import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
from diffusers.utils import load_image
from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
import copy
import random
import time
# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
loras = json.load(f)
# Initialize the base model
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
base_model = "black-forest-labs/FLUX.1-dev"
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
vae=good_vae,
transformer=pipe.transformer,
text_encoder=pipe.text_encoder,
tokenizer=pipe.tokenizer,
text_encoder_2=pipe.text_encoder_2,
tokenizer_2=pipe.tokenizer_2,
torch_dtype=dtype
)
MAX_SEED = 2**32-1
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
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
else:
width = 1024
height = 1024
return (
gr.update(placeholder=new_placeholder),
updated_text,
evt.index,
width,
height,
)
@spaces.GPU(duration=70)
def generate_image(prompt_mash, 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
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
prompt=prompt_mash,
num_inference_steps=steps,
guidance_scale=cfg_scale,
width=width,
height=height,
generator=generator,
joint_attention_kwargs={"scale": lora_scale},
output_type="pil",
good_vae=good_vae,
):
yield img
@spaces.GPU(duration=70)
def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
generator = torch.Generator(device="cuda").manual_seed(seed)
pipe_i2i.to("cuda")
image_input = load_image(image_input_path)
final_image = pipe_i2i(
prompt=prompt_mash,
image=image_input,
strength=image_strength,
num_inference_steps=steps,
guidance_scale=cfg_scale,
width=width,
height=height,
generator=generator,
joint_attention_kwargs={"scale": lora_scale},
output_type="pil",
).images[0]
return final_image
def run_lora(prompt, image_input, image_strength, 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"]
if(trigger_word):
if "trigger_position" in selected_lora:
if selected_lora["trigger_position"] == "prepend":
prompt_mash = f"{trigger_word} {prompt}"
else:
prompt_mash = f"{prompt} {trigger_word}"
else:
prompt_mash = f"{trigger_word} {prompt}"
else:
prompt_mash = prompt
with calculateDuration("Unloading LoRA"):
pipe.unload_lora_weights()
pipe_i2i.unload_lora_weights()
# Load LoRA weights
with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
if(image_input is not None):
if "weights" in selected_lora:
pipe_i2i.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
else:
pipe_i2i.load_lora_weights(lora_path)
else:
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)
if(image_input is not None):
final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
yield final_image, seed, gr.update(visible=False)
else:
image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
# Consume the generator to get the final image
final_image = None
step_counter = 0
for image in image_generator:
step_counter+=1
final_image = image
progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
yield image, seed, gr.update(value=progress_bar, visible=True)
yield final_image, seed, gr.update(value=progress_bar, visible=False)
def get_huggingface_safetensors(link):
split_link = link.split("/")
if(len(split_link) == 2):
model_card = ModelCard.load(link)
base_model = model_card.data.get("base_model")
print(base_model)
if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
raise Exception("Not a FLUX LoRA!")
image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
trigger_word = model_card.data.get("instance_prompt", "")
image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
fs = HfFileSystem()
try:
list_of_files = fs.ls(link, detail=False)
for file in list_of_files:
if(file.endswith(".safetensors")):
safetensors_name = file.split("/")[-1]
if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
image_elements = file.split("/")
image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
except Exception as e:
print(e)
gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
return split_link[1], link, safetensors_name, trigger_word, image_url
def check_custom_model(link):
if(link.startswith("https://")):
if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
link_split = link.split("huggingface.co/")
return get_huggingface_safetensors(link_split[1])
else:
return get_huggingface_safetensors(link)
def add_custom_lora(custom_lora):
global loras
if(custom_lora):
try:
title, repo, path, trigger_word, image = check_custom_model(custom_lora)
print(f"Loaded custom LoRA: {repo}")
card = f'''
<div class="custom_lora_card">
<span>Loaded custom LoRA:</span>
<div class="card_internal">
<img src="{image}" />
<div>
<h3>{title}</h3>
<small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
</div>
</div>
</div>
'''
existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
if(not existing_item_index):
new_item = {
"image": image,
"title": title,
"repo": repo,
"weights": path,
"trigger_word": trigger_word
}
print(new_item)
existing_item_index = len(loras)
loras.append(new_item)
return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
except Exception as e:
gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, ""
else:
return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
def remove_custom_lora():
return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
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}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.card_internal{display: flex;height: 100px;margin-top: .5em}
.card_internal img{margin-right: 1em}
.styler{--form-gap-width: 0px !important}
#progress{height:30px}
#progress .generating{display:none}
.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
title = gr.HTML(
"""<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> FLUX LoRA the Explorer</h1>""",
elem_id="title",
)
selected_index = gr.State(None)
with gr.Row():
with gr.Column(scale=3):
prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
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():
selected_info = gr.Markdown("")
gallery = gr.Gallery(
[(item["image"], item["title"]) for item in loras],
label="LoRA Gallery",
allow_preview=False,
columns=3,
elem_id="gallery"
)
with gr.Group():
custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
custom_lora_info = gr.HTML(visible=False)
custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
with gr.Column():
progress_bar = gr.Markdown(elem_id="progress",visible=False)
result = gr.Image(label="Generated Image")
with gr.Row():
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
input_image = gr.Image(label="Input image", type="filepath")
image_strength = gr.Slider(label="Image Strength", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
with gr.Column():
with gr.Row():
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
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, randomize=True)
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)
gallery.select(
update_selection,
inputs=[width, height],
outputs=[prompt, selected_info, selected_index, width, height]
)
custom_lora.input(
add_custom_lora,
inputs=[custom_lora],
outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
)
custom_lora_button.click(
remove_custom_lora,
outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
)
gr.on(
triggers=[generate_button.click, prompt.submit],
fn=run_lora,
inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
outputs=[result, seed, progress_bar]
)
app.queue()
app.launch()