Update README.md

README.md

@@ -3,8 +3,10 @@ license: openrail++
 tags:
 - text-to-image
 - stable-diffusion
+- Neuron
+- Inferentia
 ---
-# SD-XL 1.0-base Model Card
+# SD-XL 1.0-base Model Card - Neuron
 ![row01](01.png)
 
 ## Model
@@ -46,142 +48,21 @@ The chart above evaluates user preference for SDXL (with and without refinement)
 The SDXL base model performs significantly better than the previous variants, and the model combined with the refinement module achieves the best overall performance.
 
 
-### 🧨 Diffusers 
-
-Make sure to upgrade diffusers to >= 0.19.0:
-```
-pip install diffusers --upgrade
-```
-
-In addition make sure to install `transformers`, `safetensors`, `accelerate` as well as the invisible watermark:
-```
-pip install invisible_watermark transformers accelerate safetensors
-```
-
-To just use the base model, you can run:
-
-```py
-from diffusers import DiffusionPipeline
-import torch
-
-pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
-pipe.to("cuda")
-
-# if using torch < 2.0
-# pipe.enable_xformers_memory_efficient_attention()
-
-prompt = "An astronaut riding a green horse"
-
-images = pipe(prompt=prompt).images[0]
-```
-
-To use the whole base + refiner pipeline as an ensemble of experts you can run:
+### Usage
 
 ```py
-from diffusers import DiffusionPipeline
-import torch
-
-# load both base & refiner
-base = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True
-)
-base.to("cuda")
-refiner = DiffusionPipeline.from_pretrained(
-    "stabilityai/stable-diffusion-xl-refiner-1.0",
-    text_encoder_2=base.text_encoder_2,
-    vae=base.vae,
-    torch_dtype=torch.float16,
-    use_safetensors=True,
-    variant="fp16",
-)
-refiner.to("cuda")
-
-# Define how many steps and what % of steps to be run on each experts (80/20) here
-n_steps = 40
-high_noise_frac = 0.8
-
-prompt = "A majestic lion jumping from a big stone at night"
-
-# run both experts
-image = base(
-    prompt=prompt,
-    num_inference_steps=n_steps,
-    denoising_end=high_noise_frac,
-    output_type="latent",
-).images
-image = refiner(
-    prompt=prompt,
-    num_inference_steps=n_steps,
-    denoising_start=high_noise_frac,
-    image=image,
-).images[0]
-```
+from diffusers import DPMSolverMultistepScheduler
+from optimum.neuron import NeuronStableDiffusionXLPipeline
 
-When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
-```py
-pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-```
+pipeline = NeuronStableDiffusionXLPipeline.from_pretrained("Shekswess/stable-diffusion-xl-base-1.0-neuron", device_ids=[0, 1])
+pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
 
-If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
-instead of `.to("cuda")`:
-
-```diff
-- pipe.to("cuda")
-+ pipe.enable_model_cpu_offload()
+prompt = "A swirling beautiful exploding scene of magical wonders and surreal ideas and objects with portraits of beautiful woman with silk back to camera, flowers, light, cosmic wonder, nebula, high-resolution"
+image = pipeline(prompt=prompt).images[0].save("output.png)
 ```
 
 For more information on how to use Stable Diffusion XL with `diffusers`, please have a look at [the Stable Diffusion XL Docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl).
 
-### Optimum
-[Optimum](https://github.com/huggingface/optimum) provides a Stable Diffusion pipeline compatible with both [OpenVINO](https://docs.openvino.ai/latest/index.html) and [ONNX Runtime](https://onnxruntime.ai/).
-
-#### OpenVINO
-
-To install Optimum with the dependencies required for OpenVINO :
-
-```bash
-pip install optimum[openvino]
-```
-
-To load an OpenVINO model and run inference with OpenVINO Runtime, you need to replace `StableDiffusionXLPipeline` with Optimum `OVStableDiffusionXLPipeline`. In case you want to load a PyTorch model and convert it to the OpenVINO format on-the-fly, you can set `export=True`.
-
-```diff
-- from diffusers import StableDiffusionXLPipeline
-+ from optimum.intel import OVStableDiffusionXLPipeline
-
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-- pipeline = StableDiffusionXLPipeline.from_pretrained(model_id)
-+ pipeline = OVStableDiffusionXLPipeline.from_pretrained(model_id)
-prompt = "A majestic lion jumping from a big stone at night"
-image = pipeline(prompt).images[0]
-```
-
-You can find more examples (such as static reshaping and model compilation) in optimum [documentation](https://huggingface.co/docs/optimum/main/en/intel/inference#stable-diffusion-xl).
-
-
-#### ONNX
-
-To install Optimum with the dependencies required for ONNX Runtime inference :
-
-```bash
-pip install optimum[onnxruntime]
-```
-
-To load an ONNX model and run inference with ONNX Runtime, you need to replace `StableDiffusionXLPipeline` with Optimum `ORTStableDiffusionXLPipeline`. In case you want to load a PyTorch model and convert it to the ONNX format on-the-fly, you can set `export=True`.
-
-```diff
-- from diffusers import StableDiffusionXLPipeline
-+ from optimum.onnxruntime import ORTStableDiffusionXLPipeline
-
-model_id = "stabilityai/stable-diffusion-xl-base-1.0"
-- pipeline = StableDiffusionXLPipeline.from_pretrained(model_id)
-+ pipeline = ORTStableDiffusionXLPipeline.from_pretrained(model_id)
-prompt = "A majestic lion jumping from a big stone at night"
-image = pipeline(prompt).images[0]
-```
-
-You can find more examples in optimum [documentation](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/models#stable-diffusion-xl).
-
 
 ## Uses
 
@@ -212,4 +93,7 @@ The model was not trained to be factual or true representations of people or eve
 - The autoencoding part of the model is lossy.
 
 ### Bias
-While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
+While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
+
+## Original Model
+[Model](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)