Create app.py

app.py

@@ -0,0 +1,77 @@
+import argparse
+import os
+import time
+from os import path
+from safetensors.torch import load_file
+
+cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
+os.environ["TRANSFORMERS_CACHE"] = cache_path
+os.environ["HF_HUB_CACHE"] = cache_path
+os.environ["HF_HOME"] = cache_path
+
+import gradio as gr
+import torch
+from diffusers import StableDiffusionXLPipeline, LCMScheduler
+
+# from scheduling_tcd import TCDScheduler
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+class timer:
+    def __init__(self, method_name="timed process"):
+        self.method = method_name
+
+    def __enter__(self):
+        self.start = time.time()
+        print(f"{self.method} starts")
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        end = time.time()
+        print(f"{self.method} took {str(round(end - self.start, 2))}s")
+
+if not path.exists(cache_path):
+    os.makedirs(cache_path, exist_ok=True)
+
+pipe = StableDiffusionXLPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.bfloat16, safety_checker=None)
+pipe.to(device="cuda", dtype=torch.bfloat16)
+unet_state = load_file(hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-Unet.safetensors"), device="cuda")
+pipe.unet.load_state_dict(unet_state)
+pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, timestep_spacing ="trailing")
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        with gr.Row():
+            with gr.Column():
+                num_images = gr.Slider(label="Number of Images", minimum=1, maximum=8, step=1, value=4, interactive=True)
+                height = gr.Number(label="Image Height", value=1024, interactive=True)
+                width = gr.Number(label="Image Width", value=1024, interactive=True)
+                # steps = gr.Slider(label="Inference Steps", minimum=1, maximum=8, step=1, value=1, interactive=True)
+                # eta = gr.Number(label="Eta (Corresponds to parameter eta (η) in the DDIM paper, i.e. 0.0 eqauls DDIM, 1.0 equals LCM)", value=1., interactive=True)
+                prompt = gr.Text(label="Prompt", value="a photo of a cat", interactive=True)
+                seed = gr.Number(label="Seed", value=3413, interactive=True)
+                btn = gr.Button(value="run")
+            with gr.Column():
+                output = gr.Gallery(width=1024, height=768)
+
+            def process_image(num_images, height, width, prompt, seed):
+                global pipe
+                with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
+                    return pipe(
+                        prompt=[prompt]*num_images,
+                        generator=torch.Generator().manual_seed(int(seed)),
+                        num_inference_steps=1,
+                        guidance_scale=0.,
+                        height=int(height),
+                        width=int(width),
+                        timesteps=[800]
+                    ).images
+
+            reactive_controls = [num_images, height, width, prompt, seed]
+
+            # for control in reactive_controls:
+            #     control.change(fn=process_image, inputs=reactive_controls, outputs=[output])
+
+            btn.click(process_image, inputs=reactive_controls, outputs=[output])
+
+if __name__ == "__main__":
+    demo.launch()