Update README.md

README.md

@@ -9,4 +9,129 @@ tags:
 - text-to-image
 - stable-diffusion-xl
 - stable-diffusion-xl-diffusers
----
+---
+# Stable-fast-xl Model Card
+
+Stable-fast is an ultra lightweight inference optimization framework for HuggingFace Diffusers on NVIDIA GPUs. stable-fast provides super fast inference optimization by utilizing some key techniques.
+this repository contains a compact installation of the stable-fast compiler https://github.com/chengzeyi/stable-fast and its inference with the stable-diffusion-xl-base-1.0
+Inference with [stable-diffusion-xl-base-1.0)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and [stable-diffusion-xl-1.0-inpainting-0.1](https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1)
+
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/670503434c094132b2282e63/Xib4SHo9PX7-oSWP3Or3Y.png)
+
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/670503434c094132b2282e63/-a7V70NkS09TeMSZAKgVB.png)
+
+# iinference sdxl model 30%+ faster!!!
+
+## Differences With Other Acceleration Libraries
+#### Fast:
+stable-fast is specialy optimized for HuggingFace Diffusers. It achieves a high performance across many libraries. And it provides a very fast compilation speed within only a few seconds. It is significantly faster than **torch.compile**, **TensorRT** and **AITemplate** in compilation time.
+#### Minimal:
+stable-fast works as a plugin framework for **PyTorch**. It utilizes existing PyTorch functionality and infrastructures and is compatible with other acceleration techniques, as well as popular fine-tuning techniques and deployment solutions.
+
+
+# How to use
+
+### Install dependencies
+```bash
+pip install diffusers, transformers, safetensors, accelerate, sentencepiece
+```
+
+### Download repository and run script for stable-fast installation
+```bash
+git clone https://huggingface.co/artemtumch/stable-fast-xl
+cd stable-fast-xl
+sh install_stable-fast.sh
+```
+
+## Generate image
+```py
+from diffusers import DiffusionPipeline
+import torch
+
+from sfast.compilers.stable_diffusion_pipeline_compiler import (
+compile, CompilationConfig
+)
+
+import xformers
+import triton
+
+pipe = DiffusionPipeline.from_pretrained(
+"stabilityai/stable-diffusion-xl-base-1.0",
+torch_dtype=torch.float16,
+use_safetensors=True,
+variant="fp16"
+)
+
+# enable to reduce GPU VRAM usage (~30%)
+# pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
+
+pipe.to("cuda")
+
+# if using torch < 2.0
+# pipe.enable_xformers_memory_efficient_attention()
+
+config = CompilationConfig.Default()
+
+config.enable_xformers = True
+config.enable_triton = True
+config.enable_cuda_graph = True
+
+pipe = compile(pipe, config)
+
+prompt = "An astronaut riding a green horse"
+
+images = pipe(prompt=prompt).images[0]
+```
+
+## Inpainting
+```py
+from diffusers import StableDiffusionXLInpaintPipeline
+from diffusers.utils import load_image
+import torch
+
+from sfast.compilers.stable_diffusion_pipeline_compiler import (
+compile, CompilationConfig
+)
+
+import xformers
+import triton
+
+pipe = StableDiffusionXLInpaintPipeline.from_pretrained(
+"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
+torch_dtype=torch.float16,
+variant="fp16"
+)
+
+# enable to reduce GPU VRAM usage (~30%)
+# pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16)
+
+pipe.to("cuda")
+
+config = CompilationConfig.Default()
+
+config.enable_xformers = True
+config.enable_triton = True
+config.enable_cuda_graph = True
+
+pipe = compile(pipe, config)
+
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+
+image = load_image(img_url).resize((1024, 1024))
+mask_image = load_image(mask_url).resize((1024, 1024))
+
+prompt = "a tiger sitting on a park bench"
+generator = torch.Generator(device="cuda").manual_seed(0)
+
+image = pipe(
+prompt=prompt,
+image=image,
+mask_image=mask_image,
+guidance_scale=8.0,
+num_inference_steps=20, # steps between 15 and 30 work well
+strength=0.99, # make sure to use `strength` below 1.0
+generator=generator,
+).images[0]
+
+```