relicense

2022-11-10 12:59:53 -07:00 · 2022-11-10 12:59:53 -07:00 · 1f5b671b67
parent bc626e80e1
commit 1f5b671b67
175 changed files with 847 additions and 20335 deletions
--- a/.dockerignore
+++ b/.dockerignore
@ -1,4 +0,0 @@
-./venv
-./danbooru-aesthetic
-./logs
-*.ckpt
--- a/.gitignore
+++ b/.gitignore
@ -1,59 +0,0 @@
-# OS-generated
-# ------------
-.DS_Store*
-[Tt]humbs.db
-[Dd]esktop.ini
-
-# Programming - general
-*.log
-example.png
-scores.json
-danbooru-aesthetic
-logs
-*.tar
-
-# =========================================================================== #
-# 	Python-related
-# =========================================================================== #
-# src: https://github.com/github/gitignore/blob/master/Python.gitignore
-
-# JetBrains PyCharm / Rider
-.idea/
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-venv/
-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-
-# =========================================================================== #
-# 	Repo-specific
-# =========================================================================== #
-/src/
-
-#Obsidian
-.obsidian/
--- a/.gitmodules
+++ b/.gitmodules
@ -0,0 +1,3 @@
+[submodule "dataset/aesthetic"]
+	path = dataset/aesthetic
+	url = https://github.com/waifu-diffusion/aesthetic
--- a/6
+++ b/6
@ -1,10 +1,6 @@
 FROM pytorch/pytorch:latest

-RUN apt update && \
-    apt install -y git curl unzip vim && \
-    pip install git+https://github.com/derfred/lightning.git@waifu-1.6.0#egg=pytorch-lightning
 RUN mkdir /waifu
 COPY . /waifu/
 WORKDIR /waifu
-RUN grep -v pytorch-lightning requirements.txt > requirements-waifu.txt && \
-    pip install -r requirements-waifu.txt
+RUN pip install -r requirement.txt
--- a/671
+++ b/671
@ -1,14 +1,661 @@
-All rights reserved by the authors.
-You must not distribute the weights provided to you directly or indirectly without explicit consent of the authors.
-You must not distribute harmful, offensive, dehumanizing content or otherwise harmful representations of people or their environments, cultures, religions, etc. produced with the model weights
-or other generated content described in the "Misuse and Malicious Use" section in the model card.
-The model weights are provided for research purposes only.
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007

+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.

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+
+  13. Remote Network Interaction; Use with the GNU General Public License.
+
+  Notwithstanding any other provision of this License, if you modify the
+Program, your modified version must prominently offer all users
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+
+  14. Revised Versions of this License.
+
+  The Free Software Foundation may publish revised and/or new versions of
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+
+  Each version is given a distinguishing version number.  If the
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+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
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+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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+  16. Limitation of Liability.
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+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU Affero General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU Affero General Public License for more details.
+
+    You should have received a copy of the GNU Affero General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If your software can interact with users remotely through a computer
+network, you should also make sure that it provides a way for users to
+get its source.  For example, if your program is a web application, its
+interface could display a "Source" link that leads users to an archive
+of the code.  There are many ways you could offer source, and different
+solutions will be better for different programs; see section 13 for the
+specific requirements.
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU AGPL, see
+<https://www.gnu.org/licenses/>.
--- a/README.md
+++ b/README.md
@ -1,55 +1,28 @@
-
-
 # Waifu Diffusion

-Waifu Diffusion is the name for this project of finetuning Stable Diffusion on images and captions downloaded through Danbooru
+[Waifu Diffusion](https://huggingface.co/hakurei/waifu-diffusion) is the name for this project of finetuning [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5) on anime-styled images.

-(**Note:** This project has **no affiliation with Danbooru.**)
+<img src=https://user-images.githubusercontent.com/26317155/194690196-8da73f2a-039d-4349-8b08-e24e8fd20959.png width=40% height=40%>

-<img src=https://cdn.discordapp.com/attachments/872361510133981234/1016022078635388979/unknown.png?3867929 width=40% height=40%>
+<sub>1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt</sub>

-<sub>Prompt: touhou 1girl komeiji_koishi portrait</sub>
+## Setup

-## Documentation
+```shell
+pip install -r requirements.txt
+```

-[Index](./docs/en/README.md)
+## Project Structure

-[Weights](./docs/en/weights/README.md)
+```
+├── dataset: Dataset preparation and utilities
+│   ├── aesthetic: Aesthetic ranking
+│   └── download: Downloading utilities
+└── trainer: The actual training code
+```

-[Training Guide](./docs/en/training/README.md)
-
-All thanks goes to CompVis and Stability AI for releasing this codebase!
-
-Model Link: https://huggingface.co/hakurei/waifu-diffusion
-
-### Any questions? Come hop on by to our Discord server!
+## License
+Training Code: [AGPL-3.0](LICENSE)
+Model Weights: [CreativeML Open RAIL-M](https://huggingface.co/spaces/CompVis/stable-diffusion-license)

 [![Discord Server](https://discordapp.com/api/guilds/930499730843250783/widget.png?style=banner2)](https://discord.gg/Sx6Spmsgx7)
-
-# Stable Diffusion
-*Stable Diffusion was made possible thanks to a collaboration with [Stability AI](https://stability.ai/) and [Runway](https://runwayml.com/) and builds upon our previous work:*
-
-## Comments 
-
- Our codebase for the diffusion models builds heavily on [OpenAI's ADM codebase](https://github.com/openai/guided-diffusion)
-and [https://github.com/lucidrains/denoising-diffusion-pytorch](https://github.com/lucidrains/denoising-diffusion-pytorch). 
-Thanks for open-sourcing!
-
- The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). 
-
-
-## BibTeX
-
-```
-@misc{rombach2021highresolution,
-      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
-      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
-      year={2021},
-      eprint={2112.10752},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-
-```
-
-
--- a/Stable_Diffusion_v1_Model_Card.md
+++ b/Stable_Diffusion_v1_Model_Card.md
@ -1,140 +0,0 @@
-# Stable Diffusion v1 Model Card
-This model card focuses on the model associated with the Stable Diffusion model, available [here](https://github.com/CompVis/stable-diffusion).
-
-## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [Proprietary](LICENSE)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
-
-      @InProceedings{Rombach_2022_CVPR,
-          author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-          title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-          booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-          month     = {June},
-          year      = {2022},
-          pages     = {10684-10695}
-      }
-
-# Uses
-
-## Direct Use 
-The model is intended for research purposes only. Possible research areas and
-tasks include
-
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
-
-Excluded uses are described below.
-
- ### Misuse, Malicious Use, and Out-of-Scope Use
-_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
-
-
-The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
-#### Out-of-Scope Use
-The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
-#### Misuse and Malicious Use
-Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
-
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
-
-## Limitations and Bias
-
-### Limitations
-
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
-  [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
-  and is not fit for product use without additional safety mechanisms and
-  considerations.
-
-### Bias
-While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. 
-Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), 
-which consists of images that are primarily limited to English descriptions. 
-Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. 
-This affects the overall output of the model, as white and western cultures are often set as the default. Further, the 
-ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
-
-
-## Training
-
-**Training Data**
-The model developers used the following dataset for training the model:
-
- LAION-2B (en) and subsets thereof (see next section)
-
-**Training Procedure**
-Stable Diffusion v1 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, 
-
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
-
-We currently provide three checkpoints, `sd-v1-1.ckpt`, `sd-v1-2.ckpt` and `sd-v1-3.ckpt`,
-which were trained as follows,
-
- `sd-v1-1.ckpt`: 237k steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
-  194k steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- `sd-v1-2.ckpt`: Resumed from `sd-v1-1.ckpt`.
-  515k steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
-filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-
-
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
-
-## Evaluation Results 
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
-steps show the relative improvements of the checkpoints:
-
-![pareto](assets/v1-variants-scores.jpg) 
-
-Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution.  Not optimized for FID scores.
-## Environmental Impact
-
-**Stable Diffusion v1** **Estimated Emissions**
-Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
-
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
-## Citation
-    @InProceedings{Rombach_2022_CVPR,
-        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-        month     = {June},
-        year      = {2022},
-        pages     = {10684-10695}
-    }
-
-*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
-
--- a/Gradio.cmd
+++ b/Gradio.cmd
@ -1,7 +0,0 @@
-@echo off
-IF NOT EXIST CONDA umamba create -r conda -f environment.yaml -y
-call conda\condabin\activate.bat ldm
-cls
-
-:PROMPT
-python scripts/txt2img_gradio.py
--- a/aesthetics/aesthetics.py
+++ b/aesthetics/aesthetics.py
@ -1,142 +0,0 @@
-import webdataset as wds
-from PIL import Image
-import io
-import matplotlib.pyplot as plt
-import os
-import json
-
-from warnings import filterwarnings
-
-
-os.environ["CUDA_VISIBLE_DEVICES"] = "1"    # choose GPU if you are on a multi GPU server
-import numpy as np
-import torch
-import pytorch_lightning as pl
-import torch.nn as nn
-from torchvision import datasets, transforms
-import tqdm
-
-from os.path import join
-from datasets import load_dataset
-import pandas as pd
-from torch.utils.data import Dataset, DataLoader
-import json
-
-import clip
-
-
-from PIL import Image, ImageFile
-
-
-#####  This script will predict the aesthetic score for this image file:
-
-img_path = "../250k_data-0/img/000baa665498e7a61130d7662f81e698.jpg"
-
-
-
-
-
-# if you changed the MLP architecture during training, change it also here:
-class MLP(pl.LightningModule):
-    def __init__(self, input_size, xcol='emb', ycol='avg_rating'):
-        super().__init__()
-        self.input_size = input_size
-        self.xcol = xcol
-        self.ycol = ycol
-        self.layers = nn.Sequential(
-            nn.Linear(self.input_size, 1024),
-            #nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(1024, 128),
-            #nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(128, 64),
-            #nn.ReLU(),
-            nn.Dropout(0.1),
-
-            nn.Linear(64, 16),
-            #nn.ReLU(),
-
-            nn.Linear(16, 1)
-        )
-
-    def forward(self, x):
-        return self.layers(x)
-
-    def training_step(self, batch, batch_idx):
-            x = batch[self.xcol]
-            y = batch[self.ycol].reshape(-1, 1)
-            x_hat = self.layers(x)
-            loss = F.mse_loss(x_hat, y)
-            return loss
-    
-    def validation_step(self, batch, batch_idx):
-        x = batch[self.xcol]
-        y = batch[self.ycol].reshape(-1, 1)
-        x_hat = self.layers(x)
-        loss = F.mse_loss(x_hat, y)
-        return loss
-
-    def configure_optimizers(self):
-        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
-        return optimizer
-
-def normalized(a, axis=-1, order=2):
-    import numpy as np  # pylint: disable=import-outside-toplevel
-
-    l2 = np.atleast_1d(np.linalg.norm(a, order, axis))
-    l2[l2 == 0] = 1
-    return a / np.expand_dims(l2, axis)
-
-
-model = MLP(768)  # CLIP embedding dim is 768 for CLIP ViT L 14
-
-s = torch.load("sac+logos+ava1-l14-linearMSE.pth")   # load the model you trained previously or the model available in this repo
-
-model.load_state_dict(s)
-
-model.to("cuda")
-model.eval()
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model2, preprocess = clip.load("ViT-L/14", device=device)  #RN50x64   
-
-@torch.inference_mode()
-def aesthetic(img_path):
-    pil_image = Image.open(img_path)
-    image = preprocess(pil_image).unsqueeze(0).to(device)
-    with torch.no_grad():
-        image_features = model2.encode_image(image)
-    im_emb_arr = normalized(image_features.cpu().detach().numpy())
-    prediction = model(torch.from_numpy(im_emb_arr).to(device).type(torch.cuda.FloatTensor))
-    return prediction.item()
-
-import json
-import glob
-import shutil
-
-imdir = '../250k_data-0/img/'
-ext = ['png', 'jpg', 'jpeg', 'bmp']
-images = []
-[images.extend(glob.glob(imdir + '*.' + e)) for e in ext]
-
-aesthetic_scores = {}
-
-try:
-    for i in tqdm.tqdm(images):
-        try:
-            score = aesthetic(i)
-        except:
-            print(f'skipping {i}')
-            continue
-        if score < 5.0:
-            shutil.move(i, i.replace('img', 'nonaesthetic'))
-        elif score > 6.0:
-            shutil.move(i, i.replace('img', 'aesthetic'))
-        aesthetic_scores[i] = score
-except KeyboardInterrupt:
-    pass
-finally:
-    with open('scores.json', 'w') as f:
-        f.write(json.dumps(aesthetic_scores))
--- a/aesthetics/sac+logos+ava1-l14-linearMSE.pth
+++ b/aesthetics/sac+logos+ava1-l14-linearMSE.pth
--- a/assets/a-painting-of-a-fire.png
+++ b/assets/a-painting-of-a-fire.png
--- a/assets/a-photograph-of-a-fire.png
+++ b/assets/a-photograph-of-a-fire.png
--- a/assets/a-shirt-with-a-fire-printed-on-it.png
+++ b/assets/a-shirt-with-a-fire-printed-on-it.png
--- a/assets/a-shirt-with-the-inscription-'fire'.png
+++ b/assets/a-shirt-with-the-inscription-'fire'.png
--- a/assets/a-watercolor-painting-of-a-fire.png
+++ b/assets/a-watercolor-painting-of-a-fire.png
--- a/assets/birdhouse.png
+++ b/assets/birdhouse.png
--- a/assets/fire.png
+++ b/assets/fire.png
--- a/assets/inpainting.png
+++ b/assets/inpainting.png
--- a/assets/modelfigure.png
+++ b/assets/modelfigure.png
--- a/assets/rdm-preview.jpg
+++ b/assets/rdm-preview.jpg
--- a/assets/reconstruction1.png
+++ b/assets/reconstruction1.png
--- a/assets/reconstruction2.png
+++ b/assets/reconstruction2.png
--- a/assets/results.gif
+++ b/assets/results.gif
--- a/assets/stable-samples/img2img/mountains-1.png
+++ b/assets/stable-samples/img2img/mountains-1.png
--- a/assets/stable-samples/img2img/mountains-2.png
+++ b/assets/stable-samples/img2img/mountains-2.png
--- a/assets/stable-samples/img2img/mountains-3.png
+++ b/assets/stable-samples/img2img/mountains-3.png
--- a/assets/stable-samples/img2img/sketch-mountains-input.jpg
+++ b/assets/stable-samples/img2img/sketch-mountains-input.jpg
--- a/assets/stable-samples/img2img/upscaling-in.png
+++ b/assets/stable-samples/img2img/upscaling-in.png
--- a/assets/stable-samples/img2img/upscaling-out.png
+++ b/assets/stable-samples/img2img/upscaling-out.png
--- a/assets/stable-samples/txt2img/000002025.png
+++ b/assets/stable-samples/txt2img/000002025.png
--- a/assets/stable-samples/txt2img/000002035.png
+++ b/assets/stable-samples/txt2img/000002035.png
--- a/assets/stable-samples/txt2img/merged-0005.png
+++ b/assets/stable-samples/txt2img/merged-0005.png
--- a/assets/stable-samples/txt2img/merged-0006.png
+++ b/assets/stable-samples/txt2img/merged-0006.png
--- a/assets/stable-samples/txt2img/merged-0007.png
+++ b/assets/stable-samples/txt2img/merged-0007.png
--- a/assets/the-earth-is-on-fire,-oil-on-canvas.png
+++ b/assets/the-earth-is-on-fire,-oil-on-canvas.png
--- a/assets/txt2img-convsample.png
+++ b/assets/txt2img-convsample.png
--- a/assets/txt2img-preview.png
+++ b/assets/txt2img-preview.png
--- a/assets/v1-variants-scores.jpg
+++ b/assets/v1-variants-scores.jpg
--- a/configs/autoencoder/autoencoder_kl_16x16x16.yaml
+++ b/configs/autoencoder/autoencoder_kl_16x16x16.yaml
@ -1,54 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 16
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 16
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16]
-      dropout: 0.0
-
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2
--- a/configs/autoencoder/autoencoder_kl_32x32x4.yaml
+++ b/configs/autoencoder/autoencoder_kl_32x32x4.yaml
@ -1,53 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 4
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 4
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [ ]
-      dropout: 0.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2
--- a/configs/autoencoder/autoencoder_kl_64x64x3.yaml
+++ b/configs/autoencoder/autoencoder_kl_64x64x3.yaml
@ -1,54 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 3
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 3
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,2,4 ]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [ ]
-      dropout: 0.0
-
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2
--- a/configs/autoencoder/autoencoder_kl_8x8x64.yaml
+++ b/configs/autoencoder/autoencoder_kl_8x8x64.yaml
@ -1,53 +0,0 @@
-model:
-  base_learning_rate: 4.5e-6
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 64
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 64
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,1,2,2,4,4]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [16,8]
-      dropout: 0.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 12
-    wrap: True
-    train:
-      target: ldm.data.imagenet.ImageNetSRTrain
-      params:
-        size: 256
-        degradation: pil_nearest
-    validation:
-      target: ldm.data.imagenet.ImageNetSRValidation
-      params:
-        size: 256
-        degradation: pil_nearest
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 1000
-        max_images: 8
-        increase_log_steps: True
-
-  trainer:
-    benchmark: True
-    accumulate_grad_batches: 2
--- a/configs/latent-diffusion/celebahq-ldm-vq-4.yaml
+++ b/configs/latent-diffusion/celebahq-ldm-vq-4.yaml
@ -1,86 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ckpt_path: models/first_stage_models/vq-f4/model.ckpt
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 48
-    num_workers: 5
-    wrap: false
-    train:
-      target: taming.data.faceshq.CelebAHQTrain
-      params:
-        size: 256
-    validation:
-      target: taming.data.faceshq.CelebAHQValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True
--- a/configs/latent-diffusion/cin-ldm-vq-f8.yaml
+++ b/configs/latent-diffusion/cin-ldm-vq-f8.yaml
@ -1,98 +0,0 @@
-model:
-  base_learning_rate: 1.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: class_label
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 256
-        attention_resolutions:
-        #note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 32 for f8
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 4
-        num_head_channels: 32
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 512
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 4
-        n_embed: 16384
-        ckpt_path: configs/first_stage_models/vq-f8/model.yaml
-        ddconfig:
-          double_z: false
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 32
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.ClassEmbedder
-      params:
-        embed_dim: 512
-        key: class_label
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 64
-    num_workers: 12
-    wrap: false
-    train:
-      target: ldm.data.imagenet.ImageNetTrain
-      params:
-        config:
-          size: 256
-    validation:
-      target: ldm.data.imagenet.ImageNetValidation
-      params:
-        config:
-          size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True
--- a/configs/latent-diffusion/cin256-v2.yaml
+++ b/configs/latent-diffusion/cin256-v2.yaml
@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 0.0001
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: class_label
-    image_size: 64
-    channels: 3
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss
-    use_ema: False
-    
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 192
-        attention_resolutions:
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 5
-        num_heads: 1
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 512
-    
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.ClassEmbedder
-      params:
-        n_classes: 1001
-        embed_dim: 512
-        key: class_label
--- a/configs/latent-diffusion/ffhq-ldm-vq-4.yaml
+++ b/configs/latent-diffusion/ffhq-ldm-vq-4.yaml
@ -1,85 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        embed_dim: 3
-        n_embed: 8192
-        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 42
-    num_workers: 5
-    wrap: false
-    train:
-      target: taming.data.faceshq.FFHQTrain
-      params:
-        size: 256
-    validation:
-      target: taming.data.faceshq.FFHQValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True
--- a/configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml
+++ b/configs/latent-diffusion/lsun_bedrooms-ldm-vq-4.yaml
@ -1,85 +0,0 @@
-model:
-  base_learning_rate: 2.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0195
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    image_size: 64
-    channels: 3
-    monitor: val/loss_simple_ema
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 64
-        in_channels: 3
-        out_channels: 3
-        model_channels: 224
-        attention_resolutions:
-        # note: this isn\t actually the resolution but
-        # the downsampling factor, i.e. this corresnponds to
-        # attention on spatial resolution 8,16,32, as the
-        # spatial reolution of the latents is 64 for f4
-        - 8
-        - 4
-        - 2
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        num_head_channels: 32
-    first_stage_config:
-      target: ldm.models.autoencoder.VQModelInterface
-      params:
-        ckpt_path: configs/first_stage_models/vq-f4/model.yaml
-        embed_dim: 3
-        n_embed: 8192
-        ddconfig:
-          double_z: false
-          z_channels: 3
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions: []
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config: __is_unconditional__
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 48
-    num_workers: 5
-    wrap: false
-    train:
-      target: ldm.data.lsun.LSUNBedroomsTrain
-      params:
-        size: 256
-    validation:
-      target: ldm.data.lsun.LSUNBedroomsValidation
-      params:
-        size: 256
-
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True
--- a/configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml
+++ b/configs/latent-diffusion/lsun_churches-ldm-kl-8.yaml
@ -1,91 +0,0 @@
-model:
-  base_learning_rate: 5.0e-5   # set to target_lr by starting main.py with '--scale_lr False'
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.0155
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    loss_type: l1
-    first_stage_key: "image"
-    cond_stage_key: "image"
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: False
-    concat_mode: False
-    scale_by_std: True
-    monitor: 'val/loss_simple_ema'
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [10000]
-        cycle_lengths: [10000000000000]
-        f_start: [1.e-6]
-        f_max: [1.]
-        f_min: [ 1.]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        in_channels: 4
-        out_channels: 4
-        model_channels: 192
-        attention_resolutions: [ 1, 2, 4, 8 ]   # 32, 16, 8, 4
-        num_res_blocks: 2
-        channel_mult: [ 1,2,2,4,4 ]  # 32, 16, 8, 4, 2
-        num_heads: 8
-        use_scale_shift_norm: True
-        resblock_updown: True
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: "val/rec_loss"
-        ckpt_path: "models/first_stage_models/kl-f8/model.ckpt"
-        ddconfig:
-          double_z: True
-          z_channels: 4
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
-          num_res_blocks: 2
-          attn_resolutions: [ ]
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-
-    cond_stage_config: "__is_unconditional__"
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 96
-    num_workers: 5
-    wrap: False
-    train:
-      target: ldm.data.lsun.LSUNChurchesTrain
-      params:
-        size: 256
-    validation:
-      target: ldm.data.lsun.LSUNChurchesValidation
-      params:
-        size: 256
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 5000
-        max_images: 8
-        increase_log_steps: False
-
-
-  trainer:
-    benchmark: True
--- a/configs/latent-diffusion/txt2img-1p4B-eval.yaml
+++ b/configs/latent-diffusion/txt2img-1p4B-eval.yaml
@ -1,71 +0,0 @@
-model:
-  base_learning_rate: 5.0e-05
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.012
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 32
-    channels: 4
-    cond_stage_trainable: true
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32
-        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_heads: 8
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 1280
-        use_checkpoint: true
-        legacy: False
-
-    first_stage_config:
-      target: ldm.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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.BERTEmbedder
-      params:
-        n_embed: 1280
-        n_layer: 32
--- a/configs/retrieval-augmented-diffusion/768x768.yaml
+++ b/configs/retrieval-augmented-diffusion/768x768.yaml
@ -1,68 +0,0 @@
-model:
-  base_learning_rate: 0.0001
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.0015
-    linear_end: 0.015
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: jpg
-    cond_stage_key: nix
-    image_size: 48
-    channels: 16
-    cond_stage_trainable: false
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_by_std: false
-    scale_factor: 0.22765929
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 48
-        in_channels: 16
-        out_channels: 16
-        model_channels: 448
-        attention_resolutions:
-        - 4
-        - 2
-        - 1
-        num_res_blocks: 2
-        channel_mult:
-        - 1
-        - 2
-        - 3
-        - 4
-        use_scale_shift_norm: false
-        resblock_updown: false
-        num_head_channels: 32
-        use_spatial_transformer: true
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: true
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        monitor: val/rec_loss
-        embed_dim: 16
-        ddconfig:
-          double_z: true
-          z_channels: 16
-          resolution: 256
-          in_channels: 3
-          out_ch: 3
-          ch: 128
-          ch_mult:
-          - 1
-          - 1
-          - 2
-          - 2
-          - 4
-          num_res_blocks: 2
-          attn_resolutions:
-          - 16
-          dropout: 0.0
-        lossconfig:
-          target: torch.nn.Identity
-    cond_stage_config:
-      target: torch.nn.Identity
--- a/configs/stable-diffusion/v1-4-finetune-test.yaml
+++ b/configs/stable-diffusion/v1-4-finetune-test.yaml
@ -1,123 +0,0 @@
-model:
-  base_learning_rate: 7.5e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        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_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ckpt_path: "../latent-diffusion/logs/original/checkpoints/last.ckpt"
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 512
-          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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-      params:
-        penultimate: true # use 2nd last layer - https://arxiv.org/pdf/2205.11487.pdf D.1
-        extended_mode: 3 # extend clip context to 225 tokens - as per NAI blogpost
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 2 
-    num_workers: 2
-    wrap: false
-    train:
-      target: ldm.data.localdanboorubase.LocalDanbooruBase
-      params:
-        data_root: '../dataset'
-        size: 512
-        mode: "train"
-        ucg: 0.1 # unconditional guidance training
-    validation:
-      target: ldm.data.localdanboorubase.LocalDanbooruBase
-      params:
-        data_root: '../dataset'
-        size: 512
-        mode: "val"
-        val_split: 64
-        ucg: 0.1
-
-lightning:
-  modelcheckpoint:
-    params:
-      every_n_train_steps: 500
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 500
-        max_images: 4
-        increase_log_steps: False
-        log_first_step: False
-        log_images_kwargs:
-          use_ema_scope: False
-          inpaint: False
-          plot_progressive_rows: False
-          plot_diffusion_rows: False
-          N: 4
-          ddim_steps: 50
-  trainer:
-      precision: 16
-      amp_backend: "native"
-      strategy: "fsdp"
-      benchmark: True
-      limit_val_batches: 0
-      num_sanity_val_steps: 0
-      accumulate_grad_batches: 1
--- a/configs/stable-diffusion/v1-4-vae.yaml
+++ b/configs/stable-diffusion/v1-4-vae.yaml
@ -1,62 +0,0 @@
-model:
-  base_learning_rate: 1.5e-7
-  target: ldm.models.autoencoder.AutoencoderKL
-  params:
-    monitor: "val/rec_loss"
-    embed_dim: 4
-    lossconfig:
-      target: ldm.modules.losses.LPIPSWithDiscriminator
-      params:
-        disc_start: 50001
-        kl_weight: 0.000001
-        disc_weight: 0.5
-
-    ddconfig:
-      double_z: True
-      z_channels: 4
-      resolution: 256
-      in_channels: 3
-      out_ch: 3
-      ch: 128
-      ch_mult: [ 1,2,4,4 ]  # num_down = len(ch_mult)-1
-      num_res_blocks: 2
-      attn_resolutions: [ ]
-      dropout: 0.0
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    num_workers: 16
-    batch_size: 16
-    wrap: True
-    train:
-      target: ldm.data.localdanbooruvae.LocalDanbooruBaseVAE
-      params:
-        data_root: "../dataset"
-        size: 256
-        mode: "train"
-        downscale_f: 8
-    validation:
-      target: ldm.data.localdanbooruvae.LocalDanbooruBaseVAE
-      params:
-        data_root: "../dataset"
-        size: 256
-        mode: "val"
-        val_split: 64
-        downscale_f: 8
-
-lightning:
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 200
-        max_images: 4
-        increase_log_steps: True
-
-  trainer:
-    find_unused_parameters: True
-    benchmark: True
-    limit_val_batches: 0
-    num_sanity_val_steps: 0
-    accumulate_grad_batches: 1
--- a/configs/stable-diffusion/v1-finetune-4gpu.yaml
+++ b/configs/stable-diffusion/v1-finetune-4gpu.yaml
@ -1,117 +0,0 @@
-model:
-  base_learning_rate: 5.0e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        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_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 512
-          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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-      params:
-        penultimate: True
-        extended_mode: True
-        max_chunks: 3
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 4
-    num_workers: 4
-    wrap: false
-    train:
-      target: ldm.data.local.LocalBase
-      params:
-        size: 512
-        mode: "train"
-    validation:
-      target: ldm.data.local.LocalBase
-      params:
-        size: 512
-        mode: "val"
-        val_split: 64
-
-lightning:
-  modelcheckpoint:
-    params:
-      every_n_train_steps: 500
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 500
-        max_images: 4
-        increase_log_steps: False
-        log_first_step: False
-        log_images_kwargs:
-          use_ema_scope: False
-          inpaint: False
-          plot_progressive_rows: False
-          plot_diffusion_rows: False
-          N: 4
-          ddim_steps: 50
-
-trainer:
-    benchmark: True
-    val_check_interval: 5000000
-    num_sanity_val_steps: 0
-    accumulate_grad_batches: 1
--- a/configs/stable-diffusion/v1-finetune-8gpu.yaml
+++ b/configs/stable-diffusion/v1-finetune-8gpu.yaml
@ -1,113 +0,0 @@
-model:
-  base_learning_rate: 1.5e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        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_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 512
-          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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 4
-    num_workers: 4
-    wrap: false
-    train:
-      target: ldm.data.local.LocalBase
-      params:
-        size: 512
-        mode: "train"
-    validation:
-      target: ldm.data.local.LocalBase
-      params:
-        size: 512
-        mode: "val"
-        val_split: 64
-
-lightning:
-  modelcheckpoint:
-    params:
-      every_n_train_steps: 500
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 500
-        max_images: 4
-        increase_log_steps: False
-        log_first_step: False
-        log_images_kwargs:
-          use_ema_scope: False
-          inpaint: False
-          plot_progressive_rows: False
-          plot_diffusion_rows: False
-          N: 4
-          ddim_steps: 50
-
-trainer:
-    benchmark: True
-    val_check_interval: 5000000
-    num_sanity_val_steps: 0
-    accumulate_grad_batches: 1
--- a/configs/stable-diffusion/v1-finetune-danbooru-8gpu.yaml
+++ b/configs/stable-diffusion/v1-finetune-danbooru-8gpu.yaml
@ -1,113 +0,0 @@
-model:
-  base_learning_rate: 1.5e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        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_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 512
-          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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-
-data:
-  target: ldm.data.localdanbooru.DanbooruWebDataModuleFromConfig
-  params:
-    tar_base: "links.tar"
-    batch_size: 1
-    num_workers: 1
-    max_size: 768
-    resize: false
-    flip_p: 0.5
-    image_key: "image"
-    copyright_rate: 1.0
-    character_rate: 1.0
-    general_rate: 1.0
-    artist_rate: 1.0
-    normalize: true
-    caption_shuffle: true
-    random_order: true
-
-lightning:
-  modelcheckpoint:
-    params:
-      every_n_train_steps: 500
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 500
-        max_images: 4
-        increase_log_steps: False
-        log_first_step: False
-        log_images_kwargs:
-          use_ema_scope: False
-          inpaint: False
-          plot_progressive_rows: False
-          plot_diffusion_rows: False
-          N: 4
-          ddim_steps: 50
-
-trainer:
-    benchmark: True
-    val_check_interval: 5000000
-    num_sanity_val_steps: 0
-    accumulate_grad_batches: 1
--- a/configs/stable-diffusion/v1-finetune-danboorubase-8gpu.yaml
+++ b/configs/stable-diffusion/v1-finetune-danboorubase-8gpu.yaml
@ -1,116 +0,0 @@
-model:
-  base_learning_rate: 1.5e-06
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        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_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 512
-          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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 1
-    num_workers: 1
-    wrap: false
-    train:
-      target: ldm.data.localdanboorubase.LocalDanbooruBase
-      params:
-        data_root: "./dataset"
-        size: 768
-        mode: "train"
-    validation:
-      target: ldm.data.localdanboorubase.LocalDanbooruBase
-      params:
-        data_root: "./dataset"
-        size: 768
-        mode: "val"
-        val_split: 64
-
-lightning:
-  find_unused_parameters: False
-  modelcheckpoint:
-    params:
-      every_n_train_steps: 2000
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 2000
-        max_images: 2
-        increase_log_steps: False
-        log_first_step: False
-        log_images_kwargs:
-          use_ema_scope: False
-          inpaint: False
-          plot_progressive_rows: False
-          plot_diffusion_rows: False
-          N: 4
-          ddim_steps: 50
-
-trainer:
-    benchmark: True
-    val_check_interval: 5000000
-    num_sanity_val_steps: 0
-    accumulate_grad_batches: 1
--- a/configs/stable-diffusion/v1-finetune.yaml
+++ b/configs/stable-diffusion/v1-finetune.yaml
@ -1,100 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 50
-    timesteps: 1000
-    first_stage_key: image
-    cond_stage_key: caption
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: true   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 1 ] # NOTE for resuming. use 10000 if starting from scratch
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        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_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.models.autoencoder.AutoencoderKL
-      params:
-        embed_dim: 4
-        monitor: val/rec_loss
-        ddconfig:
-          double_z: true
-          z_channels: 4
-          resolution: 512
-          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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 1
-    num_workers: 1
-    wrap: false
-    train:
-      target: ldm.data.local.LocalBase
-      params:
-        size: 512
-    validation:
-      target: ldm.data.local.LocalBase
-      params:
-        size: 512
-
-lightning:
-  modelcheckpoint:
-    params:
-      every_n_train_steps: 500
-  callbacks:
-    image_logger:
-      target: main.ImageLogger
-      params:
-        batch_frequency: 500
-        max_images: 4
-        increase_log_steps: False
-
-  trainer:
-    benchmark: True
-    max_steps: 6100
--- a/configs/stable-diffusion/v1-inference.yaml
+++ b/configs/stable-diffusion/v1-inference.yaml
@ -1,73 +0,0 @@
-model:
-  base_learning_rate: 1.0e-04
-  target: ldm.models.diffusion.ddpm.LatentDiffusion
-  params:
-    linear_start: 0.00085
-    linear_end: 0.0120
-    num_timesteps_cond: 1
-    log_every_t: 200
-    timesteps: 1000
-    first_stage_key: "jpg"
-    cond_stage_key: "txt"
-    image_size: 64
-    channels: 4
-    cond_stage_trainable: false   # Note: different from the one we trained before
-    conditioning_key: crossattn
-    monitor: val/loss_simple_ema
-    scale_factor: 0.18215
-    use_ema: False
-
-    scheduler_config: # 10000 warmup steps
-      target: ldm.lr_scheduler.LambdaLinearScheduler
-      params:
-        warm_up_steps: [ 10000 ]
-        cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
-        f_start: [ 1.e-6 ]
-        f_max: [ 1. ]
-        f_min: [ 1. ]
-
-    unet_config:
-      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
-      params:
-        image_size: 32 # unused
-        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_heads: 8
-        use_spatial_transformer: True
-        transformer_depth: 1
-        context_dim: 768
-        use_checkpoint: True
-        legacy: False
-
-    first_stage_config:
-      target: ldm.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
-
-    cond_stage_config:
-      target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
-      params:
-        penultimate: True
-        extended_mode: 3
--- a/data/DejaVuSans.ttf
+++ b/data/DejaVuSans.ttf
--- a/data/example_conditioning/superresolution/sample_0.jpg
+++ b/data/example_conditioning/superresolution/sample_0.jpg
--- a/data/example_conditioning/text_conditional/sample_0.txt
+++ b/data/example_conditioning/text_conditional/sample_0.txt
@ -1 +0,0 @@
-A basket of cerries
--- a/data/imagenet_clsidx_to_label.txt
+++ b/data/imagenet_clsidx_to_label.txt
--- a/data/imagenet_train_hr_indices.p
+++ b/data/imagenet_train_hr_indices.p
--- a/data/imagenet_val_hr_indices.p
+++ b/data/imagenet_val_hr_indices.p
--- a/data/index_synset.yaml
+++ b/data/index_synset.yaml
--- a/data/inpainting_examples/6458524847_2f4c361183_k.png
+++ b/data/inpainting_examples/6458524847_2f4c361183_k.png
--- a/data/inpainting_examples/6458524847_2f4c361183_k_mask.png
+++ b/data/inpainting_examples/6458524847_2f4c361183_k_mask.png
--- a/data/inpainting_examples/8399166846_f6fb4e4b8e_k.png
+++ b/data/inpainting_examples/8399166846_f6fb4e4b8e_k.png
--- a/data/inpainting_examples/8399166846_f6fb4e4b8e_k_mask.png
+++ b/data/inpainting_examples/8399166846_f6fb4e4b8e_k_mask.png
--- a/data/inpainting_examples/alex-iby-G_Pk4D9rMLs.png
+++ b/data/inpainting_examples/alex-iby-G_Pk4D9rMLs.png
--- a/data/inpainting_examples/alex-iby-G_Pk4D9rMLs_mask.png
+++ b/data/inpainting_examples/alex-iby-G_Pk4D9rMLs_mask.png
--- a/data/inpainting_examples/bench2.png
+++ b/data/inpainting_examples/bench2.png
--- a/data/inpainting_examples/bench2_mask.png
+++ b/data/inpainting_examples/bench2_mask.png
--- a/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0.png
+++ b/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0.png
--- a/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0_mask.png
+++ b/data/inpainting_examples/bertrand-gabioud-CpuFzIsHYJ0_mask.png
--- a/data/inpainting_examples/billow926-12-Wc-Zgx6Y.png
+++ b/data/inpainting_examples/billow926-12-Wc-Zgx6Y.png
--- a/data/inpainting_examples/billow926-12-Wc-Zgx6Y_mask.png
+++ b/data/inpainting_examples/billow926-12-Wc-Zgx6Y_mask.png
--- a/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png
+++ b/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png
--- a/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png
+++ b/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png
--- a/data/inpainting_examples/photo-1583445095369-9c651e7e5d34.png
+++ b/data/inpainting_examples/photo-1583445095369-9c651e7e5d34.png
--- a/data/inpainting_examples/photo-1583445095369-9c651e7e5d34_mask.png
+++ b/data/inpainting_examples/photo-1583445095369-9c651e7e5d34_mask.png
--- a/dataset/download/download.py
+++ b/dataset/download/download.py
@ -1,137 +1,137 @@
-from inspect import trace
-import os
-import json
-import requests
-import multiprocessing
-import tqdm
-import webdataset
-from concurrent import futures
-import io
-import tarfile
-import glob
-import uuid
-
-from PIL import Image, ImageOps
-
-# downloads URLs from JSON
-
-import argparse
-import shutil
-import numpy as np
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--file', '-f', type=str, required=False, default='links.json')
-parser.add_argument('--out_file', '-o', type=str, required=False, default='dataset-%06d.tar')
-parser.add_argument('--max_size', '-m', type=int, required=False, default=4294967296)
-parser.add_argument('--threads', '-p', required=False, default=16, type=int)
-parser.add_argument('--resize', '-r', required=False, default=512, type=int)
-args = parser.parse_args()
-
-def resize_image(image: Image, max_size=(512,512), center_crop=True):
-    if not center_crop:
-        image = ImageOps.contain(image, max_size, Image.LANCZOS)
-        # resize to integer multiple of 64
-        w, h = image.size
-        w, h = map(lambda x: x - x % 64, (w, h))
-
-        ratio = w / h
-        src_ratio = image.width / image.height
-
-        src_w = w if ratio > src_ratio else image.width * h // image.height
-        src_h = h if ratio <= src_ratio else image.height * w // image.width
-
-        resized = image.resize((src_w, src_h), resample=Image.LANCZOS)
-        res = Image.new("RGB", (w, h))
-        res.paste(resized, box=(w // 2 - src_w // 2, h // 2 - src_h // 2))
-    else:
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-        img = np.array(image).astype(np.uint8)
-        crop = min(img.shape[0], img.shape[1])
-        h, w, = img.shape[0], img.shape[1]
-        img = img[(h - crop) // 2:(h + crop) // 2,
-            (w - crop) // 2:(w + crop) // 2]
-        res = Image.fromarray(img)
-        res = res.resize(max_size, resample=Image.LANCZOS)
-
-    return res
-
-class DownloadManager():
-    def __init__(self, max_threads: int = 32):
-        self.failed_downloads = []
-        self.max_threads = max_threads
-        self.uuid = str(uuid.uuid1())
-    
-    # args = (post_id, link, caption_data)
-    def download(self, args_thread):
-        try:
-            image = Image.open(requests.get(args_thread[1], stream=True).raw).convert('RGB')
-            if args.resize:
-                image = resize_image(image, max_size=(args.resize, args.resize))
-            image_bytes = io.BytesIO()
-            image.save(image_bytes, format='PNG')
-            __key__ = '%07d' % int(args_thread[0])
-            image = image_bytes.getvalue()
-            caption = str(json.dumps(args_thread[2]))
-
-            with open(f'{self.uuid}/{__key__}.image', 'wb') as f:
-                f.write(image)
-            with open(f'{self.uuid}/{__key__}.caption', 'w') as f:
-                f.write(caption)
-
-        except Exception as e:
-            import traceback
-            print(e, traceback.print_exc())
-            self.failed_downloads.append((args_thread[0], args_thread[1], args_thread[2]))
-    
-    def download_urls(self, file_path):
-        with open(file_path) as f:
-            data = json.load(f)
-        thread_args = []
-
-        delimiter = '\\' if os.name == 'nt' else '/'
-
-        self.uuid = (file_path.split(delimiter)[-1]).split('.')[0]
-        
-        if not os.path.exists(f'./{self.uuid}'):
-            os.mkdir(f'{self.uuid}')
-
-        print(f'Loading {file_path} for downloading on {self.max_threads} threads... Writing to dataset {self.uuid}')
-
-        # create initial thread_args
-        for k, v in tqdm.tqdm(data.items()):
-            thread_args.append((k, v['file_url'], v))
-        
-        # divide thread_args into chunks divisible by max_threads
-        chunks = []
-        for i in range(0, len(thread_args), self.max_threads):
-            chunks.append(thread_args[i:i+self.max_threads])
-        
-        print(f'Downloading {len(thread_args)} images...')
-
-        # download chunks synchronously
-        for chunk in tqdm.tqdm(chunks):
-            with futures.ThreadPoolExecutor(args.threads) as p:
-                p.map(self.download, chunk)
-
-        if len(self.failed_downloads) > 0:
-            print("Failed downloads:")
-            for i in self.failed_downloads:
-                print(i[0])
-            print("\n")
-        
-        # put things into tar
-        print(f'Writing webdataset to {self.uuid}')
-        archive = tarfile.open(f'{self.uuid}.tar', 'w')
-        files = glob.glob(f'{self.uuid}/*')
-        for f in tqdm.tqdm(files):
-            archive.add(f, f.split(delimiter)[-1])
-        
-        archive.close()
-        
-        print('Cleaning up...')
-        shutil.rmtree(self.uuid)
-        
-if __name__ == '__main__':
-    dm = DownloadManager(max_threads=args.threads)
-    dm.download_urls(args.file)
+from inspect import trace
+import os
+import json
+import requests
+import multiprocessing
+import tqdm
+import webdataset
+from concurrent import futures
+import io
+import tarfile
+import glob
+import uuid
+
+from PIL import Image, ImageOps
+
+# downloads URLs from JSON
+
+import argparse
+import shutil
+import numpy as np
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--file', '-f', type=str, required=False, default='links.json')
+parser.add_argument('--out_file', '-o', type=str, required=False, default='dataset-%06d.tar')
+parser.add_argument('--max_size', '-m', type=int, required=False, default=4294967296)
+parser.add_argument('--threads', '-p', required=False, default=16, type=int)
+parser.add_argument('--resize', '-r', required=False, default=512, type=int)
+args = parser.parse_args()
+
+def resize_image(image: Image, max_size=(512,512), center_crop=True):
+    if not center_crop:
+        image = ImageOps.contain(image, max_size, Image.LANCZOS)
+        # resize to integer multiple of 64
+        w, h = image.size
+        w, h = map(lambda x: x - x % 64, (w, h))
+
+        ratio = w / h
+        src_ratio = image.width / image.height
+
+        src_w = w if ratio > src_ratio else image.width * h // image.height
+        src_h = h if ratio <= src_ratio else image.height * w // image.width
+
+        resized = image.resize((src_w, src_h), resample=Image.LANCZOS)
+        res = Image.new("RGB", (w, h))
+        res.paste(resized, box=(w // 2 - src_w // 2, h // 2 - src_h // 2))
+    else:
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+        img = np.array(image).astype(np.uint8)
+        crop = min(img.shape[0], img.shape[1])
+        h, w, = img.shape[0], img.shape[1]
+        img = img[(h - crop) // 2:(h + crop) // 2,
+            (w - crop) // 2:(w + crop) // 2]
+        res = Image.fromarray(img)
+        res = res.resize(max_size, resample=Image.LANCZOS)
+
+    return res
+
+class DownloadManager():
+    def __init__(self, max_threads: int = 32):
+        self.failed_downloads = []
+        self.max_threads = max_threads
+        self.uuid = str(uuid.uuid1())
+    
+    # args = (post_id, link, caption_data)
+    def download(self, args_thread):
+        try:
+            image = Image.open(requests.get(args_thread[1], stream=True).raw).convert('RGB')
+            if args.resize:
+                image = resize_image(image, max_size=(args.resize, args.resize))
+            image_bytes = io.BytesIO()
+            image.save(image_bytes, format='PNG')
+            __key__ = '%07d' % int(args_thread[0])
+            image = image_bytes.getvalue()
+            caption = str(json.dumps(args_thread[2]))
+
+            with open(f'{self.uuid}/{__key__}.image', 'wb') as f:
+                f.write(image)
+            with open(f'{self.uuid}/{__key__}.caption', 'w') as f:
+                f.write(caption)
+
+        except Exception as e:
+            import traceback
+            print(e, traceback.print_exc())
+            self.failed_downloads.append((args_thread[0], args_thread[1], args_thread[2]))
+    
+    def download_urls(self, file_path):
+        with open(file_path) as f:
+            data = json.load(f)
+        thread_args = []
+
+        delimiter = '\\' if os.name == 'nt' else '/'
+
+        self.uuid = (file_path.split(delimiter)[-1]).split('.')[0]
+        
+        if not os.path.exists(f'./{self.uuid}'):
+            os.mkdir(f'{self.uuid}')
+
+        print(f'Loading {file_path} for downloading on {self.max_threads} threads... Writing to dataset {self.uuid}')
+
+        # create initial thread_args
+        for k, v in tqdm.tqdm(data.items()):
+            thread_args.append((k, v['file_url'], v))
+        
+        # divide thread_args into chunks divisible by max_threads
+        chunks = []
+        for i in range(0, len(thread_args), self.max_threads):
+            chunks.append(thread_args[i:i+self.max_threads])
+        
+        print(f'Downloading {len(thread_args)} images...')
+
+        # download chunks synchronously
+        for chunk in tqdm.tqdm(chunks):
+            with futures.ThreadPoolExecutor(args.threads) as p:
+                p.map(self.download, chunk)
+
+        if len(self.failed_downloads) > 0:
+            print("Failed downloads:")
+            for i in self.failed_downloads:
+                print(i[0])
+            print("\n")
+        
+        # put things into tar
+        print(f'Writing webdataset to {self.uuid}')
+        archive = tarfile.open(f'{self.uuid}.tar', 'w')
+        files = glob.glob(f'{self.uuid}/*')
+        for f in tqdm.tqdm(files):
+            archive.add(f, f.split(delimiter)[-1])
+        
+        archive.close()
+        
+        print('Cleaning up...')
+        shutil.rmtree(self.uuid)
+        
+if __name__ == '__main__':
+    dm = DownloadManager(max_threads=args.threads)
+    dm.download_urls(args.file)
--- a/dataset/download/local/convert.py
+++ b/dataset/download/local/convert.py
--- a/dataset/download/local/extractfromjson_danboo21.py
+++ b/dataset/download/local/extractfromjson_danboo21.py
--- a/dataset/download/local/nsfw_processer_danboo21.py
+++ b/dataset/download/local/nsfw_processer_danboo21.py
--- a/dataset/download/scrape.py
+++ b/dataset/download/scrape.py
--- a/docs/en/README.md
+++ b/docs/en/README.md
@ -1,7 +0,0 @@
-# Documentation
-
-Waifu Diffusion is a project based off CompVis/Stable-Diffusion.
-
-For guidance on how to start training, see [training](./training/README.md).
-
-For a list of trained weights, see [weights](./weights/README.md).
--- a/docs/en/training/README.md
+++ b/docs/en/training/README.md
@ -1,8 +0,0 @@
-# Training documentation
-Training is available with waifu-diffusion. Before starting, we remind you that, at this moment at least 30GB of VRAM is needed, along with at least 30gb of storage if you don't mind cleaning up every so often.
-## Contents
-1. [Dataset](./dataset.md)
-2. [Configuration](./configuration.md)
-3. [Executing](./executing.md)
-4. Recommendations
-5. FAQ
--- a/docs/en/training/configuration.md
+++ b/docs/en/training/configuration.md
@ -1,3 +0,0 @@
-# 2. Configuration
-This section is to be done on the machine where you are going to train. 
-Soon because my instance is on maintenance
--- a/docs/en/training/dataset.md
+++ b/docs/en/training/dataset.md
@ -1,120 +0,0 @@
-# 1. Dataset
-
-In this guide we are going to use the Danbooru2021 dataset by Gwern.net. You are free to use any other dataset as long as you know how to convert it to the right format.
-
-## Contents
-1. Dataset requirements
-2. Downloading the dataset
-3. Organizing the dataset
-4. Packaging the dataset
-
-## Dataset requirements
-
-The dataset needs to be in the following format
-
-/dataset/ : Root dataset folder, can be any name
-
-/dataset/img/ : Folder for images
-
-/dataset/txt/ : Folder for text files
-
-It is recommended to have the images in 512x512 resolution and in JPG format. While the text files need to have the same name as the images it refers to.
-
-Foe example:
-````
-mydataset
-├── img
-│   └── image001.jpg
-└── txt
-    └── image001.txt
-````
-Where image001.txt has the tags (prompt) to be used for image001.jpg
-
-## Downloading the dataset
-This is optional; If you have your own dataset skip this part.
-
-### Downloading Rsync
-Danbooru2021 is available for download through rsync.
-#### Linux
-On Linux, you should be able to install rsync via your package manager.
-````bash
-apt install rsync
-````
-#### Windows
-On Windows, you are going to need to install Cygwin, a posix runtime for Windows which allows the usage of many linux-only programs inside windows.
-
-[Cygwin Installer for x86](https://www.cygwin.com/setup-x86_64.exe)
-
-On the installer, select mirrors.kernel.org for Download Site:
-
-![cygwin-mirrors.png](./res/cygwin-mirrors.png)
-
-Next, search for "rsync" on the search bar, change "View: Pending" to "View: Full", and select on the "New" tab the latest version. Do the same for "zip".
-
-![cygwin-packages.png](./res/cygwin-packages.png)
-
-GIF explaining the entire process:
-
-![cygwin-gif.gif](./res/cygwin-gif.gif)
-
-Once the installation is finished, you should see "Cygwin64 Terminal" on your Start Menu. Launch it and you should be greated by the following window:
-
-![cygwin-idle.png](./res/cygwin-idle.png)
-
-You may now follow the intructions
-
-### Downloading the dataset
-Remember that instructions here apply universally, both on Linux and Windows (If you are using Cygwin that is).
-
-The entire dataset weights about 5TB. You are not going to download everything, instead, you are only going to download two kinds of files:
-
-1. The images
-2. The JSON files (metadata)
-
-If you want to see the entire file list, you can refer to the [Danbooru2021 information site](https://www.gwern.net/Danbooru2021).
-
-We are going to extract the images from the 512px folder for convinience, since this folder already has the images resized to 512x512 resolution in JPG format. It only has safe rated images, for NSFW refer to [gwern.net](https://www.gwern.net/Danbooru2021#samples). 
-
-Folders from 0000 to 0009.
-> The folders are named according to the last 3 digits of the image ID on danbooru. Images on folder 0001 will have its ID end on 001.
-
-We are also going to download the only the first JSON batch. If you want to train on more data you should download more JSON batches.
-
-Download the 512px folders from 0000 to 0009 (3.86GB):
-```bash
-rsync -r rsync://176.9.41.242:873/danbooru2021/512px/000* ./512px/
-```
-Download the first batch of metadata, posts000000000000.json (800MB):
-``` shell
-rsync rsync://176.9.41.242:873/danbooru2021/metadata/posts000000000000.json ./metadata/
-```
-You should now have two folders named: 512px and metadata.
-
-## Organizing the dataset
-Although we have the dataset, the metadata that explains what the image is, is inside the JSON file. In order to extract the data into individual txt files, we are going to use the script inside ``danbooru_data/local/extractfromjson_danboo21.py``
-
-Assuming you are in the same directory as metadata and 512px folder:
-````bash 
-python danbooru_data/local/extractfromjson_danboo21.py -J metadata/posts000000000000.json -E danbooru-aesthetic
-````
-
-Once the script has finished, you should have a "danbooru-aesthetic" folder, whose insides look like this:
-
-![labeled_data-insides.png](./res/labeled_data-insides.png)
-
-## Packaging the dataset
-Next we need to put the extracted data into the format required in the section "Dataset requirements". Run the following commands:
-``` shell
-mkdir danbooru-aesthetic/img danbooru-aesthetic/txt
-mv danbooru-aesthetic/*.jpg danbooru-aesthetic/img
-mv danbooru-aesthetic/*.txt danbooru-aesthetic/txt
-```
-
-In order to reduce size, zip the contents of labeled_data:
-``` shell
-zip -r danbooru-aesthetic.zip danbooru-aesthetic
-```
-This will package the entire danbooru-aesthetic folder into a zip file. This command DOES NOT output any information in the terminal, so be patient.
-
-## Finish
-You can now continue to Configure
--- a/docs/en/training/executing.md
+++ b/docs/en/training/executing.md
@ -1,51 +0,0 @@
-# 3. Executing
-
-There are two modes of executing the training:
-1. Using docker image. This is the fastest way to get started.
-2. Using system python install. Allows more customization.
-
-Note: You will need to provide the initial checkpoint for resuming the training. This must be a version with the full EMA. Otherwise you will get this error:
-```
-RuntimeError: Error(s) in loading state_dict for LatentDiffusion:
-  Missing key(s) in state_dict: "model_ema.diffusion_modeltime_embed0weight", "model_ema.diffusion_modeltime_embed0bias".... (Many lines of similar outputs)
-```
-
-## 1. Using docker image
-
-An image is provided at `ghcr.io/derfred/waifu-diffusion`. Execute it using by adjusting the NUM_GPU variable:
-```
-docker run -it -e NUM_GPU=x ghcr.io/derfred/waifu-diffusion
-```
-
-Next you will want to download the starting checkpoint into the file `model.ckpt` and copy the training data in the directory `/waifu/danbooru-aesthetic`.
-
-Finally execute the training using:
-```
-sh train.sh -t -n "aesthetic" --resume_from_checkpoint model.ckpt --base ./configs/stable-diffusion/v1-finetune-4gpu.yaml --no-test --seed 25 --scale_lr False --data_root "./danbooru-aesthetic"
-```
-
-## 2. system python install
-
-First install the dependencies:
-```bash
-pip install -r requirements.txt
-```
-
-Next you will want to download the starting checkpoint into the file `model.ckpt` and copy the training data in the directory `/waifu/danbooru-aesthetic`.
-
-Also you will need to edit the configuration in `./configs/stable-diffusion/v1-finetune-4gpu.yaml`. In the `data` section (around line 70) change the `batch_size` and `num_workers` to the number of GPUs you are using:
-```
-data:
-  target: main.DataModuleFromConfig
-  params:
-    batch_size: 4
-    num_workers: 4
-    wrap: false
-```
-
-Finally execute the training using the following command. You need to adjust the `--gpu` parameter according to your GPU settings.
-```bash
-sh train.sh -t -n "aesthetic" --resume_from_checkpoint model.ckpt --base ./configs/stable-diffusion/v1-finetune-4gpu.yaml --no-test --seed 25 --scale_lr False --data_root "./danbooru-aesthetic" --gpu=0,1,2,3,
-```
-
-In case you get an error stating `KeyError: 'Trying to restore optimizer state but checkpoint contains only the model. This is probably due to ModelCheckpoint.save_weights_only being set to True.'` follow these instructions: https://discord.com/channels/930499730843250783/953132470528798811/1018668937052962908
--- a/docs/en/training/res/cygwin-gif.gif
+++ b/docs/en/training/res/cygwin-gif.gif
--- a/docs/en/training/res/cygwin-idle.png
+++ b/docs/en/training/res/cygwin-idle.png
--- a/docs/en/training/res/cygwin-mirrors.png
+++ b/docs/en/training/res/cygwin-mirrors.png
--- a/docs/en/training/res/cygwin-packages.png
+++ b/docs/en/training/res/cygwin-packages.png
--- a/docs/en/training/res/labeled_data-insides.png
+++ b/docs/en/training/res/labeled_data-insides.png
--- a/docs/en/weights/README.md
+++ b/docs/en/weights/README.md
@ -1,40 +0,0 @@
-# Waifu Diffusion v1.3
-
-Waifu Diffusion is a latent text-to-image diffusion model that has been conditioned on high-quality anime images through fine-tuning.
-
- [Float 16 EMA Pruned](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-float16.ckpt)
- [Float 32 EMA Pruned](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-float32.ckpt)
- [Float 32 Full Weights](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-full.ckpt)
- [Float 32 Full Weights + Optimizer Weights (For Training)](https://huggingface.co/hakurei/waifu-diffusion-v1-3/blob/main/wd-v1-3-full-opt.ckpt)
-
-## Model Description
-
-The model originally used for fine-tuning is [Stable Diffusion 1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4), which is a latent image diffusion model trained on [LAION2B-en](https://huggingface.co/datasets/laion/laion2B-en). The current model has been fine-tuned with a learning rate of 5.0e-6 for 10 epochs on 680k anime-styled images.
-
-[See here for an in-depth overview of Waifu Diffusion 1.3.](https://gist.github.com/harubaru/f727cedacae336d1f7877c4bbe2196e1)
-
-## License
-
-This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
-The CreativeML OpenRAIL License specifies: 
-
-1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 
-2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
-3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
-[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
-
-## Downstream Uses
-
-This model can be used for entertainment purposes and as a generative art assistant.
-
-## Team Members and Acknowledgements
-
-This project would not have been possible without the incredible work by the [CompVis Researchers](https://ommer-lab.com/).
-
- [Anthony Mercurio](https://github.com/harubaru)
- [Salt](https://github.com/sALTaccount/)
- [Cafe](https://twitter.com/cafeai_labs)
-
-In order to reach us, you can join our [Discord server](https://discord.gg/touhouai).
-
-[![Discord Server](https://discordapp.com/api/guilds/930499730843250783/widget.png?style=banner2)](https://discord.gg/touhouai)
--- a/docs/en/weights/danbooru-7-09-2022/README.md
+++ b/docs/en/weights/danbooru-7-09-2022/README.md
@ -1,19 +0,0 @@
-Waifu Diffusion v1.2
-
-Release Date: 07/09/2022
-
-Steps/Epochs/Images: 5 Epochs, 56,000 Images
-
-License: None
-
-Authors: Haru (haru#1367@discord)
-
-Mirrors:
-
-Google Drive (rate limit): https://drive.google.com/file/d/1XeoFCILTcc9kn_5uS-G0uqWS5XVANpha
-
-Magnet Link: magnet:?xt=urn:btih:INEYUMLLBBMZF22IIP4AEXLUK6XQKCSD&dn=wd-v1-2-full-ema.ckpt&xl=7703810927&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce
-
-HTTPS mirror: https://thisanimedoesnotexist.ai/downloads/wd-v1-2-full-ema.ckpt (Fastest)
-
-HTTP mirror: http://wd.links.sd:8880/wd-v1-2-full-ema.ckpt
--- a/environment.yaml
+++ b/environment.yaml
@ -1,32 +0,0 @@
-name: ldm
-channels:
-  - pytorch
-  - defaults
-dependencies:
-  - git
-  - python=3.8.5
-  - pip=20.3
-  - cudatoolkit=11.3
-  - pytorch=1.11.0
-  - torchvision=0.12.0
-  - numpy=1.19.2
-  - pip:
-    - albumentations==0.4.3
-    - opencv-python==4.1.2.30
-    - pudb==2019.2
-    - imageio==2.9.0
-    - imageio-ffmpeg==0.4.2
-    - pytorch-lightning==1.4.2
-    - omegaconf==2.1.1
-    - test-tube>=0.7.5
-    - streamlit>=0.73.1
-    - einops==0.3.0
-    - torch-fidelity==0.3.0
-    - transformers==4.19.2
-    - torchmetrics==0.6.0
-    - kornia==0.6
-    - gradio==3.1.6
-    - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
-    - -e git+https://github.com/openai/CLIP.git@main#egg=clip
-    - -e git+https://github.com/hlky/k-diffusion-sd#egg=k_diffusion
-    - -e .
--- a/ldm/data/init.py
+++ b/ldm/data/init.py
--- a/Show More
+++ b/Show More