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import gradio as gr | |
import torch | |
from diffusers import StableDiffusionXLPipeline, AutoencoderKL | |
from huggingface_hub import hf_hub_download | |
from share_btn import community_icon_html, loading_icon_html, share_js | |
import lora | |
from time import sleep | |
import copy | |
import json | |
with open("sdxl_loras.json", "r") as file: | |
data = json.load(file) | |
sdxl_loras = [ | |
{ | |
"image": item["image"], | |
"title": item["title"], | |
"repo": item["repo"], | |
"trigger_word": item["trigger_word"], | |
"weights": item["weights"], | |
"is_compatible": item["is_compatible"], | |
} | |
for item in data | |
] | |
saved_names = [ | |
hf_hub_download(item["repo"], item["weights"]) for item in sdxl_loras | |
] | |
device = "cuda" # replace this to `mps` if on a MacOS Silicon | |
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, | |
).to("cpu") | |
original_pipe = copy.deepcopy(pipe) | |
pipe.to(device) | |
last_lora = "" | |
last_merged = False | |
def update_selection(selected_state: gr.SelectData): | |
lora_repo = sdxl_loras[selected_state.index]["repo"] | |
instance_prompt = sdxl_loras[selected_state.index]["trigger_word"] | |
new_placeholder = "Type a prompt! This style works for all prompts without 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}) ✨" | |
is_compatible = sdxl_loras[selected_state.index]["is_compatible"] | |
use_with_diffusers = f''' | |
## Using [`{lora_repo}`](https://huggingface.co/{lora_repo}) | |
## Use it with diffusers: | |
''' | |
if is_compatible: | |
use_with_diffusers += f''' | |
```python | |
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_weight = 0.5 | |
image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, cross_attention_kwargs={{"scale": lora_weight}}).images[0] | |
image.save("image.png") | |
``` | |
''' | |
else: | |
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 )" | |
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/) | |
''' | |
return ( | |
updated_text, | |
instance_prompt, | |
gr.update(placeholder=new_placeholder), | |
selected_state, | |
use_with_diffusers, | |
use_with_uis, | |
) | |
def check_selected(selected_state): | |
if not selected_state: | |
raise gr.Error("You must select a LoRA") | |
def merge_incompatible_lora(full_path_lora, lora_scale): | |
for weights_file in [full_path_lora]: | |
if ";" in weights_file: | |
weights_file, multiplier = weights_file.split(";") | |
multiplier = float(multiplier) | |
else: | |
multiplier = lora_scale | |
lora_model, weights_sd = lora.create_network_from_weights( | |
multiplier, | |
full_path_lora, | |
pipe.vae, | |
pipe.text_encoder, | |
pipe.unet, | |
for_inference=True, | |
) | |
lora_model.merge_to( | |
pipe.text_encoder, pipe.unet, weights_sd, torch.float16, "cuda" | |
) | |
def run_lora(prompt, negative, lora_scale, selected_state): | |
global last_lora, last_merged, pipe | |
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 = saved_names[selected_state.index] | |
cross_attention_kwargs = None | |
if last_lora != repo_name: | |
if last_merged: | |
pipe = copy.deepcopy(original_pipe) | |
pipe.to(device) | |
else: | |
pipe.unload_lora_weights() | |
is_compatible = sdxl_loras[selected_state.index]["is_compatible"] | |
if is_compatible: | |
pipe.load_lora_weights(full_path_lora) | |
cross_attention_kwargs = {"scale": lora_scale} | |
else: | |
merge_incompatible_lora(full_path_lora, lora_scale) | |
last_merged = True | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative, | |
width=768, | |
height=768, | |
num_inference_steps=20, | |
guidance_scale=7.5, | |
cross_attention_kwargs=cross_attention_kwargs, | |
).images[0] | |
last_lora = repo_name | |
return image, gr.update(visible=True) | |
with gr.Blocks(css="custom.css") as demo: | |
title = gr.HTML( | |
"""<h1><img src="https://i.imgur.com/vT48NAO.png" alt="LoRA"> LoRA the Explorer</h1>""", | |
elem_id="title", | |
) | |
selected_state = gr.State() | |
with gr.Row(): | |
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 | |
) | |
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") | |
result = gr.Image( | |
interactive=False, label="Generated Image", elem_id="result-image" | |
) | |
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") | |
with gr.Accordion("Advanced options", open=False): | |
negative = gr.Textbox(label="Negative Prompt") | |
weight = gr.Slider(0, 10, value=1, step=0.1, label="LoRA weight") | |
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.Box(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." | |
) | |
gallery.select( | |
update_selection, | |
outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis], | |
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, selected_state], | |
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, selected_state], | |
outputs=[result, share_group], | |
) | |
share_button.click(None, [], [], _js=share_js) | |
demo.queue(max_size=20) | |
demo.launch() | |