Update README.md

Updated download links and changed github link to archived source.

README.md

@@ -39,14 +39,14 @@ For more information about how Stable Diffusion functions, please have a look at
 The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) 
 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
 
-You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
+You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [Archive of RunwayML GitHub repository](https://github.com/ashllay/stable-diffusion-archive).
 
 ### Diffusers
 ```py
 from diffusers import StableDiffusionPipeline
 import torch
 
-model_id = "runwayml/stable-diffusion-v1-5"
+model_id = "ashllay/stable-diffusion-v1-5"
 pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipe = pipe.to("cuda")
 
@@ -60,10 +60,10 @@ For more detailed instructions, use-cases and examples in JAX follow the instruc
 ### Original GitHub Repository
 
 1. Download the weights 
-   - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
-   - [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
+   - [v1-5-pruned-emaonly.ckpt](https://huggingface.co/ashllay/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
+   - [v1-5-pruned.ckpt](https://huggingface.co/ashllay/stable-diffusion-v1-5-archive/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
 
-2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
+2. Follow instructions [here](https://github.com/ashllay/stable-diffusion-archive).
 
 ## Model Details
 - **Developed by:** Robin Rombach, Patrick Esser
@@ -176,8 +176,8 @@ Currently six Stable Diffusion checkpoints are provided, which were trained as f
 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)).
 - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 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).
 - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
+- [`stable-diffusion-v1-5`](https://huggingface.co/ashllay/stable-diffusion-v1-5-archive) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
+- [`stable-diffusion-inpainting`](https://huggingface.co/ashllay/stable-diffusion-v1-5-inpainting-archive) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
 
 - **Hardware:** 32 x 8 x A100 GPUs
 - **Optimizer:** AdamW