File size: 20,963 Bytes
171f557 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 | import importlib, inspect, pkgutil, traceback, torch, os, re, typing, io
from typing import Union, List, Optional, Tuple, Iterable, Dict, Literal
from contextlib import contextmanager
from diffsynth.utils.data import VideoData
import streamlit as st
from diffsynth import ModelConfig
from diffsynth.diffusion.base_pipeline import ControlNetInput
from PIL import Image
from tqdm import tqdm
st.set_page_config(layout="wide")
class StreamlitTqdmWrapper:
"""Wrapper class that combines tqdm and streamlit progress bar"""
def __init__(self, iterable, st_progress_bar=None):
self.iterable = iterable
self.st_progress_bar = st_progress_bar
self.tqdm_bar = tqdm(iterable)
self.total = len(iterable) if hasattr(iterable, '__len__') else None
self.current = 0
def __iter__(self):
for item in self.tqdm_bar:
if self.st_progress_bar is not None and self.total is not None:
self.current += 1
self.st_progress_bar.progress(self.current / self.total)
yield item
def __enter__(self):
return self
def __exit__(self, *args):
if hasattr(self.tqdm_bar, '__exit__'):
self.tqdm_bar.__exit__(*args)
@contextmanager
def catch_error(error_value):
try:
yield
except Exception as e:
error_message = traceback.format_exc()
print(f"Error {error_value}:\n{error_message}")
def parse_vram_config_from_an_example(path):
vram_config = {
"offload_dtype": None,
"offload_device": None,
"onload_dtype": None,
"onload_device": None,
"preparing_dtype": None,
"preparing_device": None,
"computation_dtype": None,
"computation_device": None,
}
with open(path, "r") as f:
for code in f.readlines():
code = code.strip()
for param in vram_config:
if vram_config[param] is None and f'"{param}":' in code:
value = code.split(" ")[-1].replace(",", "").replace('"', "").replace("torch.", "")
vram_config[param] = value
return vram_config
def parse_model_configs_from_an_example(path):
model_configs = []
vram_config = parse_vram_config_from_an_example(path)
with open(path, "r") as f:
for code in f.readlines():
code = code.strip()
if not code.startswith("ModelConfig"):
continue
pairs = re.findall(r'(\w+)\s*=\s*["\']([^"\']+)["\']', code)
config_dict = {k: v for k, v in pairs}
vram_config_ = vram_config if "**vram_config" in code else {}
model_configs.append(ModelConfig(model_id=config_dict["model_id"], origin_file_pattern=config_dict["origin_file_pattern"], **vram_config_))
return model_configs
def list_examples(path, keyword=None):
examples = []
if os.path.isdir(path):
for file_name in os.listdir(path):
examples.extend(list_examples(os.path.join(path, file_name), keyword=keyword))
elif path.endswith(".py"):
with open(path, "r") as f:
code = f.read()
if keyword is None or keyword in code:
examples.extend([path])
return examples
def parse_available_pipelines():
from diffsynth.diffusion.base_pipeline import BasePipeline
import diffsynth.pipelines as _pipelines_pkg
available_pipelines = {}
for _, name, _ in pkgutil.iter_modules(_pipelines_pkg.__path__):
with catch_error(f"Failed: import diffsynth.pipelines.{name}"):
mod = importlib.import_module(f"diffsynth.pipelines.{name}")
classes = {
cls_name: cls for cls_name, cls in inspect.getmembers(mod, inspect.isclass)
if issubclass(cls, BasePipeline) and cls is not BasePipeline and cls.__module__ == mod.__name__
}
available_pipelines.update(classes)
return available_pipelines
def parse_available_examples(path, available_pipelines):
available_examples = {}
for pipeline_name in available_pipelines:
examples = ["None"] + list_examples(path, keyword=f"{pipeline_name}.from_pretrained")
available_examples[pipeline_name] = examples
return available_examples
def draw_selectbox(label, options, option_map, value=None, disabled=False):
default_index = 0 if value is None else tuple(options).index([option for option in option_map if option_map[option]==value][0])
option = st.selectbox(label=label, options=tuple(options), index=default_index, disabled=disabled)
return option_map.get(option)
def parse_params(fn):
params = []
for name, param in inspect.signature(fn).parameters.items():
annotation = param.annotation if param.annotation is not inspect.Parameter.empty else None
default = param.default if param.default is not inspect.Parameter.empty else None
params.append({"name": name, "dtype": annotation, "value": default})
return params
def draw_vram_device(label, value=None, key_suffix="", disabled=False):
option_map = {"None": None, "disk": "disk", "cuda": "cuda", "cpu": "cpu"}
options = option_map.keys()
default_index = 0 if value is None else tuple(options).index(value)
option = st.selectbox(label=label, options=tuple(options), index=default_index, key=label + key_suffix, disabled=disabled)
return option_map.get(option)
def draw_vram_dtype(label, value=None, key_suffix="", disabled=False):
option_map = {"None": None, "disk": "disk", "bfloat16": torch.bfloat16, "float32": torch.float32, "float16": torch.float16, "float8_e4m3fn": torch.float8_e4m3fn, "float8_e5m2": torch.float8_e5m2}
options = option_map.keys()
default_index = 0 if value is None else tuple(options).index(value)
option = st.selectbox(label=label, options=tuple(options), index=default_index, key=label + key_suffix, disabled=disabled)
return option_map.get(option)
def draw_model_config(model_config=None, key_suffix="", disabled=False, enable_vram_config=False):
with st.container(border=True):
if model_config is None:
model_config = ModelConfig()
path = st.text_input(label="path", key="path" + key_suffix, value=model_config.path, disabled=disabled)
col1, col2 = st.columns(2)
with col1:
model_id = st.text_input(label="model_id", key="model_id" + key_suffix, value=model_config.model_id, disabled=disabled)
with col2:
origin_file_pattern = st.text_input(label="origin_file_pattern", key="origin_file_pattern" + key_suffix, value=model_config.origin_file_pattern, disabled=disabled)
if enable_vram_config:
with st.container(border=True):
col1, col2 = st.columns(2)
with col1:
offload_device = draw_vram_device(label="offload_device", value=model_config.offload_device, key_suffix=key_suffix, disabled=disabled)
onload_device = draw_vram_device(label="onload_device", value=model_config.onload_device, key_suffix=key_suffix, disabled=disabled)
preparing_device = draw_vram_device(label="preparing_device", value=model_config.preparing_device, key_suffix=key_suffix, disabled=disabled)
computation_device = draw_vram_device(label="computation_device", value=model_config.computation_device, key_suffix=key_suffix, disabled=disabled)
with col2:
offload_dtype = draw_vram_dtype(label="offload_dtype", value=model_config.offload_dtype, key_suffix=key_suffix, disabled=disabled)
onload_dtype = draw_vram_dtype(label="onload_dtype", value=model_config.onload_dtype, key_suffix=key_suffix, disabled=disabled)
preparing_dtype = draw_vram_dtype(label="preparing_dtype", value=model_config.preparing_dtype, key_suffix=key_suffix, disabled=disabled)
computation_dtype = draw_vram_dtype(label="computation_dtype", value=model_config.computation_dtype, key_suffix=key_suffix, disabled=disabled)
vram_config = {
"offload_device": offload_device,
"onload_device": onload_device,
"preparing_device": preparing_device,
"computation_device": computation_device,
"offload_dtype": offload_dtype,
"onload_dtype": onload_dtype,
"preparing_dtype": preparing_dtype,
"computation_dtype": computation_dtype,
}
else:
vram_config = {}
model_config = ModelConfig(
path=None if path == "" else path,
model_id=model_id,
origin_file_pattern=origin_file_pattern,
**vram_config,
)
return model_config
def draw_multi_model_config(name="", value=None, disabled=False, enable_vram_config=False):
model_configs = []
with st.container(border=True):
st.markdown(name)
num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
for i in range(num):
model_config = draw_model_config(key_suffix=f"_{name}_{i}", model_config=None if value is None else value[i], disabled=disabled, enable_vram_config=enable_vram_config)
model_configs.append(model_config)
return model_configs
def draw_single_model_config(name="", value=None, disabled=False):
with st.container(border=True):
st.markdown(name)
model_config = draw_model_config(value, key_suffix=f"_{name}", disabled=disabled)
return model_config
def draw_multi_images(name="", value=None, disabled=False):
images = []
with st.container(border=True):
st.markdown(name)
num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
for i in range(num):
image = st.file_uploader(name, type=["png", "jpg", "jpeg", "webp"], key=f"{name}_{i}", disabled=disabled)
if image is not None: images.append(Image.open(image))
return images
def draw_multi_elements(st_element, name="", value=None, disabled=False, kwargs=None):
if kwargs is None:
kwargs = {}
elements = []
with st.container(border=True):
st.markdown(name)
num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
for i in range(num):
element = st_element(name, key=f"{name}_{i}", disabled=disabled, value=None if value is None else value[i], **kwargs)
elements.append(element)
return elements
def draw_lora_configs(name="", value=None, disabled=False):
elements = []
with st.container(border=True):
st.markdown(name)
num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
for i in range(num):
with st.container(border=True):
lora_base_model = st.text_input(label="LoRA base model", key="LoRA base model" + f"LoRA_{i}")
lora_scale = st.slider(label="LoRA scale", min_value=-8.0, max_value=8.0, value=1.0, step=0.1, key="LoRA scale" + f"LoRA_{i}")
lora_config = draw_model_config(key_suffix=f"LoRA_{i}", disabled=disabled)
element = {"base_model": lora_base_model, "alpha": lora_scale, "lora_config": lora_config}
elements.append(element)
return elements
def draw_controlnet_input(name="", value=None, disabled=False):
with st.container(border=True):
st.markdown(name)
controlnet_id = st.number_input("controlnet_id", value=0, min_value=0, max_value=20, step=1, key=f"{name}_controlnet_id")
scale = st.number_input("scale", value=1.0, min_value=0.0, max_value=10.0, key=f"{name}_scale")
image = st.file_uploader("image", type=["png", "jpg", "jpeg", "webp"], disabled=disabled, key=f"{name}_image")
if image is not None: image = Image.open(image)
inpaint_image = st.file_uploader("inpaint_image", type=["png", "jpg", "jpeg", "webp"], disabled=disabled, key=f"{name}_inpaint_image")
if inpaint_image is not None: inpaint_image = Image.open(inpaint_image)
inpaint_mask = st.file_uploader("inpaint_mask", type=["png", "jpg", "jpeg", "webp"], disabled=disabled, key=f"{name}_inpaint_mask")
if inpaint_mask is not None: inpaint_mask = Image.open(inpaint_mask)
return ControlNetInput(controlnet_id=controlnet_id, scale=scale, image=image, inpaint_image=inpaint_image, inpaint_mask=inpaint_mask)
def draw_controlnet_inputs(name, value=None, disabled=False):
controlnet_inputs = []
with st.container(border=True):
st.markdown(name)
num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
for i in range(num):
controlnet_input = draw_controlnet_input(name=f"{name}_{i}", value=None, disabled=disabled)
controlnet_inputs.append(controlnet_input)
return controlnet_inputs
def draw_ui_element(name, dtype, value):
unsupported_dtype = [
Dict[str, torch.Tensor],
torch.Tensor,
]
if dtype in unsupported_dtype:
return
if value is None:
with st.container(border=True):
enable = st.checkbox(f"Enable {name}", value=False)
ui = draw_ui_element_safely(name, dtype, value=value, disabled=not enable)
if enable:
return ui
else:
return None
else:
return draw_ui_element_safely(name, dtype, value)
def draw_video(name, value=None, disabled=False):
ui = st.file_uploader(name, type=["mp4"], disabled=disabled)
if ui is not None:
ui = VideoData(ui)
ui = [ui[i] for i in range(len(ui))]
return ui
def draw_ui_element_safely(name, dtype, value, disabled=False):
if dtype == torch.dtype:
option_map = {"bfloat16": torch.bfloat16, "float32": torch.float32, "float16": torch.float16}
ui = draw_selectbox(name, option_map.keys(), option_map, value=value, disabled=disabled)
elif dtype == Union[str, torch.device]:
option_map = {"cuda": "cuda", "cpu": "cpu"}
ui = draw_selectbox(name, option_map.keys(), option_map, value=value, disabled=disabled)
elif dtype == bool:
ui = st.checkbox(name, value=value, disabled=disabled)
elif dtype == ModelConfig:
ui = draw_single_model_config(name, value=value, disabled=disabled)
elif dtype in [list[ModelConfig], List[ModelConfig], Union[list[ModelConfig], ModelConfig, str]]:
if name == "model_configs":
model_configs = st.session_state.get("model_configs_from_example")
ui = draw_multi_model_config(name, model_configs, disabled=disabled, enable_vram_config=True)
else:
ui = draw_multi_model_config(name, disabled=disabled)
elif dtype == str:
if "prompt" in name:
ui = st.text_area(name, value=value, height=3, disabled=disabled)
else:
ui = st.text_input(name, value=value, disabled=disabled)
elif dtype == float:
ui = st.number_input(name, value=value, disabled=disabled)
elif dtype == int:
ui = st.number_input(name, value=value, step=1, disabled=disabled)
elif dtype == Image.Image:
ui = st.file_uploader(name, type=["png", "jpg", "jpeg", "webp"], disabled=disabled)
if ui is not None: ui = Image.open(ui)
elif dtype in [List[Image.Image], list[Image.Image], Union[list[Image.Image], Image.Image], Union[List[Image.Image], Image.Image]]:
if "video" in name:
ui = draw_video(name, value=value, disabled=disabled)
else:
ui = draw_multi_images(name, value=value, disabled=disabled)
elif dtype in [List[ControlNetInput], list[ControlNetInput]]:
ui = draw_controlnet_inputs(name, value=value, disabled=disabled)
elif dtype in [List[str], list[str]]:
ui = draw_multi_elements(st.text_input, name, value=value, disabled=disabled)
elif dtype in [List[float], list[float], Union[list[float], float], Union[List[float], float]]:
ui = draw_multi_elements(st.number_input, name, value=value, disabled=disabled)
elif dtype in [List[int], list[int]]:
ui = draw_multi_elements(st.number_input, name, value=value, disabled=disabled, kwargs={"step": 1})
elif dtype in [List[List[Image.Image]], list[list[Image.Image]]]:
ui = draw_multi_elements(draw_video, name, value=value, disabled=disabled)
elif dtype in [tuple[int, int], Tuple[int, int]]:
with st.container(border=True):
st.markdown(name)
ui = (st.text_input(f"{name}_0", value=value[0], disabled=disabled), st.text_input(f"{name}_1", value=value[1], disabled=disabled))
elif isinstance(dtype, typing._LiteralGenericAlias):
with st.container(border=True):
st.markdown(f"{name} ({dtype})")
ui = st.text_input(name, value=value, disabled=disabled, label_visibility="hidden")
elif dtype is None:
if name == "progress_bar_cmd":
ui = value
else:
st.markdown(f"(`{name}` is not not configurable in WebUI). dtype: `{dtype}`.")
ui = value
return ui
def flush_example():
for key in list(st.session_state.keys()):
if key not in ["available_pipelines", "available_examples"]:
del st.session_state[key]
def launch_webui():
input_col, output_col = st.columns(2)
with input_col:
if "available_pipelines" not in st.session_state:
st.session_state["available_pipelines"] = parse_available_pipelines()
if "available_examples" not in st.session_state:
st.session_state["available_examples"] = parse_available_examples("./examples", st.session_state["available_pipelines"])
with st.expander("Pipeline", expanded=True):
pipeline_class = draw_selectbox("Pipeline Class", st.session_state["available_pipelines"].keys(), st.session_state["available_pipelines"], value=st.session_state["available_pipelines"]["ZImagePipeline"])
example = st.selectbox("Parse model configs from an example (optional)", st.session_state["available_examples"][pipeline_class.__name__], on_change=flush_example)
if st.button("Step 1: Parse Pipeline", type="primary"):
st.session_state["pipeline_class"] = pipeline_class
if example != "None":
st.session_state["model_configs_from_example"] = parse_model_configs_from_an_example(example)
if "pipeline_class" not in st.session_state:
return
with st.expander("Model", expanded=True):
input_params = {}
params = parse_params(pipeline_class.from_pretrained)
for param in params:
input_params[param["name"]] = draw_ui_element(**param)
lora_configs = draw_lora_configs(name="LoRA")
if st.button("Step 2: Load Models", type="primary"):
with st.spinner("Loading models", show_time=True):
if "pipe" in st.session_state:
del st.session_state["pipe"]
torch.cuda.empty_cache()
pipe = pipeline_class.from_pretrained(**input_params)
for lora_config in lora_configs:
pipe.load_lora(pipe.get_module(pipe, lora_config["base_model"]), lora_config=lora_config["lora_config"], alpha=lora_config["alpha"])
st.session_state["pipe"] = pipe
if "pipe" not in st.session_state:
return
with st.expander("Input", expanded=True):
pipe = st.session_state["pipe"]
input_params = {}
params = parse_params(pipeline_class.__call__)
for param in params:
if param["name"] in ["self"]:
continue
input_params[param["name"]] = draw_ui_element(**param)
with output_col:
if st.button("Step 3: Generate", type="primary"):
if "progress_bar_cmd" in input_params:
input_params["progress_bar_cmd"] = lambda iterable: StreamlitTqdmWrapper(iterable, st.progress(0))
result = pipe(**input_params)
st.session_state["result"] = result
if "result" in st.session_state:
result = st.session_state["result"]
if isinstance(result, Image.Image):
st.image(result)
buf = io.BytesIO()
result.save(buf, format='PNG')
st.download_button(label="Download", data=buf.getvalue(), file_name="image.png", mime="image/png", type="primary")
else:
print(f"unsupported result format: {result}")
launch_webui()
|