Spaces:
Paused
Paused
File size: 9,044 Bytes
b888bcf 6a4b741 5715833 6a4b741 b888bcf 6a4b741 b888bcf 6a4b741 5715833 b888bcf b626f76 6a4b741 f1f7ebd 75a89e7 6a4b741 b888bcf 5715833 6a4b741 b626f76 5715833 b888bcf 6a4b741 b888bcf 6a4b741 46b8e8d 6a4b741 b888bcf 6a4b741 b888bcf 6a4b741 5715833 6a4b741 b888bcf 41a4356 6a4b741 b888bcf 6a4b741 b888bcf 5715833 b888bcf 6a4b741 5715833 b888bcf 5715833 6a4b741 b888bcf 6a4b741 b888bcf 5715833 b888bcf 6a4b741 5715833 6a4b741 5715833 b888bcf 5715833 b888bcf 5715833 b888bcf 5715833 b888bcf 5715833 b888bcf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
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()
|