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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/stable-diffusion-3.5-large" # 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),
):
# Seed Handling
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
# Generate Image
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 = """
/* CSS Styling (remains unchanged from earlier examples) */
"""
# Higher Defaults for Advanced Settings
DEFAULT_STEPS = 50
DEFAULT_GUIDANCE = 7.5
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("<div id='header'><h1 id='title'>Veshon: Veshup's Image Generation AI</h1><p id='subtitle'>Create stunning images with just a prompt. Powered by cutting-edge AI technology.</p></div>")
with gr.Row():
prompt = gr.Text(
label="Your Creative Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt here...",
container=False,
)
run_button = gr.Button("Generate Image", scale=0, variant="primary", elem_classes="gradio-button")
result = gr.Image(label="Generated Image", 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 if needed",
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=768, # Higher default resolution
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=768, # Higher default resolution
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.0,
maximum=15.0,
step=0.1,
value=DEFAULT_GUIDANCE, # Higher guidance by default
)
num_inference_steps = gr.Slider(
label="Number of Inference Steps",
minimum=1,
maximum=150, # Increased maximum steps
step=1,
value=DEFAULT_STEPS, # Higher inference steps for quality
)
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()
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),
):
# Seed Handling
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
# Generate Image
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 = """
/* CSS Styling (remains unchanged from earlier examples) */
"""
# Higher Defaults for Advanced Settings
DEFAULT_STEPS = 50
DEFAULT_GUIDANCE = 7.5
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown("<div id='header'><h1 id='title'>Veginator: Veshup's Image Generation AI</h1><p id='subtitle'>Create stunning images with just a prompt. Powered by cutting-edge AI technology.</p></div>")
with gr.Row():
prompt = gr.Text(
label="Your Creative Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt here...",
container=False,
)
run_button = gr.Button("Generate Image", scale=0, variant="primary", elem_classes="gradio-button")
result = gr.Image(label="Generated Image", 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 if needed",
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=768, # Higher default resolution
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=768, # Higher default resolution
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.0,
maximum=15.0,
step=0.1,
value=DEFAULT_GUIDANCE, # Higher guidance by default
)
num_inference_steps = gr.Slider(
label="Number of Inference Steps",
minimum=1,
maximum=150, # Increased maximum steps
step=1,
value=DEFAULT_STEPS, # Higher inference steps for quality
)
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()
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