Upload app.py

app.py

@@ -0,0 +1,149 @@
+import gradio as gr
+import spaces
+import numpy as np
+import random
+import spaces
+import torch
+from diffusers import SanaSprintPipeline
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+pipe = SanaSprintPipeline.from_pretrained(
+    "Efficient-Large-Model/Sana_Sprint_0.6B_1024px_diffusers",
+    torch_dtype=torch.bfloat16
+)
+pipe2 = SanaSprintPipeline.from_pretrained(
+    "Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers",
+    torch_dtype=torch.bfloat16
+)
+pipe.to(device)
+pipe2.to(device)
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+@spaces.GPU(duration=5)
+def infer(prompt, model_size, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=4.5, num_inference_steps=2, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    
+    # Choose the appropriate model based on selected model size
+    selected_pipe = pipe if model_size == "0.6B" else pipe2
+    
+    img = selected_pipe(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+            output_type="pil"
+    )
+    print(img)
+    return img.images[0], seed
+    
+examples = [
+    ["a tiny astronaut hatching from an egg on the moon", "1.6B"],
+    ["🐶 Wearing 🕶 flying on the 🌈", "1.6B"],
+    ["an anime illustration of a wiener schnitzel", "0.6B"],
+    ["a photorealistic landscape of mountains at sunset", "0.6B"],
+]
+
+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"""# Sana Sprint""")
+        gr.Markdown("Demo for the real-time [Sana Sprint](https://huggingface.co/collections/Efficient-Large-Model/sana-sprint-67d6810d65235085b3b17c76) model")
+        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)
+        
+        model_size = gr.Radio(
+            label="Model Size",
+            choices=["0.6B", "1.6B"],
+            value="1.6B",
+            interactive=True
+        )
+        
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                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,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+            
+            with gr.Row():
+
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    step=0.1,
+                    value=4.5,
+                )
+  
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=2,
+                )
+        
+        gr.Examples(
+            examples = examples,
+            fn = infer,
+            inputs = [prompt, model_size],  # Add model_size to inputs
+            outputs = [result, seed],
+            cache_examples="lazy"
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, model_size, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],  # Add model_size to inputs
+        outputs = [result, seed]
+    )
+
+demo.launch()