multimodalart's picture
Update app.py
5a92ac7 verified
raw
history blame
11.4 kB
import os
import gradio as gr
import json
import logging
import torch
from PIL import Image
import spaces
from diffusers import DiffusionPipeline
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
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
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_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
image = pipe(
prompt=prompt_mash,
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"]
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
# 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"])
#pipe.fuse_lora()
else:
pipe.load_lora_weights(lora_path)
#pipe.fuse_lora()
# Set random seed for reproducibility
with calculateDuration("Randomizing seed"):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
pipe.to("cpu")
#pipe.unfuse_lora()
pipe.unload_lora_weights()
return image, seed
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}
'''
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"> 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(scale=3):
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(scale=4):
result = gr.Image(label="Generated Image")
with gr.Row():
with gr.Accordion("Advanced Settings", open=False):
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, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
outputs=[result, seed]
)
app.queue()
app.launch()