import spaces # Import this first to avoid CUDA initialization issues
import os
import gradio as gr
import json
import torch
import random
import time
from PIL import Image
from diffusers import DiffusionPipeline
# Define the device
device = "cuda" if torch.cuda.is_available() else "cpu"
# Use the 'waffles' environment variable as the access token
hf_token = os.getenv('waffles')
# Ensure the token is loaded correctly
if not hf_token:
raise ValueError("Hugging Face API token not found. Please set the 'waffles' environment variable.")
# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
loras = json.load(f)
# Initialize the base model with authentication and specify the device
# Initialize the base model with authentication and specify the device
pipe = DiffusionPipeline.from_pretrained(
"black-forest-labs/FLUX.1-schnell",
torch_dtype=torch.bfloat16,
token=hf_token
).to(device)
MAX_SEED = 2**32 - 1
class calculateDuration:
def __init__(self, activity_name=""):
self.activity_name = activity_name
def __enter__(self):
self.start_time = time.time()
return self
def __exit__(self, exc_type, exc_value, traceback):
self.end_time = time.time()
self.elapsed_time = self.end_time - self.start_time
if self.activity_name:
print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
else:
print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
@spaces.GPU(duration=90)
def generate_images(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, num_images, progress):
generator = torch.Generator(device=device).manual_seed(seed)
images = []
with calculateDuration("Generating images"):
for _ in range(num_images):
# Generate each image
image = pipe(
prompt=f"{prompt} {trigger_word}",
num_inference_steps=steps,
guidance_scale=cfg_scale,
width=width,
height=height,
generator=generator,
joint_attention_kwargs={"scale": lora_scale},
).images[0]
images.append(image)
return images
def run_lora(prompt, cfg_scale, steps, selected_repo, randomize_seed, seed, width, height, lora_scale, num_images, progress=gr.Progress(track_tqdm=True)):
if not selected_repo:
raise gr.Error("You must select a LoRA before proceeding.")
selected_lora = next((lora for lora in loras if lora["repo"] == selected_repo), None)
if not selected_lora:
raise gr.Error("Selected LoRA not found.")
lora_path = selected_lora["repo"]
trigger_word = selected_lora["trigger_word"]
# Load LoRA weights
with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
if "weights" in selected_lora:
pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
else:
pipe.load_lora_weights(lora_path)
# Set random seed for reproducibility
with calculateDuration("Randomizing seed"):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
images = generate_images(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, num_images, progress)
pipe.to("cpu")
pipe.unload_lora_weights()
return images, seed
def update_selection(evt: gr.SelectData):
index = evt.index
selected_lora = loras[index]
return f"Selected LoRA: {selected_lora['title']}", selected_lora["repo"]
run_lora.zerogpu = True
css = '''
#gen_btn{height: 100%}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.5em}
#gallery .grid-wrap{height: auto; width: auto;}
#gallery .gallery-item{width: 50px; height: 50px; margin: 0px;} /* Make buttons 50% height and width */
#gallery img{width: 100%; height: 100%; object-fit: cover;} /* Resize images to fit buttons */
#info_blob {
background-color: #f0f0f0;
border: 2px solid #ccc;
padding: 10px;
margin: 10px 0;
text-align: center;
font-size: 1.2em;
font-weight: bold;
color: #333;
border-radius: 8px;
}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
title = gr.HTML(
"""
FLUX LoRA the Explorer
""",
elem_id="title",
)
# Info blob stating what the app is running
info_blob = gr.HTML(
""" Activist, Futurist, and Realist LoRa-stocked Quick-Use Image Manufactory (over Flux Schnell)
"""
)
selected_lora_text = gr.Markdown("Selected LoRA: None")
selected_repo = gr.State(value="")
# Prompt takes the full line
prompt = gr.Textbox(label="Prompt", lines=5, placeholder="Type a prompt after selecting a LoRA", elem_id="full_line_prompt")
with gr.Row():
with gr.Column(scale=1): # LoRA collection on the left
gallery = gr.Gallery(
[(item["image"], item["title"]) for item in loras],
label="LoRA Gallery",
allow_preview=False,
columns=3,
elem_id="gallery"
)
with gr.Column(scale=1): # Generated images on the right
result = gr.Gallery(label="Generated Images")
seed = gr.Number(label="Seed", value=0, interactive=False)
with gr.Column():
with gr.Row():
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=1)
steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=4)
with gr.Row():
width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
with gr.Row():
randomize_seed = gr.Checkbox(True, label="Randomize seed")
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.95)
num_images = gr.Slider(label="Number of Images", minimum=1, maximum=4, step=1, value=1)
gallery.select(
fn=update_selection,
inputs=[],
outputs=[selected_lora_text, selected_repo]
)
generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
generate_button.click(
run_lora,
inputs=[prompt, cfg_scale, steps, selected_repo, randomize_seed, seed, width, height, lora_scale, num_images],
outputs=[result, seed]
)
app.queue()
app.launch()