app.py

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
import requests

# 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 download_file(url, directory=None):
 if directory is None:
 directory = os.getcwd() # Use current working directory if not specified
 
 # Get the filename from the URL
 filename = url.split('/')[-1]
 
 # Full path for the downloaded file
 filepath = os.path.join(directory, filename)
 
 # Download the file
 response = requests.get(url)
 response.raise_for_status() # Raise an exception for bad status codes
 
 # Write the content to the file
 with open(filepath, 'wb') as file:
 file.write(response.content)
 
 return filepath
 
def update_selection(evt: gr.SelectData, selected_indices, width, height):
 selected_index = evt.index
 selected_indices = selected_indices or []
 if selected_index in selected_indices:
 # LoRA is already selected, remove it
 selected_indices.remove(selected_index)
 else:
 if len(selected_indices) < 2:
 selected_indices.append(selected_index)
 else:
 gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")
 return (
 gr.update(),
 gr.update(),
 gr.update(),
 gr.update(),
 gr.update(),
 gr.update(),
 gr.update(),
 gr.update(),
 gr.update(),
 gr.update(),
 )

 # Initialize outputs
 selected_info_1 = "Select a LoRA 1"
 selected_info_2 = "Select a LoRA 2"
 lora_scale_1 = 0.95
 lora_scale_2 = 0.95
 lora_image_1 = None
 lora_image_2 = None
 if len(selected_indices) >= 1:
 lora1 = loras[selected_indices[0]]
 selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
 lora_image_1 = lora1['image']
 if len(selected_indices) >= 2:
 lora2 = loras[selected_indices[1]]
 selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
 lora_image_2 = lora2['image']

 # Update prompt placeholder based on last selected LoRA
 if selected_indices:
 last_selected_lora = loras[selected_indices[-1]]
 new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
 else:
 new_placeholder = "Type a prompt after selecting a LoRA"

 return (
 gr.update(placeholder=new_placeholder),
 selected_info_1,
 selected_info_2,
 selected_indices,
 lora_scale_1,
 lora_scale_2,
 width,
 height,
 lora_image_1,
 lora_image_2,
 )

def remove_lora_1(selected_indices):
 selected_indices = selected_indices or []
 if len(selected_indices) >= 1:
 selected_indices.pop(0)
 # Update selected_info_1 and selected_info_2
 selected_info_1 = "Select a LoRA 1"
 selected_info_2 = "Select a LoRA 2"
 lora_scale_1 = 0.95
 lora_scale_2 = 0.95
 lora_image_1 = None
 lora_image_2 = None
 if len(selected_indices) >= 1:
 lora1 = loras[selected_indices[0]]
 selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
 lora_image_1 = lora1['image']
 if len(selected_indices) >= 2:
 lora2 = loras[selected_indices[1]]
 selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
 lora_image_2 = lora2['image']
 return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def remove_lora_2(selected_indices):
 selected_indices = selected_indices or []
 if len(selected_indices) >= 2:
 selected_indices.pop(1)
 # Update selected_info_1 and selected_info_2
 selected_info_1 = "Select a LoRA 1"
 selected_info_2 = "Select a LoRA 2"
 lora_scale_1 = 0.95
 lora_scale_2 = 0.95
 lora_image_1 = None
 lora_image_2 = None
 if len(selected_indices) >= 1:
 lora1 = loras[selected_indices[0]]
 selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
 lora_image_1 = lora1['image']
 if len(selected_indices) >= 2:
 lora2 = loras[selected_indices[1]]
 selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
 lora_image_2 = lora2['image']
 return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

def randomize_loras(selected_indices):
 if len(loras) < 2:
 raise gr.Error("Not enough LoRAs to randomize.")
 selected_indices = random.sample(range(len(loras)), 2)
 lora1 = loras[selected_indices[0]]
 lora2 = loras[selected_indices[1]]
 selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
 selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
 lora_scale_1 = 0.95
 lora_scale_2 = 0.95
 lora_image_1 = lora1['image']
 lora_image_2 = lora2['image']
 return selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2

@spaces.GPU(duration=70)
def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
 print("Generating image...")
 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": 1.0},
 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, seed):
 pipe_i2i.to("cuda")
 generator = torch.Generator(device="cuda").manual_seed(seed)
 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": 1.0},
 output_type="pil",
 ).images[0]
 return final_image

def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, progress=gr.Progress(track_tqdm=True)):
 if not selected_indices:
 raise gr.Error("You must select at least one LoRA before proceeding.")

 selected_loras = [loras[idx] for idx in selected_indices]

 # Build the prompt with trigger words
 prepends = []
 appends = []
 for lora in selected_loras:
 trigger_word = lora.get('trigger_word', '')
 if trigger_word:
 if lora.get("trigger_position") == "prepend":
 prepends.append(trigger_word)
 else:
 appends.append(trigger_word)
 prompt_mash = " ".join(prepends + [prompt] + appends)
 print("Prompt Mash: ", prompt_mash)
 # Unload previous LoRA weights
 with calculateDuration("Unloading LoRA"):
 pipe.unload_lora_weights()
 pipe_i2i.unload_lora_weights()

 # Load LoRA weights with respective scales
 lora_names = []
 lora_weights = []
 with calculateDuration("Loading LoRA weights"):
 for idx, lora in enumerate(selected_loras):
 lora_name = f"lora_{idx}"
 lora_names.append(lora_name)
 lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2)
 lora_path = lora['repo']
 weight_name = lora.get("weights")
 print(f"Lora Path: {lora_path}")
 if image_input is not None:
 if weight_name:
 pipe_i2i.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
 else:
 pipe_i2i.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
 else:
 if weight_name:
 pipe.load_lora_weights(lora_path, weight_name=weight_name, low_cpu_mem_usage=True, adapter_name=lora_name)
 else:
 pipe.load_lora_weights(lora_path, low_cpu_mem_usage=True, adapter_name=lora_name)
 print("Loaded LoRAs:", lora_names)
 if image_input is not None:
 pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
 else:
 pipe.set_adapters(lora_names, adapter_weights=lora_weights)
 # Set random seed for reproducibility
 with calculateDuration("Randomizing seed"):
 if randomize_seed:
 seed = random.randint(0, MAX_SEED)

 # Generate image
 if image_input is not None:
 final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
 yield final_image, seed, gr.update(visible=False)
 else:
 image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, 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(f"Base model: {base_model}")
 if base_model not in ["black-forest-labs/FLUX.1-dev", "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()
 safetensors_name = None
 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)
 raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA")
 if not safetensors_name:
 raise gr.Error("No *.safetensors file found in the repository")
 return split_link[1], link, safetensors_name, trigger_word, image_url
 else:
 raise gr.Error("Invalid Hugging Face repository link")

def check_custom_model(link):
 if link.endswith(".safetensors"):
 # Treat as direct link to the LoRA weights
 title = os.path.basename(link)
 repo = link
 path = None # No specific weight name
 trigger_word = ""
 image_url = None
 return title, repo, path, trigger_word, image_url
 elif link.startswith("https://"):
 if "huggingface.co" in link:
 link_split = link.split("huggingface.co/")
 return get_huggingface_safetensors(link_split[1])
 else:
 raise Exception("Unsupported URL")
 else:
 # Assume it's a Hugging Face model path
 return get_huggingface_safetensors(link)

def add_custom_lora(custom_lora, selected_indices):
 global loras
 if custom_lora:
 try:
 title, repo, path, trigger_word, image = check_custom_model(custom_lora)
 print(f"Loaded custom LoRA: {repo}")
 existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
 if existing_item_index is None:
 if(repo.endswith(".safetensors") and repo.startswith("http")):
 repo = download_file(repo)
 new_item = {
 "image": image,
 "title": title,
 "repo": repo,
 "weights": path,
 "trigger_word": trigger_word
 }
 print(f"New LoRA: {new_item}")
 existing_item_index = len(loras)
 loras.append(new_item)
 
 # Update gallery
 gallery_items = [(item["image"], item["title"]) for item in loras]
 # Update selected_indices if there's room
 if len(selected_indices) < 2:
 selected_indices.append(existing_item_index)
 else:
 gr.Warning("You can select up to 2 LoRAs, remove one to select a new one.")

 # Update selected_info and images
 selected_info_1 = "Select a LoRA 1"
 selected_info_2 = "Select a LoRA 2"
 lora_scale_1 = 0.95
 lora_scale_2 = 0.95
 lora_image_1 = None
 lora_image_2 = None
 if len(selected_indices) >= 1:
 lora1 = loras[selected_indices[0]]
 selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
 lora_image_1 = lora1['image'] if lora1['image'] else None
 if len(selected_indices) >= 2:
 lora2 = loras[selected_indices[1]]
 selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
 lora_image_2 = lora2['image'] if lora2['image'] else None
 print("Chegou no final")
 return (
 gr.update(value=gallery_items),
 selected_info_1,
 selected_info_2,
 selected_indices,
 lora_scale_1,
 lora_scale_2,
 lora_image_1,
 lora_image_2
 )
 except Exception as e:
 print(e)
 gr.Warning(str(e))
 return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()
 else:
 return gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update()

def remove_custom_lora(selected_indices):
 global loras
 if loras:
 custom_lora_repo = loras[-1]['repo']
 # Remove from loras list
 loras = loras[:-1]
 # Remove from selected_indices if selected
 custom_lora_index = len(loras)
 if custom_lora_index in selected_indices:
 selected_indices.remove(custom_lora_index)
 # Update gallery
 gallery_items = [(item["image"], item["title"]) for item in loras]
 # Update selected_info and images
 selected_info_1 = "Select a LoRA 1"
 selected_info_2 = "Select a LoRA 2"
 lora_scale_1 = 0.95
 lora_scale_2 = 0.95
 lora_image_1 = None
 lora_image_2 = None
 if len(selected_indices) >= 1:
 lora1 = loras[selected_indices[0]]
 selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
 lora_image_1 = lora1['image']
 if len(selected_indices) >= 2:
 lora2 = loras[selected_indices[1]]
 selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
 lora_image_2 = lora2['image']
 return (
 gr.update(value=gallery_items),
 selected_info_1,
 selected_info_2,
 selected_indices,
 lora_scale_1,
 lora_scale_2,
 lora_image_1,
 lora_image_2
 )
def run_test():
 print("testing")
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: 5vh}
#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
.custom_lora_card{margin-bottom: 1em}
.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}
.button_total{height: 100%}
#loaded_loras [data-testid="block-info"]{font-size:80%}
#custom_lora_structure{background: var(--block-background-fill)}
#custom_lora_btn{margin-top: auto;margin-bottom: 11px}
#random_btn{font-size: 300%}
'''

with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
 title = gr.HTML(
 """<h1><img src="https://i.imgur.com/L3uECk5.png" alt="LoRA"> LoRA Lab</h1>""",
 elem_id="title",
 )
 selected_indices = gr.State([])
 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):
 generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
 with gr.Row(elem_id="loaded_loras"):
 with gr.Column(scale=1, min_width=25):
 randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
 with gr.Column(scale=8):
 with gr.Row():
 with gr.Column(scale=0, min_width=50):
 lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
 with gr.Column(scale=3, min_width=100):
 selected_info_1 = gr.Markdown("Select a LoRA 1")
 with gr.Column(scale=5, min_width=50):
 lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95)
 with gr.Row():
 remove_button_1 = gr.Button("Remove", size="sm")
 with gr.Column(scale=8):
 with gr.Row():
 with gr.Column(scale=0, min_width=50):
 lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
 with gr.Column(scale=3, min_width=100):
 selected_info_2 = gr.Markdown("Select a LoRA 2")
 with gr.Column(scale=5, min_width=50):
 lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95)
 with gr.Row():
 remove_button_2 = gr.Button("Remove", size="sm")
 with gr.Row():
 with gr.Column():
 with gr.Group():
 with gr.Row(elem_id="custom_lora_structure"):
 custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="multimodalart/vintage-ads-flux", scale=3, min_width=150)
 add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
 remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
 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")
 gallery = gr.Gallery(
 [(item["image"], item["title"]) for item in loras],
 label="Or pick from the LoRA Explorer gallery",
 allow_preview=False,
 columns=5,
 elem_id="gallery"
 )
 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="Denoise Strength", info="Lower means more image influence", 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)

 gallery.select(
 update_selection,
 inputs=[selected_indices, width, height],
 outputs=[prompt, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, width, height, lora_image_1, lora_image_2]
 )
 remove_button_1.click(
 remove_lora_1,
 inputs=[selected_indices],
 outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
 )
 remove_button_2.click(
 remove_lora_2,
 inputs=[selected_indices],
 outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
 )
 randomize_button.click(
 randomize_loras,
 inputs=[selected_indices],
 outputs=[selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
 )
 add_custom_lora_button.click(
 add_custom_lora,
 inputs=[custom_lora, selected_indices],
 outputs=[gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
 ).success(
 run_test
 )
 remove_custom_lora_button.click(
 remove_custom_lora,
 inputs=[selected_indices],
 outputs=[gallery, selected_info_1, selected_info_2, selected_indices, lora_scale_1, lora_scale_2, lora_image_1, lora_image_2]
 )
 gr.on(
 triggers=[generate_button.click, prompt.submit],
 fn=run_lora,
 inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height],
 outputs=[result, seed, progress_bar]
 )

app.queue()
app.launch()