File size: 11,530 Bytes
ef88f9e 64b94c8 ef88f9e 64b94c8 ef88f9e bd9365d ef88f9e c85dfe4 ef88f9e |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 |
import os
import sys
import gradio as gr
import numpy as np
import shutil
import copy
import json
import gc
import random
from PIL import Image
'''
models
images
custom.css
sd_cfg.json
'''
'''
if not os.path.exists("sd-ggml-cpp-dp"):
os.system("git clone https://huggingface.co/svjack/sd-ggml-cpp-dp")
else:
shutil.rmtree("sd-ggml-cpp-dp")
os.system("git clone https://huggingface.co/svjack/sd-ggml-cpp-dp")
assert os.path.exists("sd-ggml-cpp-dp")
os.chdir("sd-ggml-cpp-dp")
'''
os.system("pip install huggingface_hub")
#### https://huggingface.co/svjack/sd-ggml-cpp-dp/resolve/main/models/Cyberpunk_Anime_Diffusion-ggml-model_q4_0.bin
def make_and_download_clean_dir(repo_name = "svjack/sd-ggml",
rp_tgt_tail_dict = {
"models": "wget https://huggingface.co/{}/resolve/main/{}/{}"
}
):
import shutil
import os
from tqdm import tqdm
from huggingface_hub import HfFileSystem
fs = HfFileSystem()
req_dir = repo_name.split("/")[-1]
if os.path.exists(req_dir):
shutil.rmtree(req_dir)
os.mkdir(req_dir)
os.chdir(req_dir)
fd_list = fs.ls(repo_name, detail = False)
fd_clean_list = list(filter(lambda x: not x.split("/")[-1].startswith("."), fd_list))
for path in tqdm(fd_clean_list):
src = path
tgt = src.split("/")[-1]
print("downloading {} to {}".format(src, tgt))
if tgt not in rp_tgt_tail_dict:
fs.download(
src, tgt, recursive = True
)
else:
tgt_cmd_format = rp_tgt_tail_dict[tgt]
os.mkdir(tgt)
os.chdir(tgt)
sub_fd_list = fs.ls(src, detail = False)
for sub_file in tqdm(sub_fd_list):
tgt_cmd = tgt_cmd_format.format(
repo_name, tgt, sub_file.split("/")[-1]
)
print("run {}".format(tgt_cmd))
os.system(tgt_cmd)
os.chdir("..")
os.chdir("..")
make_and_download_clean_dir("svjack/sd-ggml")
os.chdir("sd-ggml")
assert os.path.exists("stable-diffusion.cpp")
os.system("cmake stable-diffusion.cpp")
os.system("cmake --build . --config Release")
assert os.path.exists("bin")
def process(model_path ,prompt, num_samples, image_resolution, sample_steps, seed,):
from PIL import Image
from uuid import uuid1
output_path = "output_image_dir"
if not os.path.exists(output_path):
os.mkdir(output_path)
else:
shutil.rmtree(output_path)
os.mkdir(output_path)
assert os.path.exists(output_path)
run_format = './bin/sd -m {} --sampling-method "dpm++2mv2" -o "{}/{}.png" -p "{}" --steps {} -H {} -W {} -s {}'
images = []
for i in range(num_samples):
uid = str(uuid1())
run_cmd = run_format.format(model_path, output_path,
uid, prompt, sample_steps, image_resolution,
image_resolution, seed + i)
print("run cmd: {}".format(run_cmd))
os.system(run_cmd)
assert os.path.exists(os.path.join(output_path, "{}.png".format(uid)))
image = Image.open(os.path.join(output_path, "{}.png".format(uid)))
images.append(np.asarray(image))
results = images
return results
model_list = list(map(lambda x: os.path.join("models", x), os.listdir("models")))
assert model_list
sdxl_loras_raw = []
with open("sd_cfg.json", "r") as file:
data = json.load(file)
sdxl_loras_raw = [
{
"image": item["image"],
"title": item["title"],
"repo": item["repo"],
"trigger_word": item["trigger_word"],
"model_path": item["model_path"]
#"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)
}
for item in data
]
sdxl_loras_raw = list(filter(lambda d: d["model_path"] in model_list, sdxl_loras_raw))
assert sdxl_loras_raw
def update_selection(selected_state: gr.SelectData, sdxl_loras):
lora_repo = sdxl_loras[selected_state.index]["repo"]
instance_prompt = sdxl_loras[selected_state.index]["trigger_word"]
new_placeholder = "Type a prompt. This applies for all prompts, no need for a trigger word" if instance_prompt == "" else "Type a prompt to use your selected model"
#weight_name = sdxl_loras[selected_state.index]["weights"]
updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨ "
is_compatible = True
is_pivotal = True
use_with_diffusers = f'''
## Using [`{lora_repo}`](https://huggingface.co/{lora_repo})
## Use it 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})
- [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 Model")
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)
sorted_gallery = sorted(sdxl_loras, key=lambda x: x["title"], reverse=False)
return [(item["image"], item["title"]) for item in sorted_gallery], sorted_gallery
'''
def run_lora(prompt, negative, lora_scale, selected_state, sdxl_loras,
progress=gr.Progress(track_tqdm=True)):
'''
def run_lora(prompt, selected_state, sdxl_loras,
image_resolution, sample_steps, seed,
progress=gr.Progress(track_tqdm=True)):
#global last_lora, last_merged, last_fused, pipe
'''
if negative == "":
negative = None
'''
if not selected_state:
raise gr.Error("You must select a Model")
repo_name = sdxl_loras[selected_state.index]["repo"]
model_path = sdxl_loras[selected_state.index]["model_path"]
#weight_name = sdxl_loras[selected_state.index]["weights"]
'''
image = pipe(
prompt=prompt,
negative_prompt=negative,
width=1024,
height=1024,
num_inference_steps=20,
guidance_scale=7.5,
).images[0]
last_lora = repo_name
gc.collect()
'''
num_samples = 1
#### image_resolution : 512
#### sample_steps : 8
#### seed : 20
image = process(model_path ,prompt, num_samples, image_resolution, sample_steps, seed,)[0]
image = Image.fromarray(image.astype(np.uint8))
#return image, gr.update(visible=True)
return image
with gr.Blocks(css="custom.css") as demo:
#with gr.Blocks() as demo:
gr_sdxl_loras = gr.State(value=sdxl_loras_raw)
title = gr.HTML(
"""<h1><img src="https://i.imgur.com/vT48NAO.png" alt="SD"> StableDiffusion GGML Explorer</h1>""",
elem_id="title",
)
selected_state = gr.State()
with gr.Row(elem_id="main_app"):
with gr.Box(elem_id="gallery_box"):
order_gallery = gr.Radio(choices=["random", "alphabetical"],
value="random", label="Order by", elem_id="order_radio")
gallery = gr.Gallery(
#value=[(item["image"], item["title"]) for item in sdxl_loras_raw],
label="SD Model Gallery",
allow_preview=True,
#rows = 1,
columns=3,
#scale = 3,
min_width = 256,
#object_fit = "scale-down",
elem_id="gallery",
show_share_button=False,
height=512
)
with gr.Column():
prompt_title = gr.Markdown(
value="### Click on a Model in the gallery to select it",
visible=True,
elem_id="selected_model",
)
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1,
placeholder="Type a prompt after selecting a Model", 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")
#weight = gr.Slider(0, 10, value=0.8, step=0.1, label="LoRA weight")
#negative = ""
image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=256)
sample_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=8, step=1)
seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
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],
outputs=[prompt_title, prompt, prompt, selected_state,],
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, gr_sdxl_loras],
inputs=[prompt, selected_state, gr_sdxl_loras, image_resolution, sample_steps, seed],
#outputs=[result, share_group],
#outputs=[result,],
outputs = result
)
button.click(
fn=check_selected,
inputs=[selected_state],
queue=False,
show_progress=False
).success(
fn=run_lora,
#inputs=[prompt, negative, weight, selected_state, gr_sdxl_loras],
inputs=[prompt, selected_state, gr_sdxl_loras, image_resolution, sample_steps, seed],
#outputs=[result, share_group],
#outputs=[result,],
outputs = result
)
#share_button.click(None, [], [], _js=share_js)
demo.load(fn=shuffle_gallery, inputs=[gr_sdxl_loras], outputs=[gallery, gr_sdxl_loras], queue=False)
demo.queue(max_size=20)
demo.launch()
|