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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", use_safetensors=True) | |
pipe.enable_xformers_memory_efficient_attention() | |
else: | |
pipe = DiffusionPipeline.from_pretrained(model_path, use_safetensors=True) | |
pipe = pipe.to(device) | |
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() | |