Package updates

Author: cmdr2

README.md

@@ -1,9 +1,9 @@
 Wrapper for the official [Stable Diffusion](https://github.com/Stability-AI/stablediffusion) repository, to allow installing via `pip`. Please see the installation section below for more details.
 
 # Stable Diffusion Version 2
-![t2i](assets/stable-samples/txt2img/768/merged-0006.png)
-![t2i](assets/stable-samples/txt2img/768/merged-0002.png)
-![t2i](assets/stable-samples/txt2img/768/merged-0005.png)
+![t2i](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/txt2img/768/merged-0006.png)
+![t2i](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/txt2img/768/merged-0002.png)
+![t2i](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/txt2img/768/merged-0005.png)
 
 This repository contains [Stable Diffusion](https://github.com/CompVis/stable-diffusion) models trained from scratch and will be continuously updated with
 new checkpoints. The following list provides an overview of all currently available models. More coming soon.
@@ -14,6 +14,8 @@ new checkpoints. The following list provides an overview of all currently availa
 
 Step 1 is not necessary on Mac.
 
+This will install the `ldm` package, which contains the stable diffusion code.
+
 ## News
 
 **December 7, 2022**
@@ -32,7 +34,7 @@ Per default, the attention operation of the model is evaluated at full precision
 - Added a [x4 upscaling latent text-guided diffusion model](#image-upscaling-with-stable-diffusion).
 - New [depth-guided stable diffusion model](#depth-conditional-stable-diffusion), finetuned from _SD 2.0-base_. The model is conditioned on monocular depth estimates inferred via [MiDaS](https://github.com/isl-org/MiDaS) and can be used for structure-preserving img2img and shape-conditional synthesis.
 
-  ![d2i](assets/stable-samples/depth2img/depth2img01.png)
+  ![d2i](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/depth2img/depth2img01.png)
 - A [text-guided inpainting model](#image-inpainting-with-stable-diffusion), finetuned from SD _2.0-base_.
 
 We follow the [original repository](https://github.com/CompVis/stable-diffusion) and provide basic inference scripts to sample from the models.
@@ -109,13 +111,13 @@ and OpenCLIP ViT-H/14 text encoder for the diffusion model. The _SD 2-v_ model p
 Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
 5.0, 6.0, 7.0, 8.0) and 50 DDIM sampling steps show the relative improvements of the checkpoints:
 
-![sd evaluation results](assets/model-variants.jpg)
+![sd evaluation results](https://github.com/Stability-AI/stablediffusion/raw/main/assets/model-variants.jpg)
 
 
 
 ### Text-to-Image
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0003.png)
-![txt2img-stable2](assets/stable-samples/txt2img/merged-0001.png)
+![txt2img-stable2](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/txt2img/merged-0003.png)
+![txt2img-stable2](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/txt2img/merged-0001.png)
 
 Stable Diffusion 2 is a latent diffusion model conditioned on the penultimate text embeddings of a CLIP ViT-H/14 text encoder.
 We provide a [reference script for sampling](#reference-sampling-script).
@@ -147,7 +149,7 @@ non-EMA to EMA weights.
 
 ### Image Modification with Stable Diffusion
 
-![depth2img-stable2](assets/stable-samples/depth2img/merged-0000.png)
+![depth2img-stable2](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/depth2img/merged-0000.png)
 #### Depth-Conditional Stable Diffusion
 
 To augment the well-established [img2img](https://github.com/CompVis/stable-diffusion#image-modification-with-stable-diffusion) functionality of Stable Diffusion, we provide a _shape-preserving_ stable diffusion model.
@@ -166,19 +168,19 @@ streamlit run scripts/streamlit/depth2img.py configs/stable-diffusion/v2-midas-i
 ```
 
 This method can be used on the samples of the base model itself.
-For example, take [this sample](assets/stable-samples/depth2img/old_man.png) generated by an anonymous discord user.
+For example, take [this sample](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/depth2img/old_man.png) generated by an anonymous discord user.
 Using the [gradio](https://gradio.app) or [streamlit](https://streamlit.io/) script `depth2img.py`, the MiDaS model first infers a monocular depth estimate given this input, 
 and the diffusion model is then conditioned on the (relative) depth output.
 
 <p align="center">
 <b> depth2image </b><br/>
-<img src=assets/stable-samples/depth2img/d2i.gif>
+<img src=https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/depth2img/d2i.gif>
 </p>
 
-This model is particularly useful for a photorealistic style; see the [examples](assets/stable-samples/depth2img).
+This model is particularly useful for a photorealistic style; see the [examples](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/depth2img).
 For a maximum strength of 1.0, the model removes all pixel-based information and only relies on the text prompt and the inferred monocular depth estimate.
 
-![depth2img-stable3](assets/stable-samples/depth2img/merged-0005.png)
+![depth2img-stable3](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/depth2img/merged-0005.png)
 
 #### Classic Img2Img
 
@@ -189,7 +191,7 @@ python scripts/img2img.py --prompt "A fantasy landscape, trending on artstation"
 and adapt the checkpoint and config paths accordingly.
 
 ### Image Upscaling with Stable Diffusion
-![upscaling-x4](assets/stable-samples/upscaling/merged-dog.png)
+![upscaling-x4](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/upscaling/merged-dog.png)
 After [downloading the weights](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler), run
 ```
 python scripts/gradio/superresolution.py configs/stable-diffusion/x4-upscaling.yaml <path-to-checkpoint>
@@ -207,7 +209,7 @@ This model can be used both on real inputs and on synthesized examples. For the
 
 ### Image Inpainting with Stable Diffusion
 
-![inpainting-stable2](assets/stable-inpainting/merged-leopards.png)
+![inpainting-stable2](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-inpainting/merged-leopards.png)
 
 [Download the SD 2.0-inpainting checkpoint](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) and run
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "stable-diffusion-sdkit"
-version = "2.1.1"
+version = "2.1.2"
 description = "High-Resolution Image Synthesis with Latent Diffusion Models. This is a wrapper around the original repo, to allow installing via pip."
 readme = "README.md"
 requires-python = ">=3.8"

setup.py

@@ -22,9 +22,6 @@
 # They need to run: pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116
 
 setup(
-    name='stable-diffusion-sdkit',
-    version='2.1.0',
-    description='',
     packages=find_packages(),
     install_requires=dependencies,
 )