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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:
sdxl_loras = [
(
item["image"],
item["title"],
item["repo"],
item["trigger_word"],
item["weights"],
item["is_compatible"],
)
for item in json.load(file)
]
saved_names = [
hf_hub_download(repo_id, filename) for _, _, repo_id, _, filename, _ 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][2]
instance_prompt = sdxl_loras[selected_state.index][3]
weight_name = sdxl_loras[selected_state.index][4]
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co./{lora_repo})"
use_with_diffusers = f"""
## Using [`{lora_repo}`](https://huggingface.co./{lora_repo})
## Use it with diffusers:
```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")
```
"""
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,
selected_state,
use_with_diffusers,
use_with_uis,
)
def run_lora(prompt, negative, weight, 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][2]
weight_name = sdxl_loras[selected_state.index][4]
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][5]
if is_compatible:
pipe.load_lora_weights(full_path_lora)
cross_attention_kwargs = {"scale": weight}
else:
for weights_file in [full_path_lora]:
if ";" in weights_file:
weights_file, multiplier = weights_file.split(";")
multiplier = float(weight)
else:
multiplier = 1.0
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"
)
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=[(a, b) for a, b, _, _, _, _ in sdxl_loras],
label="SDXL LoRA Gallery",
allow_preview=False,
columns=3,
elem_id="gallery",
)
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", 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, selected_state, use_diffusers, use_uis],
queue=False,
show_progress=False,
)
prompt.submit(
fn=run_lora,
inputs=[prompt, negative, weight, selected_state],
outputs=[result, share_group],
)
button.click(
fn=run_lora,
inputs=[prompt, negative, weight, selected_state],
outputs=[result, share_group],
)
share_button.click(None, [], [], _js=share_js)
demo.launch()
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