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import json
import random
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline, LCMScheduler
with open("sdxl_lora.json", "r") as file:
data = json.load(file)
sdxl_loras_raw = [
{
"image": item["image"],
"title": item["title"],
"repo": item["repo"],
"trigger_word": item["trigger_word"],
"weights": item["weights"],
"is_pivotal": item.get("is_pivotal", False),
"text_embedding_weights": item.get("text_embedding_weights", None),
"likes": item.get("likes", 0),
}
for item in data
]
# Sort the loras by likes
sdxl_loras_raw = sorted(sdxl_loras_raw, key=lambda x: x["likes"], reverse=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
if gr.NO_RELOAD:
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16")
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights("jasperai/flash-sdxl", adapter_name="lora")
pipe.to(device="cuda", dtype=torch.float16)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def check_and_load_lora_user(user_lora_selector, user_lora_weight, gr_lora_loaded):
flash_sdxl_id = "jasperai/flash-sdxl"
global pipe
if user_lora_selector == "" or user_lora_selector == "":
raise gr.Error("Please select a LoRA before running the inference.")
if gr_lora_loaded != user_lora_selector:
gr.Info("Loading LoRA")
pipe.unload_lora_weights()
pipe.load_lora_weights(flash_sdxl_id, adapter_name="lora")
pipe.load_lora_weights(user_lora_selector, adapter_name="user")
pipe.set_adapters(["lora", "user"], adapter_weights=[1.0, user_lora_weight])
gr.Info("LoRA Loaded")
gr_lora_loaded = user_lora_selector
return gr_lora_loaded
def rescale_lora(user_lora_weight):
global pipe
pipe.set_adapters(["lora", "user"], adapter_weights=[1.0, user_lora_weight])
def update_selection(
selected_state: gr.SelectData,
gr_sdxl_loras,
):
lora_id = gr_sdxl_loras[selected_state.index]["repo"]
trigger_word = gr_sdxl_loras[selected_state.index]["trigger_word"]
return lora_id, trigger_word
@spaces.GPU
def infer(
pre_prompt,
prompt,
seed,
randomize_seed,
num_inference_steps,
negative_prompt,
guidance_scale,
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
if pre_prompt != "":
prompt = f"{pre_prompt} {prompt}"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
).images[0]
return image
css = """
h1 {
text-align: center;
display:block;
}
p {
text-align: justify;
display:block;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
gr.Markdown(
f"""
# ⚡ FlashDiffusion: FlashLoRA ⚡
This is an interactive demo of [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) **on top of** existing LoRAs.
The distillation method proposed in [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) *by Clément Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin* from Jasper Research.
The LoRAs can be added **without** any retraining for similar results in most cases. Feel free to tweak the parameters and use your own LoRAs by giving a look at the [Github Repo](https://github.com/gojasper/flash-diffusion)
"""
)
gr.Markdown(
"If you enjoy the space, please also promote *open-source* by giving a ⭐ to our repo [](https://github.com/gojasper/flash-diffusion)"
)
# Index of selected LoRA
gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
# Serve as memory for currently loaded lora in pipe
gr_lora_loaded = gr.State(value="")
gr_lora_id = gr.State(value="")
with gr.Row():
with gr.Blocks():
with gr.Column():
user_lora_selector = gr.Textbox(
label="Current Selected LoRA",
max_lines=1,
interactive=False,
)
user_lora_weight = gr.Slider(
label="Selected LoRA Weight",
minimum=0.5,
maximum=3,
step=0.1,
value=1,
)
gallery = gr.Gallery(
value=[(item["image"], item["title"]) for item in sdxl_loras_raw],
label="SDXL LoRA Gallery",
allow_preview=False,
columns=3,
elem_id="gallery",
show_share_button=False,
)
with gr.Column():
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
scale=5,
)
run_button = gr.Button("Run", scale=1)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
pre_prompt = gr.Text(
label="Pre-Prompt",
show_label=True,
max_lines=1,
placeholder="Pre Prompt from the LoRA config",
container=True,
scale=5,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=4,
maximum=8,
step=1,
value=4,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=6,
step=0.5,
value=1,
)
hint_negative = gr.Markdown(
"""💡 _Hint : Negative Prompt will only work with Guidance > 1 but the model was
trained to be used with guidance = 1 (ie. without guidance).
Can degrade the results, use cautiously._"""
)
negative_prompt = gr.Text(
label="Negative Prompt",
show_label=False,
max_lines=1,
placeholder="Enter a negative Prompt",
container=False,
)
gr.on(
[
run_button.click,
seed.change,
randomize_seed.change,
# prompt.change,
prompt.submit,
negative_prompt.change,
negative_prompt.submit,
guidance_scale.change,
],
fn=check_and_load_lora_user,
inputs=[user_lora_selector, user_lora_weight, gr_lora_loaded],
outputs=[gr_lora_loaded],
).success(
fn=infer,
inputs=[
pre_prompt,
prompt,
seed,
randomize_seed,
num_inference_steps,
negative_prompt,
guidance_scale,
],
outputs=[result],
show_progress=True,
)
user_lora_weight.change(
fn=rescale_lora,
inputs=[user_lora_weight],
outputs=[],
show_progress="hidden",
)
gallery.select(
fn=update_selection,
inputs=[gr_sdxl_loras],
outputs=[
user_lora_selector,
pre_prompt,
],
show_progress=False,
)
gr.Markdown("**Disclaimer:**")
gr.Markdown(
"This demo is only for research purpose. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards."
)
demo.queue().launch()
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