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import gradio as gr
from huggingface_hub import login, HfFileSystem
from diffusers import DiffusionPipeline, StableDiffusionXLPipeline
import torch
import copy
import os
import spaces
import random
is_shared_ui = True if "fffiloni/sd-xl-lora-fusion" in os.environ['SPACE_ID'] else False
hf_token = os.environ.get("HF_TOKEN")
login(token = hf_token)
fs = HfFileSystem(token=hf_token)
original_pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
def load_sfts(repo_1_id, repo_2_id):
# List all ".safetensors" files in repos
sfts_available_files_1 = fs.glob(f"{repo_1_id}/**.safetensors")
sfts_available_files_2 = fs.glob(f"{repo_2_id}/**.safetensors")
return gr.update(choices=sfts_available_files_1, value=sfts_available_files_1[0], visible=True), gr.update(choices=sfts_available_files_2, value=sfts_available_files_2[0], visible=True)
@spaces.GPU
def infer(lora_1_id, lora_1_sfts, lora_2_id, lora_2_sfts, prompt, negative_prompt, lora_1_scale, lora_2_scale, seed):
unet = copy.deepcopy(original_pipe.unet)
text_encoder = copy.deepcopy(original_pipe.text_encoder)
text_encoder_2 = copy.deepcopy(original_pipe.text_encoder_2)
pipe = StableDiffusionXLPipeline(
vae = original_pipe.vae,
text_encoder = text_encoder,
text_encoder_2 = text_encoder_2,
scheduler = original_pipe.scheduler,
tokenizer = original_pipe.tokenizer,
tokenizer_2 = original_pipe.tokenizer_2,
unet = unet
)
pipe.to("cuda")
pipe.load_lora_weights(
lora_1_id,
weight_name = lora_1_sfts,
low_cpu_mem_usage = True,
use_auth_token = True
)
pipe.fuse_lora(lora_1_scale)
pipe.load_lora_weights(
lora_2_id,
weight_name = lora_2_sfts,
low_cpu_mem_usage = True,
use_auth_token = True
)
pipe.fuse_lora(lora_2_scale)
if negative_prompt == "" :
negative_prompt = None
if seed < 0 :
seed = random.randint(0, 423538377342)
generator = torch.Generator(device="cuda").manual_seed(seed)
image = pipe(
prompt = prompt,
negative_prompt = negative_prompt,
num_inference_steps = 25,
width = 1024,
height = 1024,
generator = generator
).images[0]
return image, seed
css="""
#col-container{
margin: 0 auto;
max-width: 680px;
text-align: left;
}
div#warning-duplicate {
background-color: #ebf5ff;
padding: 0 10px 5px;
margin: 20px 0;
}
div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
color: #0f4592!important;
}
div#warning-duplicate strong {
color: #0f4592;
}
p.actions {
display: flex;
align-items: center;
margin: 20px 0;
}
div#warning-duplicate .actions a {
display: inline-block;
margin-right: 10px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
if is_shared_ui:
top_description = gr.HTML(f'''
<div class="gr-prose">
<h2><svg xmlns="http://www.w3.org/2000/svg" width="18px" height="18px" style="margin-right: 0px;display: inline-block;"fill="none"><path fill="#fff" d="M7 13.2a6.3 6.3 0 0 0 4.4-10.7A6.3 6.3 0 0 0 .6 6.9 6.3 6.3 0 0 0 7 13.2Z"/><path fill="#fff" fill-rule="evenodd" d="M7 0a6.9 6.9 0 0 1 4.8 11.8A6.9 6.9 0 0 1 0 7 6.9 6.9 0 0 1 7 0Zm0 0v.7V0ZM0 7h.6H0Zm7 6.8v-.6.6ZM13.7 7h-.6.6ZM9.1 1.7c-.7-.3-1.4-.4-2.2-.4a5.6 5.6 0 0 0-4 1.6 5.6 5.6 0 0 0-1.6 4 5.6 5.6 0 0 0 1.6 4 5.6 5.6 0 0 0 4 1.7 5.6 5.6 0 0 0 4-1.7 5.6 5.6 0 0 0 1.7-4 5.6 5.6 0 0 0-1.7-4c-.5-.5-1.1-.9-1.8-1.2Z" clip-rule="evenodd"/><path fill="#000" fill-rule="evenodd" d="M7 2.9a.8.8 0 1 1 0 1.5A.8.8 0 0 1 7 3ZM5.8 5.7c0-.4.3-.6.6-.6h.7c.3 0 .6.2.6.6v3.7h.5a.6.6 0 0 1 0 1.3H6a.6.6 0 0 1 0-1.3h.4v-3a.6.6 0 0 1-.6-.7Z" clip-rule="evenodd"/></svg>
Note: you might want to use private custom LoRa models</h2>
<p class="main-message">
To do so, <strong>duplicate the Space</strong> and run it on your own profile using <strong>your own access token</strong> and eventually a GPU (T4-small or A10G-small) for faster inference without waiting in the queue.<br />
</p>
<p class="actions">
<a href="https://huggingface.co/spaces/{os.environ['SPACE_ID']}?duplicate=true">
<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg-dark.svg" alt="Duplicate this Space" />
</a>
to start using private models and skip the queue
</p>
</div>
''', elem_id="warning-duplicate")
title = gr.HTML(
'''
<h1 style="text-align: center;">LoRA Fusion</h1>
<p style="text-align: center;">Fuse 2 custom LoRa models</p>
'''
)
# PART 1 • MODELS
with gr.Row():
with gr.Column():
lora_1_id = gr.Textbox(
label = "LoRa 1 ID",
placeholder = "username/model_id"
)
lora_1_sfts = gr.Textbox(
label = "Safetensors file",
placeholder = "specific_chosen.safetensors"
)
with gr.Column():
lora_2_id = gr.Dropdown(
label = "LoRa 2 ID",
placeholder = "username/model_id",
visible=False
)
lora_2_sfts = gr.Dropdown(
label = "Safetensors file",
placeholder = "specific_chosen.safetensors",
visible=False
)
load_models_btn = gr.Button("Load models and .safetensors")
# PART 2 • INFERENCE
with gr.Row():
prompt = gr.Textbox(
label = "Your prompt",
info = "Use your trigger words into a coherent prompt",
placeholde = "e.g: a triggerWordOne portrait in triggerWord2 style"
)
run_btn = gr.Button("Run")
output_image = gr.Image(
label = "Output"
)
# Advanced Settings
with gr.Accordion("Advanced Settings", open=False):
with gr.Row():
lora_1_scale = gr.Slider(
label = "LoRa 1 scale",
minimum = 0,
maximum = 1,
step = 0.1,
value = 0.7
)
lora_2_scale = gr.Slider(
label = "LoRa 2 scale",
minimum = 0,
maximum = 1,
step = 0.1,
value = 0.7
)
negative_prompt = gr.Textbox(
label = "Negative prompt"
)
seed = gr.Slider(
label = "Seed",
info = "-1 denotes a random seed",
minimum = -1,
maximum = 423538377342,
value = -1
)
last_used_seed = gr.Number(
label = "Last used seed",
info = "the seed used in the last generation",
)
# ACTIONS
load_models_btn.click(
fn = load_sfts,
inputs = [
lora_1_id,
lora_2_id
],
outputs = [
lora_1_sfts,
lora_2_sfts
]
)
run_btn.click(
fn = infer,
inputs = [
lora_1_id,
lora_1_sfts,
lora_2_id,
lora_2_sfts,
prompt,
negative_prompt,
lora_1_scale,
lora_2_scale,
seed
],
outputs = [
output_image,
last_used_seed
]
)
demo.queue(concurrency_count=2).launch()
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