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handle huggingface model variant fallback

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This commit is contained in:
Vladimir Mandic
2024-02-06 18:08:14 -05:00
parent ad69dcc7e6
commit 593dae4e24
6 changed files with 28 additions and 17 deletions
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modules/control/run.py
+1 -1
View File
@@ -365,7 +365,7 @@ def control_run(units: List[unit.Unit], inputs, inits, mask, unit_type: str, is_
)
if processed_image is not None:
processed_images.append(processed_image)
if shared.opts.control_unload_processor:
if shared.opts.control_unload_processor and process.processor_id is not None:
processors.config[process.processor_id]['dirty'] = True # to force reload
process.model = None
modules/sd_models.py
+19 -4
View File
@@ -38,6 +38,7 @@ sd_metadata = None
sd_metadata_pending = 0
sd_metadata_timer = 0
debug_move = shared.log.trace if os.environ.get('SD_MOVE_DEBUG', None) is not None else lambda *args, **kwargs: None
debug_load = os.environ.get('SD_LOAD_DEBUG', None)
class CheckpointInfo:
@@ -747,10 +748,12 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
diffusers_load_config = {
"low_cpu_mem_usage": True,
"torch_dtype": devices.dtype,
"load_connected_pipeline": True,
# sd15 specific but we cant know ahead of time
"safety_checker": None,
"requires_safety_checker": False,
"load_safety_checker": False,
"load_connected_pipeline": True,
# "use_safetensors": True,
}
if shared.opts.diffusers_model_load_variant != 'default':
diffusers_load_config['variant'] = shared.opts.diffusers_model_load_variant
@@ -770,7 +773,6 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
return
sd_model = None
try:
if shared.cmd_opts.ckpt is not None and os.path.isdir(shared.cmd_opts.ckpt) and model_data.initial: # initial load
ckpt_basename = os.path.basename(shared.cmd_opts.ckpt)
@@ -783,6 +785,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
sd_model = diffusers.DiffusionPipeline.from_pretrained(model_file, **diffusers_load_config)
except Exception as e:
shared.log.error(f'Failed loading model: {model_file} {e}')
errors.display(e, f'Load model: {model_file}')
list_models() # rescan for downloaded model
checkpoint_info = CheckpointInfo(model_name)
@@ -802,6 +805,9 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
shared.log.debug(f'Diffusers loading: path="{checkpoint_info.path}"')
pipeline, model_type = detect_pipeline(checkpoint_info.path, op)
if os.path.isdir(checkpoint_info.path):
files = shared.walk_files(checkpoint_info.path, ['.safetensors', '.bin', '.ckpt'])
if 'variant' not in diffusers_load_config and any('diffusion_pytorch_model.fp16' in f for f in files): # deal with diffusers lack of variant fallback when loading
diffusers_load_config['variant'] = 'fp16'
if model_type in ['InstaFlow']: # forced pipeline
try:
pipeline = diffusers.utils.get_class_from_dynamic_module('instaflow_one_step', module_file='pipeline.py')
@@ -821,11 +827,16 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
sd_model = pipeline.from_pretrained(checkpoint_info.path)
else:
err1, err2, err3 = None, None, None
# diffusers_load_config['use_safetensors'] = True
if debug_load:
shared.log.debug(f'Diffusers load args: {diffusers_load_config}')
try: # 1 - autopipeline, best choice but not all pipelines are available
sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
sd_model.model_type = sd_model.__class__.__name__
except Exception as e:
err1 = e
if debug_load:
errors.display(e, 'Load AutoPipeline')
# shared.log.error(f'AutoPipeline: {e}')
try: # 2 - diffusion pipeline, works for most non-linked pipelines
if err1 is not None:
@@ -833,14 +844,18 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
sd_model.model_type = sd_model.__class__.__name__
except Exception as e:
err2 = e
if debug_load:
errors.display(e, "Load DiffusionPipeline")
# shared.log.error(f'DiffusionPipeline: {e}')
try: # 3 - try basic pipeline just in case
if err2 is not None:
sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
sd_model.model_type = sd_model.__class__.__name__
except Exception as e:
err3 = e # ignore last error
shared.log.error(f'StableDiffusionPipeline: {e}')
err3 = e # ignore last error
shared.log.error(f"StableDiffusionPipeline: {e}")
if debug_load:
errors.display(e, "Load StableDiffusionPipeline")
if err3 is not None:
shared.log.error(f'Failed loading {op}: {checkpoint_info.path} auto={err1} diffusion={err2}')
return
modules/sd_samplers_diffusers.py
+3 -7
View File
@@ -24,6 +24,7 @@ try:
LMSDiscreteScheduler,
PNDMScheduler,
LCMScheduler,
SASolverScheduler,
)
except Exception as e:
import diffusers
@@ -48,6 +49,7 @@ config = {
'LMSD': { 'use_karras_sigmas': False, 'timestep_spacing': 'linspace', 'steps_offset': 0 },
'PNDM': { 'skip_prk_steps': False, 'set_alpha_to_one': False, 'steps_offset': 0 },
'LCM': { 'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': "scaled_linear", 'set_alpha_to_one': True, 'rescale_betas_zero_snr': False, 'thresholding': False },
'SA Solver': {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'timestep_spacing': 'linspace'},
}
samplers_data_diffusers = [
@@ -67,15 +69,9 @@ samplers_data_diffusers = [
sd_samplers_common.SamplerData('Euler a', lambda model: DiffusionSampler('Euler a', EulerAncestralDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('Heun', lambda model: DiffusionSampler('Heun', HeunDiscreteScheduler, model), [], {}),
sd_samplers_common.SamplerData('LCM', lambda model: DiffusionSampler('LCM', LCMScheduler, model), [], {}),
sd_samplers_common.SamplerData('SA Solver', lambda model: DiffusionSampler('SA Solver', SASolverScheduler, model), [], {}),
]
try:
from diffusers import SASolverScheduler
config['SA Solver'] = {'predictor_order': 2, 'corrector_order': 2, 'thresholding': False, 'lower_order_final': True, 'use_karras_sigmas': False, 'timestep_spacing': 'linspace'}
samplers_data_diffusers.append(sd_samplers_common.SamplerData('SA Solver', lambda model: DiffusionSampler('SA Solver', SASolverScheduler, model), [], {}))
except Exception as e:
shared.log.debug(f'Sampler: {e}')
class DiffusionSampler:
def __init__(self, name, constructor, model, **kwargs):