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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 | |
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 | |
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 | |
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() | |
print(pipe.get_active_adapters()) | |
# 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) | |
print(pipe.get_active_adapters()) | |
# 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 if image else "/home/user/app/custom.png", | |
"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 La [beta]</h1><br>Mix and match any FLUX[dev] LoRAs""", | |
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() | |