Update app.py

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)

app.py

@@ -1,154 +0,0 @@
-import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
-
-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 = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-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),
-):
-    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 = [
-    "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: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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",
-                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)
-
-            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],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()