Create app.py

app.py

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+from diffusers import StableDiffusionXLPipeline, AutoencoderKL
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    torch_dtype=torch.float16, variant="fp16", use_safetensors=True,
+    vae=vae,
+    add_watermarker=False,
+).to("cuda")
+
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+def run(prompt="a photo of an astronaut riding a horse on mars", steps=10, seed=20, negative_prompt="", randomize_seed=False):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    sampling_schedule = [999, 845, 730, 587, 443, 310, 193, 116, 53, 13, 0]
+    torch.manual_seed(seed)
+    ays_images = pipe(
+        prompt,
+        negative_prompt=negative_prompt,
+        num_images_per_prompt=1,
+        timesteps=sampling_schedule,
+    ).images
+    return ays_images[0], seed
+
+examples = [
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Align-your-steps
+        Unnoficial demo for the official diffusers implementation of [Align your Steps](https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/) by NVIDIA
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            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,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+            num_inference_steps = gr.Slider(
+                label="Number of inference steps",
+                minimum=4,
+                maximum=12,
+                step=1,
+                value=8,
+            )
+
+    run_button.click(
+        fn = infer,
+        inputs = [prompt, num_inference_steps, seed, negative_prompt, randomize_seed],
+        outputs = [result]
+    )