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import json | |
import random | |
import requests | |
import os | |
from PIL import Image | |
import gradio as gr | |
import numpy as np | |
import spaces | |
import torch | |
from diffusers import DiffusionPipeline, LCMScheduler | |
from peft import PeftModel | |
# Custom LCMScheduler to ignore unexpected attributes | |
class CustomLCMScheduler(LCMScheduler): | |
def config(self): | |
return {k: v for k, v in super().config.items() if k != "skip_prk_steps"} | |
def get_image(image_data): | |
# ... (keep the get_image function as is) | |
with open("sdxl_lora.json", "r") as file: | |
data = json.load(file) | |
sdxl_loras_raw = [ | |
{ | |
"image": get_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 | |
] | |
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" | |
pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") | |
pipe.scheduler = CustomLCMScheduler.from_config(pipe.scheduler.config) | |
pipe.to(device=DEVICE, dtype=torch.float16) | |
# Load Flash SDXL LoRA | |
flash_sdxl_id = "jasperai/flash-sdxl" | |
pipe.load_lora_weights(flash_sdxl_id, adapter_name="flash_lora") | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 1024 | |
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 | |
def infer( | |
pre_prompt, | |
prompt, | |
seed, | |
randomize_seed, | |
num_inference_steps, | |
negative_prompt, | |
guidance_scale, | |
user_lora_selector, | |
user_lora_weight, | |
progress=gr.Progress(track_tqdm=True), | |
): | |
try: | |
# Load the user-selected LoRA | |
new_adapter_id = user_lora_selector.replace("/", "_") | |
pipe.load_lora_weights(user_lora_selector, adapter_name=new_adapter_id) | |
# Set adapter weights | |
pipe.set_adapters(["flash_lora", new_adapter_id], adapter_weights=[1.0, user_lora_weight]) | |
gr.Info("LoRA setup complete") | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
generator = torch.Generator().manual_seed(seed) | |
if pre_prompt != "": | |
prompt = f"{pre_prompt} {prompt}" | |
# Use Flash Diffusion settings | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
guidance_scale=1.0, # Flash Diffusion typically uses guidance_scale=1 | |
num_inference_steps=4, # Flash Diffusion uses fewer steps | |
generator=generator, | |
).images[0] | |
return image | |
except Exception as e: | |
gr.Error(f"An error occurred: {str(e)}") | |
return None | |
def infer( | |
pre_prompt, | |
prompt, | |
seed, | |
randomize_seed, | |
num_inference_steps, | |
negative_prompt, | |
guidance_scale, | |
user_lora_selector, | |
user_lora_weight, | |
progress=gr.Progress(track_tqdm=True), | |
): | |
load_lora_for_style(user_lora_selector) | |
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 = """ | |
body { | |
background-color: #1a1a1a; | |
color: #ffffff; | |
} | |
.container { | |
max-width: 900px; | |
margin: auto; | |
padding: 20px; | |
} | |
h1, h2 { | |
color: #4CAF50; | |
text-align: center; | |
} | |
.gallery { | |
display: flex; | |
flex-wrap: wrap; | |
justify-content: center; | |
} | |
.gallery img { | |
margin: 10px; | |
border-radius: 10px; | |
transition: transform 0.3s ease; | |
} | |
.gallery img:hover { | |
transform: scale(1.05); | |
} | |
.gradio-slider input[type="range"] { | |
background-color: #4CAF50; | |
} | |
.gradio-button { | |
background-color: #4CAF50 !important; | |
} | |
""" | |
with gr.Blocks(css=css) as demo: | |
gr.Markdown( | |
""" | |
# β‘ FlashDiffusion: Araminta K's FlashLoRA Showcase β‘ | |
This interactive demo showcases [Araminta K's models](https://huggingface.co/alvdansen) using [Flash Diffusion](https://gojasper.github.io/flash-diffusion-project/) technology. | |
## Acknowledgments | |
- Original Flash Diffusion technology by the Jasper AI team | |
- Based on the paper: [Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation](http://arxiv.org/abs/2406.02347) by ClΓ©ment Chadebec, Onur Tasar, Eyal Benaroche and Benjamin Aubin | |
- Models showcased here are created by Araminta K at Alvdansen Labs | |
Explore the power of FlashLoRA with Araminta K's unique artistic styles! | |
""" | |
) | |
gr_sdxl_loras = gr.State(value=sdxl_loras_raw) | |
gr_lora_id = gr.State(value="") | |
with gr.Row(): | |
with gr.Column(scale=2): | |
gallery = gr.Gallery( | |
value=[(get_image(item["image"]), item["title"]) for item in sdxl_loras_raw if get_image(item["image"]) is not None], | |
label="SDXL LoRA Gallery", | |
show_label=False, | |
elem_id="gallery", | |
columns=3, | |
height=600, | |
) | |
user_lora_selector = gr.Textbox( | |
label="Current Selected LoRA", | |
interactive=False, | |
) | |
with gr.Column(scale=3): | |
prompt = gr.Textbox( | |
label="Prompt", | |
placeholder="Enter your prompt", | |
lines=3, | |
) | |
with gr.Row(): | |
run_button = gr.Button("Run", variant="primary") | |
clear_button = gr.Button("Clear") | |
result = gr.Image(label="Result", height=512) | |
with gr.Accordion("Advanced Settings", open=False): | |
pre_prompt = gr.Textbox( | |
label="Pre-Prompt", | |
placeholder="Pre Prompt from the LoRA config", | |
lines=2, | |
) | |
with gr.Row(): | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=4, | |
maximum=8, | |
step=1, | |
value=4, | |
) | |
guidance_scale = gr.Slider( | |
label="Guidance Scale", | |
minimum=1, | |
maximum=6, | |
step=0.5, | |
value=1, | |
) | |
negative_prompt = gr.Textbox( | |
label="Negative Prompt", | |
placeholder="Enter a negative Prompt", | |
lines=2, | |
) | |
gr.on( | |
[run_button.click, prompt.submit], | |
fn=infer, | |
inputs=[ | |
pre_prompt, | |
prompt, | |
seed, | |
randomize_seed, | |
num_inference_steps, | |
negative_prompt, | |
guidance_scale, | |
user_lora_selector, | |
gr.Slider(label="Selected LoRA Weight", minimum=0.5, maximum=3, step=0.1, value=1), | |
], | |
outputs=[result], | |
) | |
clear_button.click(lambda: "", outputs=[prompt, result]) | |
gallery.select( | |
fn=update_selection, | |
inputs=[gr_sdxl_loras], | |
outputs=[user_lora_selector, pre_prompt], | |
) | |
gr.Markdown( | |
""" | |
## Unleash Your Creativity! | |
This showcase brings together the speed of Flash Diffusion and the artistic flair of Araminta K's models. | |
Craft your prompts, adjust the settings, and watch as AI brings your ideas to life in stunning detail. | |
Remember to use this tool ethically and respect copyright and individual privacy. | |
Enjoy exploring these unique artistic styles! | |
""" | |
) | |
demo.queue().launch() |