            )
+            loss = loss + self.perceptual_weight_on_inputs * perceptual_loss2.mean()
+            log[f"{split}/perceptual_loss_on_inputs"] = perceptual_loss2.mean().detach()
+        return loss, log

sgm/modules/autoencoding/lpips/loss/.gitignore

@@ -0,0 +1 @@
+vgg.pth

sgm/modules/autoencoding/lpips/loss/LICENSE

@@ -0,0 +1,23 @@
+Copyright (c) 2018, Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

sgm/modules/autoencoding/lpips/loss/lpips.py

@@ -0,0 +1,147 @@
+"""Stripped version of https://github.com/richzhang/PerceptualSimilarity/tree/master/models"""
+
+from collections import namedtuple
+
+import torch
+import torch.nn as nn
+from torchvision import models
+
+from ..util import get_ckpt_path
+
+
+class LPIPS(nn.Module):
+    # Learned perceptual metric
+    def __init__(self, use_dropout=True):
+        super().__init__()
+        self.scaling_layer = ScalingLayer()
+        self.chns = [64, 128, 256, 512, 512]  # vg16 features
+        self.net = vgg16(pretrained=True, requires_grad=False)
+        self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)
+        self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)
+        self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)
+        self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)
+        self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)
+        self.load_from_pretrained()
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def load_from_pretrained(self, name="vgg_lpips"):
+        ckpt = get_ckpt_path(name, "sgm/modules/autoencoding/lpips/loss")
+        self.load_state_dict(
+            torch.load(ckpt, map_location=torch.device("cpu")), strict=False
+        )
+        print("loaded pretrained LPIPS loss from {}".format(ckpt))
+
+    @classmethod
+    def from_pretrained(cls, name="vgg_lpips"):
+        if name != "vgg_lpips":
+            raise NotImplementedError
+        model = cls()
+        ckpt = get_ckpt_path(name)
+        model.load_state_dict(
+            torch.load(ckpt, map_location=torch.device("cpu")), strict=False
+        )
+        return model
+
+    def forward(self, input, target):
+        in0_input, in1_input = (self.scaling_layer(input), self.scaling_layer(target))
+        outs0, outs1 = self.net(in0_input), self.net(in1_input)
+        feats0, feats1, diffs = {}, {}, {}
+        lins = [self.lin0, self.lin1, self.lin2, self.lin3, self.lin4]
+        for kk in range(len(self.chns)):
+            feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(
+                outs1[kk]
+            )
+            diffs[kk] = (feats0[kk] - feats1[kk]) ** 2
+
+        res = [
+            spatial_average(lins[kk].model(diffs[kk]), keepdim=True)
+            for kk in range(len(self.chns))
+        ]
+        val = res[0]
+        for l in range(1, len(self.chns)):
+            val += res[l]
+        return val
+
+
+class ScalingLayer(nn.Module):
+    def __init__(self):
+        super(ScalingLayer, self).__init__()
+        self.register_buffer(
+            "shift", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None]
+        )
+        self.register_buffer(
+            "scale", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None]
+        )
+
+    def forward(self, inp):
+        return (inp - self.shift) / self.scale
+
+
+class NetLinLayer(nn.Module):
+    """A single linear layer which does a 1x1 conv"""
+
+    def __init__(self, chn_in, chn_out=1, use_dropout=False):
+        super(NetLinLayer, self).__init__()
+        layers = (
+            [
+                nn.Dropout(),
+            ]
+            if (use_dropout)
+            else []
+        )
+        layers += [
+            nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),
+        ]
+        self.model = nn.Sequential(*layers)
+
+
+class vgg16(torch.nn.Module):
+    def __init__(self, requires_grad=False, pretrained=True):
+        super(vgg16, self).__init__()
+        vgg_pretrained_features = models.vgg16(pretrained=pretrained).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        self.N_slices = 5
+        for x in range(4):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(4, 9):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(9, 16):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(16, 23):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(23, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        h = self.slice1(X)
+        h_relu1_2 = h
+        h = self.slice2(h)
+        h_relu2_2 = h
+        h = self.slice3(h)
+        h_relu3_3 = h
+        h = self.slice4(h)
+        h_relu4_3 = h
+        h = self.slice5(h)
+        h_relu5_3 = h
+        vgg_outputs = namedtuple(
+            "VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3", "relu5_3"]
+        )
+        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
+        return out
+
+
+def normalize_tensor(x, eps=1e-10):
+    norm_factor = torch.sqrt(torch.sum(x**2, dim=1, keepdim=True))
+    return x / (norm_factor + eps)
+
+
+def spatial_average(x, keepdim=True):
+    return x.mean([2, 3], keepdim=keepdim)