Update README.md

README.md

@@ -1,3 +1,36 @@
 ---
 license: cc-by-nc-4.0
 ---
+
+Github repo: https://github.com/magic-research/piecewise-rectified-flow <br>
+Project page: https://piecewise-rectified-flow.github.io/
+
+```python
+import torch, torchvision
+from diffusers import StableDiffusionPipeline, UNet2DConditionModel
+from src.utils_perflow import merge_delta_weights_into_unet
+from src.scheduler_perflow import PeRFlowScheduler
+delta_weights = UNet2DConditionModel.from_pretrained("hansyan/piecewise-rectified-flow-delta-weights", torch_dtype=torch.float16, variant="v0-1",).state_dict()
+pipe = StableDiffusionPipeline.from_pretrained("Lykon/dreamshaper-8", torch_dtype=torch.float16,)
+pipe = merge_delta_weights_into_unet(pipe, delta_weights)
+pipe.scheduler = PeRFlowScheduler.from_config(pipe.scheduler.config, prediction_type="epsilon", num_time_windows=4)
+pipe.to("cuda", torch.float16)
+
+prompts_list = ["A man with brown skin, a beard, and dark eyes", "A colorful bird standing on the tree, open beak",]
+for i, prompt in enumerate(prompts_list):
+    generator = torch.Generator("cuda").manual_seed(1024)
+    prompt = "RAW photo, 8k uhd, dslr, high quality, film grain, highly detailed, masterpiece; " + prompt
+    neg_prompt = "distorted, blur, smooth, low-quality, warm, haze, over-saturated, high-contrast, out of focus, dark"
+    samples = pipe(
+        prompt              = [prompt] * 8, 
+        negative_prompt     = [neg_prompt] * 8,
+        height              = 512,
+        width               = 512,
+        num_inference_steps = 8, 
+        guidance_scale      = 7.5,
+        generator           = generator,
+        output_type         = 'pt',
+    ).images
+    torchvision.utils.save_image(torchvision.utils.make_grid(samples, nrow=4), f"tmp_{i}.png")
+
+```