Update README.md

README.md

@@ -1,3 +1,53 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+---
+**Github repo**: https://github.com/magic-research/piecewise-rectified-flow <br>
+**PeRFlow accelerated SDXL-base**: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
+
+**Demo:**
+```python
+from pathlib import Path
+import torch, torchvision
+
+from diffusers import StableDiffusionXLPipeline
+pipe = StableDiffusionXLPipeline.from_pretrained("hansyan/perflow-sdxl-base", torch_dtype=torch.float16, use_safetensors=True, variant="v0-fix")
+from src.scheduler_perflow import PeRFlowScheduler
+pipe.scheduler = PeRFlowScheduler.from_config(pipe.scheduler.config, prediction_type="ddim_eps", num_time_windows=4)
+pipe.to("cuda", torch.float16)
+
+
+prompts_list = [
+    ["photorealistic, uhd, high resolution, high quality, highly detailed; RAW photo, a handsome man, wearing a black coat, outside, closeup face",
+        "distorted, blur, low-quality, haze, out of focus",],
+    ["photorealistic, uhd, high resolution, high quality, highly detailed; masterpiece, A closeup face photo of girl, wearing a rain coat, in the street, heavy rain, bokeh,",
+        "distorted, blur, low-quality, haze, out of focus",],
+    ["photorealistic, uhd, high resolution, high quality, highly detailed; RAW photo, a red luxury car, studio light",
+        "distorted, blur, low-quality, haze, out of focus",],
+    ["photorealistic, uhd, high resolution, high quality, highly detailed; masterpiece, A beautiful cat bask in the sun",
+        "distorted, blur, low-quality, haze, out of focus",],
+]
+
+num_inference_steps = 6  # suggest steps >= num_win=4
+cfg_scale_list = [2.0]  # suggest values [1.5, 2.0, 2.5]
+num_img = 2
+seed = 42
+
+for cfg_scale in cfg_scale_list:
+    for i, prompts in enumerate(prompts_list):
+        setup_seed(seed)
+        prompt, neg_prompt = prompts[0], prompts[1]
+        samples = pipe(
+            prompt              = [prompt] * num_img, 
+            negative_prompt     = [neg_prompt] * num_img,
+            height              = 1024,
+            width               = 1024,
+            num_inference_steps = num_inference_steps, 
+            guidance_scale      = cfg_scale,
+            output_type         = 'pt',
+        ).images
+        
+        cfg_int = int(cfg_scale); cfg_float = int(cfg_scale*10 - cfg_int*10)
+        save_name = f'step_{num_inference_steps}_txt{i+1}_cfg{cfg_int}-{cfg_float}.png'
+        torchvision.utils.save_image(torchvision.utils.make_grid(samples, nrow = num_img), os.path.join("demo", save_name))
+
+```