Update app.py

app.py

@@ -0,0 +1,201 @@
+import gradio as gr
+import numpy as np
+import random
+
+import spaces
+from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model_repo_id = "tensorart/stable-diffusion-3.5-large-TurboX"
+
+if torch.cuda.is_available():
+    torch_dtype = torch.float16
+else:
+    torch_dtype = torch.float32
+
+pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+
+pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(model_repo_id, subfolder="scheduler", shift=5)
+
+pipe = pipe.to(device)
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+@spaces.GPU(duration=65)
+def infer(
+    prompt,
+    negative_prompt="",
+    seed=42,
+    randomize_seed=False,
+    width=1024,
+    height=1024,
+    guidance_scale=1.5,
+    num_inference_steps=8,
+    progress=gr.Progress(track_tqdm=True),
+):
+    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,
+        generator=generator,
+    ).images[0]
+
+    return image, seed
+
+
+examples = [
+        "A capybara wearing a suit holding a sign that reads Hello World",
+        "A serene mountain lake at sunset with cherry blossoms floating on the water",
+        "A magical crystal dragon with iridescent scales in a glowing forest",
+        "A Victorian steampunk teapot with intricate brass gears and rose gold accents",
+        "A futuristic neon cityscape with flying cars and holographic billboards",
+        "A red panda painter creating a masterpiece with tiny paws in an art studio",
+]
+
+css = """
+body {
+    background: linear-gradient(135deg, #f9e2e6 0%, #e8f3fc 50%, #e2f9f2 100%);
+    background-attachment: fixed;
+    min-height: 100vh;
+}
+
+#col-container {
+    margin: 0 auto;
+    max-width: 640px;
+    background-color: rgba(255, 255, 255, 0.85);
+    border-radius: 16px;
+    box-shadow: 0 8px 16px rgba(0, 0, 0, 0.1);
+    padding: 24px;
+    backdrop-filter: blur(10px);
+}
+
+.gradio-container {
+    background: transparent !important;
+}
+
+.gr-button-primary {
+    background: linear-gradient(90deg, #6b9dfc, #8c6bfc) !important;
+    border: none !important;
+    transition: all 0.3s ease;
+}
+
+.gr-button-primary:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 5px 15px rgba(108, 99, 255, 0.3);
+}
+
+.gr-form {
+    border-radius: 12px;
+    background-color: rgba(255, 255, 255, 0.7);
+}
+
+.gr-accordion {
+    border-radius: 12px;
+    overflow: hidden;
+}
+
+h1 {
+    background: linear-gradient(90deg, #6b9dfc, #8c6bfc);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    font-weight: 800;
+}
+"""
+
+with gr.Blocks(theme="apriel", css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(" # TensorArt Stable Diffusion 3.5 Large TurboX")
+        gr.Markdown("[8-step distilled turbo model](https://huggingface.co/tensorart/stable-diffusion-3.5-large-TurboX)")
+        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, variant="primary")
+
+        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",
+            )
+
+            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=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024, 
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=512,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=7.5,
+                    step=0.1,
+                    value=1.5,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=8, 
+                )
+
+        gr.Examples(examples=examples, inputs=[prompt], outputs=[result, seed], fn=infer, cache_examples=True, cache_mode="lazy")
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer,
+        inputs=[
+            prompt,
+            negative_prompt,
+            seed,
+            randomize_seed,
+            width,
+            height,
+            guidance_scale,
+            num_inference_steps,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)