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import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
# List of models
models = {
"sdxl-turbo": "stabilityai/sdxl-turbo",
"MistoLine": "TheMistoAI/MistoLine",
"UnfilteredAI/NSFW": "UnfilteredAI/NSFW-gen-v2",
"runwayml/SD":"runwayml/stable-diffusion-v1-5"
}
# Cache to store loaded pipelines
pipelines = {}
# Function to load a model
def load_model(model_name):
if model_name in pipelines:
return pipelines[model_name]
if model_name not in models:
raise ValueError(f"Model {model_name} is not available.")
model_path = models[model_name]
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16, variant="fp16")
pipe.enable_xformers_memory_efficient_attention()
else:
pipe = DiffusionPipeline.from_pretrained(model_path)
pipe = pipe.to(device)
# Disable NSFW filters if the pipeline supports it
if hasattr(pipe, 'safety_checker'):
pipe.safety_checker = None
pipelines[model_name] = pipe
return pipe
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def infer(model_name, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
pipe = load_model(model_name)
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
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;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image Gradio Template
Currently running on {power_device}.
""")
with gr.Row():
model_name = gr.Dropdown(
label="Select Model",
choices=list(models.keys()),
value="sdxl-turbo",
show_label=True
)
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)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=0.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=12,
step=1,
value=2,
)
gr.Examples(
examples = examples,
inputs = [prompt]
)
run_button.click(
fn = infer,
inputs = [model_name, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs = [result]
)
demo.queue().launch()
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