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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, | |
) | |
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 | |
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() |