Update app.py

app.py

@@ -1,72 +1,87 @@
+import spaces
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import functools
 import torch
+from diffusers import StableDiffusion3Pipeline
+from inference import run
+from peft import LoraConfig, get_peft_model, PeftModel
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium",
+                                                torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+distill_check = 'yresearch/swd-medium-6-steps'
+pipe.transformer = PeftModel.from_pretrained(
+    pipe.transformer,
+    distill_check,
+)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
+@spaces.GPU()
+def infer(prompt, seed, randomize_seed):
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
     generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
+    sigmas = [1.0000, 0.9454, 0.8959, 0.7904, 0.7371, 0.6022]
+    scales = [32, 48, 64, 80, 96, 128]
+
+    images = run(
+        pipe,
+        prompt,
+        sigmas=sigmas,
+        scales=scales,
+        num_inference_steps=6,
+
+        guidance_scale=0.0,
+        height=int(scales[0] * 8),
+        width=int(scales[0] * 8),
         generator=generator,
-    ).images[0]
+    ).images
 
-    return image, seed
+    return images
 
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
     "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    'Long-exposure night photography of a starry sky over a mountain range, with light trails.',
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "A portrait of a girl with blonde, tousled hair, blue eyes",
 ]
 
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 520px;
 }
 """
 
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(
+            f"""
+        # ⚡ Scale-wise Distillation ⚡ 
+        # ⚡ Image Generation with 6-step SwD ⚡
+        This is a demo of [Scale-wise Distillation](https://yandex-research.github.io/invertible-cd/), 
+        a diffusion distillation method proposed in [Scale-wise Distillation of Diffusion Models](https://arxiv.org/abs/2406.14539)
+        by [Yandex Research](https://github.com/yandex-research).
+        Currently running on {power_device}.
+        """
+        )
+        gr.Markdown(
+            "If you enjoy the space, feel free to give a ⭐ to the <a href='https://github.com/yandex-research/invertible-cd' target='_blank'>Github Repo</a>. [![GitHub Stars](https://img.shields.io/github/stars/yandex-research/invertible-cd?style=social)](https://github.com/yandex-research/invertible-cd)"
+        )
 
         with gr.Row():
             prompt = gr.Text(
@@ -77,18 +92,11 @@ with gr.Blocks(css=css) as demo:
                 container=False,
             )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+            run_button = gr.Button("Run", scale=0)
 
         result = gr.Image(label="Result", show_label=False)
 
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -97,58 +105,18 @@ with gr.Blocks(css=css) as demo:
                 value=0,
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+
+
+        gr.Examples(
+            examples=examples,
+            inputs=[prompt],
+            cache_examples=False
+        )
+    run_button.click(
         fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
+        inputs=[prompt, seed, randomize_seed],
+        outputs=[result]
     )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.queue().launch(share=False)