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import os
import time
from typing import Union
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline
from modules.shared import log, opts, listdir
from modules import errors, sd_models
from modules.control.units.xs_model import ControlNetXSModel
from modules.control.units.xs_pipe import StableDiffusionControlNetXSPipeline, StableDiffusionXLControlNetXSPipeline
from modules.control.units import detect
what = 'ControlNet-XS'
debug = log.trace if os.environ.get('SD_CONTROL_DEBUG', None) is not None else lambda *args, **kwargs: None
debug('Trace: CONTROL')
predefined_sd15 = {
}
predefined_sdxl = {
'Canny': 'UmerHA/ConrolNetXS-SDXL-canny',
'Depth': 'UmerHA/ConrolNetXS-SDXL-depth',
}
models = {}
all_models = {}
all_models.update(predefined_sd15)
all_models.update(predefined_sdxl)
cache_dir = 'models/control/xs'
def find_models():
path = os.path.join(opts.control_dir, 'xs')
files = listdir(path)
files = [f for f in files if f.endswith('.safetensors')]
downloaded_models = {}
for f in files:
basename = os.path.splitext(os.path.relpath(f, path))[0]
downloaded_models[basename] = os.path.join(path, f)
all_models.update(downloaded_models)
return downloaded_models
def list_models(refresh=False):
global models # pylint: disable=global-statement
import modules.shared
if not refresh and len(models) > 0:
return models
models = {}
if modules.shared.sd_model_type == 'none':
models = ['None']
elif modules.shared.sd_model_type == 'sdxl':
models = ['None'] + sorted(predefined_sdxl) + sorted(find_models())
elif modules.shared.sd_model_type == 'sd':
models = ['None'] + sorted(predefined_sd15) + sorted(find_models())
else:
log.error(f'Control {what} model list failed: unknown model type')
models = ['None'] + sorted(predefined_sd15) + sorted(predefined_sdxl) + sorted(find_models())
debug(f'Control list {what}: path={cache_dir} models={models}')
return models
class ControlNetXS():
def __init__(self, model_id: str = None, device = None, dtype = None, load_config = None):
self.model: ControlNetXSModel = None
self.model_id: str = model_id
self.device = device
self.dtype = dtype
self.load_config = { 'cache_dir': cache_dir, 'learn_embedding': True }
if load_config is not None:
self.load_config.update(load_config)
if model_id is not None:
self.load()
def reset(self):
if self.model is not None:
debug(f'Control {what} model unloaded')
self.model = None
self.model_id = None
def load(self, model_id: str = None, time_embedding_mix: float = 0.0) -> str:
try:
t0 = time.time()
model_id = model_id or self.model_id
if model_id is None or model_id == 'None':
self.reset()
return
model_path = all_models[model_id]
if model_path == '':
return
if model_path is None:
log.error(f'Control {what} model load failed: id="{model_id}" error=unknown model id')
return
self.load_config['time_embedding_mix'] = time_embedding_mix
log.debug(f'Control {what} model loading: id="{model_id}" path="{model_path}" {self.load_config}')
if model_path.endswith('.safetensors'):
self.model = ControlNetXSModel.from_single_file(model_path, **self.load_config)
else:
self.model = ControlNetXSModel.from_pretrained(model_path, **self.load_config)
if self.device is not None:
self.model.to(self.device)
if self.dtype is not None:
self.model.to(self.dtype)
t1 = time.time()
self.model_id = model_id
log.debug(f'Control {what} model loaded: id="{model_id}" path="{model_path}" time={t1-t0:.2f}')
return f'{what} loaded model: {model_id}'
except Exception as e:
log.error(f'Control {what} model load failed: id="{model_id}" error={e}')
errors.display(e, f'Control {what} load')
return f'{what} failed to load model: {model_id}'
class ControlNetXSPipeline():
def __init__(self, controlnet: Union[ControlNetXSModel, list[ControlNetXSModel]], pipeline: Union[StableDiffusionXLPipeline, StableDiffusionPipeline], dtype = None):
t0 = time.time()
self.orig_pipeline = pipeline
self.pipeline = None
if pipeline is None:
log.error(f'Control {what} pipeline: model not loaded')
return
if detect.is_sdxl(pipeline):
self.pipeline = StableDiffusionXLControlNetXSPipeline(
vae=pipeline.vae,
text_encoder=pipeline.text_encoder,
text_encoder_2=pipeline.text_encoder_2,
tokenizer=pipeline.tokenizer,
tokenizer_2=pipeline.tokenizer_2,
unet=pipeline.unet,
scheduler=pipeline.scheduler,
# feature_extractor=getattr(pipeline, 'feature_extractor', None),
controlnet=controlnet, # can be a list
)
sd_models.move_model(self.pipeline, pipeline.device)
elif detect.is_sd15(pipeline):
self.pipeline = StableDiffusionControlNetXSPipeline(
vae=pipeline.vae,
text_encoder=pipeline.text_encoder,
tokenizer=pipeline.tokenizer,
unet=pipeline.unet,
scheduler=pipeline.scheduler,
feature_extractor=getattr(pipeline, 'feature_extractor', None),
requires_safety_checker=False,
safety_checker=None,
controlnet=controlnet, # can be a list
)
sd_models.move_model(self.pipeline, pipeline.device)
else:
log.error(f'Control {what} pipeline: class={pipeline.__class__.__name__} unsupported model type')
return
if dtype is not None and self.pipeline is not None:
self.pipeline = self.pipeline.to(dtype)
t1 = time.time()
if self.pipeline is not None:
log.debug(f'Control {what} pipeline: class={self.pipeline.__class__.__name__} time={t1-t0:.2f}')
else:
log.error(f'Control {what} pipeline: not initialized')
def restore(self):
self.pipeline = None
return self.orig_pipeline
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