import gradio as gr
import torch
import os
from diffusers import StableDiffusionXLPipeline, AutoencoderKL, LCMScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from share_btn import community_icon_html, loading_icon_html, share_js
from cog_sdxl_dataset_and_utils import TokenEmbeddingsHandler
import lora
import copy
import json
import gc
import random
from urllib.parse import quote
lora_list = hf_hub_download(repo_id="multimodalart/LoraTheExplorer", filename="sdxl_loras.json", repo_type="space")
with open(lora_list, "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_compatible": item["is_compatible"],
"is_pivotal": item.get("is_pivotal", False),
"text_embedding_weights": item.get("text_embedding_weights", None),
"likes": item.get("likes", 0),
"downloads": item.get("downloads", 0),
"is_nc": item.get("is_nc", False),
"new": item.get("new", False),
}
for item in data
]
device = "cuda"
state_dicts = {}
for item in sdxl_loras_raw:
saved_name = hf_hub_download(item["repo"], item["weights"])
if not saved_name.endswith('.safetensors'):
state_dict = torch.load(saved_name)
else:
state_dict = load_file(saved_name)
state_dicts[item["repo"]] = {
"saved_name": saved_name,
"state_dict": state_dict
}
sdxl_loras_raw_new = [item for item in sdxl_loras_raw if item.get("new") == True]
sdxl_loras_raw = [item for item in sdxl_loras_raw if item.get("new") != True]
lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
)
pipe = StableDiffusionXLPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
vae=vae,
torch_dtype=torch.float16,
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to(device)
pipe.load_lora_weights(lcm_lora_id, weight_name="pytorch_lora_weights.safetensors", adapter_name="lcm_lora", use_auth_token=os.getenv('HF_TOKEN'))
last_lora = ""
last_merged = False
last_fused = False
js = '''
var button = document.getElementById('button');
// Add a click event listener to the button
button.addEventListener('click', function() {
element.classList.add('selected');
});
'''
def update_selection(selected_state: gr.SelectData, sdxl_loras, is_new=False):
lora_repo = sdxl_loras[selected_state.index]["repo"]
instance_prompt = sdxl_loras[selected_state.index]["trigger_word"]
new_placeholder = "Type a prompt. This LoRA applies for all prompts, no need for a trigger word" if instance_prompt == "" else "Type a prompt to use your selected LoRA"
weight_name = sdxl_loras[selected_state.index]["weights"]
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨ {'(non-commercial LoRA, `cc-by-nc`)' if sdxl_loras[selected_state.index]['is_nc'] else '' }"
is_compatible = sdxl_loras[selected_state.index]["is_compatible"]
is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
use_with_diffusers = f'''
## Using [`{lora_repo}`](https://huggingface.co/{lora_repo})
## Use it with diffusers:
'''
if is_compatible:
use_with_diffusers += f'''
from diffusers import StableDiffusionXLPipeline
import torch
model_path = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
pipe.to("cuda")
pipe.load_lora_weights("{lora_repo}", weight_name="{weight_name}")
prompt = "{instance_prompt}..."
lora_scale= 0.9
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, cross_attention_kwargs={{"scale": lora_scale}}).images[0]
image.save("image.png")
'''
elif not is_pivotal:
use_with_diffusers += "This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with `bmaltais/kohya_ss` LoRA class, check out this [Google Colab](https://colab.research.google.com/drive/14aEJsKdEQ9_kyfsiV6JDok799kxPul0j )"
else:
use_with_diffusers += f"This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with sdxl-cog `TokenEmbeddingsHandler` class, check out the [model repo](https://huggingface.co/{lora_repo}#inference-with-🧨-diffusers)"
use_with_uis = f'''
## Use it with Comfy UI, Invoke AI, SD.Next, AUTO1111:
### Download the `*.safetensors` weights of [here](https://huggingface.co/{lora_repo}/resolve/main/{weight_name})
- [ComfyUI guide](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
- [Invoke AI guide](https://invoke-ai.github.io/InvokeAI/features/CONCEPTS/?h=lora#using-loras)
- [SD.Next guide](https://github.com/vladmandic/automatic)
- [AUTOMATIC1111 guide](https://stable-diffusion-art.com/lora/)
'''
if(is_new):
if(selected_state.index == 0):
selected_state.index = -9999
else:
selected_state.index *= -1
return (
updated_text,
instance_prompt,
gr.update(placeholder=new_placeholder),
selected_state,
use_with_diffusers,
use_with_uis,
gr.Gallery(selected_index=None)
)
def check_selected(selected_state):
if not selected_state:
raise gr.Error("You must select a LoRA")
def run_lora(prompt, negative, lora_scale, steps, selected_state, sdxl_loras, sdxl_loras_new, progress=gr.Progress(track_tqdm=True)):
global last_lora, last_merged, last_fused, pipe
print("Index when running ", selected_state.index)
if(selected_state.index < 0):
if(selected_state.index == -9999):
selected_state.index = 0
else:
selected_state.index *= -1
sdxl_loras = sdxl_loras_new
print("Selected State: ", selected_state.index)
print(sdxl_loras[selected_state.index]["repo"])
if negative == "":
negative = None
if not selected_state:
raise gr.Error("You must select a LoRA")
repo_name = sdxl_loras[selected_state.index]["repo"]
weight_name = sdxl_loras[selected_state.index]["weights"]
full_path_lora = state_dicts[repo_name]["saved_name"]
loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
#cross_attention_kwargs = None
if last_lora != repo_name:
#if(last_fused):
#pipe.unfuse_lora()
try:
pipe.load_lora_weights(loaded_state_dict, adapter_name=sdxl_loras[selected_state.index]["repo"])
except:
print(sdxl_loras[selected_state.index]["repo"], " was loaded")
pipe.set_adapters([sdxl_loras[selected_state.index]["repo"], "lcm_lora"], adapter_weights=[lora_scale, 1.0])
#last_fused = True
is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
if(is_pivotal):
#Add the textual inversion embeddings from pivotal tuning models
text_embedding_name = sdxl_loras[selected_state.index]["text_embedding_weights"]
text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
tokenizers = [pipe.tokenizer, pipe.tokenizer_2]
embedding_path = hf_hub_download(repo_id=repo_name, filename=text_embedding_name, repo_type="model")
embhandler = TokenEmbeddingsHandler(text_encoders, tokenizers)
embhandler.load_embeddings(embedding_path)
image = pipe(
prompt=prompt,
negative_prompt=negative,
guidance_scale=0.5,
num_inference_steps=steps
).images[0]
last_lora = repo_name
gc.collect()
return image, gr.update(visible=True)
def shuffle_gallery(sdxl_loras):
random.shuffle(sdxl_loras)
return [(item["image"], item["title"]) for item in sdxl_loras], sdxl_loras
def swap_gallery(order, sdxl_loras):
if(order == "random"):
return shuffle_gallery(sdxl_loras)
else:
sorted_gallery = sorted(sdxl_loras, key=lambda x: x.get(order, 0), reverse=True)
return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery
def deselect():
return gr.Gallery(selected_index=None)
with gr.Blocks(css="custom.css") as demo:
gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
gr_sdxl_loras_new = gr.State(value=sdxl_loras_raw_new)
title = gr.HTML(
"""
Experimental LCM LoRA the Explorer
Combine loading an LCM-LoRA with your favorite SDXL LoRa and run LoRAs in only 8 steps. Check out our blogon how this works. Try experimenting with step count and LoRA strength in advanced settings!Not all LoRAs may combine well with the LCM-LoRA
""",
elem_id="title",
)
selected_state = gr.State()
with gr.Row(elem_id="main_app"):
with gr.Group(elem_id="gallery_box"):
order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio")
selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", )
new_gallery = gr.Gallery(label="New LoRAs", elem_id="gallery_new", columns=3, value=[(item["image"], item["title"]) for item in sdxl_loras_raw_new], allow_preview=False, show_share_button=False)
gallery = gr.Gallery(
#value=[(item["image"], item["title"]) for item in sdxl_loras],
label="SDXL LoRA Gallery",
allow_preview=False,
columns=3,
elem_id="gallery",
show_share_button=False,
height=784
)
with gr.Column():
prompt_title = gr.Markdown(
value="### Click on a LoRA in the gallery to select it",
visible=True,
elem_id="selected_lora",
)
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA", elem_id="prompt")
button = gr.Button("Run", elem_id="run_button")
with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Share to community", elem_id="share-btn")
result = gr.Image(
interactive=False, label="Generated Image", elem_id="result-image"
)
with gr.Accordion("Advanced options", open=False):
negative = gr.Textbox(label="Negative Prompt", visible=False)
weight = gr.Slider(0, 10, value=1.2, step=0.1, label="LoRA weight")
steps = gr.Slider(0,8, value=4, step=1, label="Steps")
with gr.Column(elem_id="extra_info"):
with gr.Accordion(
"Use it with: 🧨 diffusers, ComfyUI, Invoke AI, SD.Next, AUTO1111",
open=False,
elem_id="accordion",
):
with gr.Row():
use_diffusers = gr.Markdown("""## Select a LoRA first 🤗""")
use_uis = gr.Markdown()
with gr.Accordion("Submit a LoRA! 📥", open=False):
submit_title = gr.Markdown(
"### Streamlined submission coming soon! Until then [suggest your LoRA in the community tab](https://huggingface.co/spaces/multimodalart/LoraTheExplorer/discussions) 🤗"
)
with gr.Group(elem_id="soon"):
submit_source = gr.Radio(
["Hugging Face", "CivitAI"],
label="LoRA source",
value="Hugging Face",
)
with gr.Row():
submit_source_hf = gr.Textbox(
label="Hugging Face Model Repo",
info="In the format `username/model_id`",
)
submit_safetensors_hf = gr.Textbox(
label="Safetensors filename",
info="The filename `*.safetensors` in the model repo",
)
with gr.Row():
submit_trigger_word_hf = gr.Textbox(label="Trigger word")
submit_image = gr.Image(
label="Example image (optional if the repo already contains images)"
)
submit_button = gr.Button("Submit!")
submit_disclaimer = gr.Markdown(
"This is a curated gallery by me, [apolinário (multimodal.art)](https://twitter.com/multimodalart). I'll try to include as many cool LoRAs as they are submitted! You can [duplicate this Space](https://huggingface.co/spaces/multimodalart/LoraTheExplorer?duplicate=true) to use it privately, and add your own LoRAs by editing `sdxl_loras.json` in the Files tab of your private space."
)
order_gallery.change(
fn=swap_gallery,
inputs=[order_gallery, gr_sdxl_loras],
outputs=[gallery, gr_sdxl_loras],
queue=False
)
gallery.select(
fn=update_selection,
inputs=[gr_sdxl_loras],
outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis, new_gallery],
queue=False,
show_progress=False
)
new_gallery.select(
fn=update_selection,
inputs=[gr_sdxl_loras_new, gr.State(True)],
outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis, gallery],
queue=False,
show_progress=False
)
prompt.submit(
fn=check_selected,
inputs=[selected_state],
queue=False,
show_progress=False
).success(
fn=run_lora,
inputs=[prompt, negative, weight, steps, selected_state, gr_sdxl_loras, gr_sdxl_loras_new],
outputs=[result, share_group],
)
button.click(
fn=check_selected,
inputs=[selected_state],
queue=False,
show_progress=False
).success(
fn=run_lora,
inputs=[prompt, negative, weight, steps, selected_state, gr_sdxl_loras, gr_sdxl_loras_new],
outputs=[result, share_group],
)
share_button.click(None, [], [], js=share_js)
demo.load(fn=shuffle_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False, js=js)
demo.queue(max_size=20)
demo.launch(share=True)