Update README.md

README.md

@@ -1,43 +1,8 @@
----
-license: openrail++
-tags:
-- stable-diffusion
-inference: false
----
-
-# Stable Diffusion x4 upscaler model card
-This model card focuses on the model associated with the Stable Diffusion Upscaler, available [here](https://github.com/Stability-AI/stablediffusion).
-This model is trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
-In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). 
-
-![Image](https://github.com/Stability-AI/stablediffusion/raw/main/assets/stable-samples/upscaling/merged-dog.png)
-
-- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `x4-upscaler-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.ckpt).
-- Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler#examples)
-
-
-## Model Details
-- **Developed by:** Robin Rombach, Patrick Esser
-- **Model type:** Diffusion-based text-to-image generation model
-- **Language(s):** English
-- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
-- **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 ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
-- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
-- **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}
-      }
 
 
 ## Examples
 
-Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
+Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) in a simple and efficient manner.
 
 ```bash
 pip install diffusers transformers accelerate scipy safetensors
@@ -47,12 +12,12 @@ pip install diffusers transformers accelerate scipy safetensors
 import requests
 from PIL import Image
 from io import BytesIO
-from diffusers import StableDiffusionUpscalePipeline
+from diffusers import StableDiffusionUpscaleLDM3DPipeline
 import torch
 
 # load model and scheduler
-model_id = "stabilityai/stable-diffusion-x4-upscaler"
-pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+model_id = "Intel/ldm3d-hr"
+pipeline = StableDiffusionUpscaleLDM3DPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipeline = pipeline.to("cuda")
 
 # let's download an  image