import gradio as gr
import json
import logging
import torch
import os
from os import path
from PIL import Image
import spaces
import copy
import random
import time
from huggingface_hub import hf_hub_download
from diffusers import FluxTransformer2DModel, FluxPipeline
import safetensors.torch
from safetensors.torch import load_file
import gc
cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
os.environ["TRANSFORMERS_CACHE"] = cache_path
os.environ["HF_HUB_CACHE"] = cache_path
os.environ["HF_HOME"] = cache_path
torch.backends.cuda.matmul.allow_tf32 = True
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
pipe.fuse_lora(lora_scale=0.125)
pipe.to(device="cuda", dtype=torch.bfloat16)
pipe.enable_model_cpu_offload()
# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
loras = json.load(f)
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")
def update_selection(evt: gr.SelectData, width, height):
selected_lora = loras[evt.index]
new_placeholder = f"Type a prompt for {selected_lora['title']}"
lora_repo = selected_lora["repo"]
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
if "aspect" in selected_lora:
if selected_lora["aspect"] == "portrait":
width = 768
height = 1024
elif selected_lora["aspect"] == "landscape":
width = 1024
height = 768
return (
gr.update(placeholder=new_placeholder),
updated_text,
evt.index,
width,
height,
)
@spaces.GPU(duration=70)
def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress):
pipe.to("cuda")
generator = torch.Generator(device="cuda").manual_seed(seed)
with calculateDuration("Generating image"):
# Generate 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]
return image
def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
if selected_index is None:
raise gr.Error("You must select a LoRA before proceeding.")
selected_lora = loras[selected_index]
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)
image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
pipe.to("cpu")
pipe.unload_lora_weights()
return image, seed
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: 10vh}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
title = gr.HTML(
"""<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> SOONfactory </h1>""",
elem_id="title",
)
# Info blob stating what the app is running
info_blob = gr.HTML(
"""<div id="info_blob"> Activist & Futurealist LoRa-stocked Img Manufactory (on Flux Merged)</div>"""
)
# Info blob stating what the app is running
info_blob = gr.HTML(
"""<div id="info_blob">Prephrase prompts w/: 1.RCA style 2. HST style autochrome 3.TOK hybrid 4.2004 photo 5.HST style 6.LEN Vladimir Lenin 7.TOK portra 8.HST portrait 9.flmft 10.HST in Peterhof 11.photo 12.pficonics 13.wh3r3sw4ld0 14.retrofuturism 15.vintage cover </div>"""
)
selected_index = gr.State(None)
with gr.Row():
with gr.Column(scale=3):
prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Select LoRa/Style & type prompt!")
with gr.Column(scale=1, elem_id="gen_column"):
generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
with gr.Row():
with gr.Column(scale=3):
selected_info = gr.Markdown("")
gallery = gr.Gallery(
[(item["image"], item["title"]) for item in loras],
label="LoRA Inventory",
allow_preview=False,
columns=3,
elem_id="gallery"
)
with gr.Column(scale=4):
result = gr.Image(label="Generated Image")
with gr.Row():
with gr.Accordion("Advanced Settings", open=True):
with gr.Column():
with gr.Row():
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=1, value=3)
steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=6)
with gr.Row():
width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=768)
height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=768)
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, randomize=True)
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.95)
gallery.select(
update_selection,
inputs=[width, height],
outputs=[prompt, selected_info, selected_index, width, height]
)
gr.on(
triggers=[generate_button.click, prompt.submit],
fn=run_lora,
inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
outputs=[result, seed]
)
app.queue(default_concurrency_limit=2).launch(show_error=True)
app.launch()