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import gradio as gr
import numpy as np
import random
import torch
from PIL import Image
import os
from huggingface_hub import hf_hub_download
from pathlib import Path
import sys
# Add src directory to Python path
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from src import model_loader
from src import pipeline
from src.config import Config, DeviceConfig
from transformers import CLIPTokenizer
# Create data directory if it doesn't exist
data_dir = Path("data")
data_dir.mkdir(exist_ok=True)
# Model configuration
MODEL_REPO = "stable-diffusion-v1-5/stable-diffusion-v1-5"
MODEL_FILENAME = "v1-5-pruned-emaonly.ckpt"
model_file = data_dir / MODEL_FILENAME
# Download model if it doesn't exist
if not model_file.exists():
print(f"Downloading model from {MODEL_REPO}...")
model_file = hf_hub_download(
repo_id=MODEL_REPO,
filename=MODEL_FILENAME,
local_dir=data_dir,
local_dir_use_symlinks=False
)
print("Model downloaded successfully!")
# Device configuration
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Initialize configuration
config = Config(
device=DeviceConfig(device=device),
tokenizer=CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
)
# Load models with SE blocks enabled
config.models = model_loader.load_models(str(model_file), device, use_se=True)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
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)
# Update config with user settings
config.seed = seed
config.diffusion.cfg_scale = guidance_scale
config.diffusion.n_inference_steps = num_inference_steps
config.model.width = width
config.model.height = height
# Generate image
output_image = pipeline.generate(
prompt=prompt,
uncond_prompt=negative_prompt,
config=config
)
# Convert numpy array to PIL Image
image = Image.fromarray(output_image)
return image, seed
examples = [
"A ultra sharp photorealtici painting of a futuristic cityscape at night with neon lights and flying cars",
"A serene mountain landscape at sunset with snow-capped peaks and a clear lake reflection",
"A detailed portrait of a cyberpunk character with glowing neon implants and holographic tattoos",
]
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(" # Custom Diffusion Model Text-to-Image Generator")
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=42,
)
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=7.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=50,
)
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() |