import os
import torch
import logging
import importlib
import backend.args
import huggingface_guess
from diffusers import DiffusionPipeline
from transformers import modeling_utils
from backend import memory_management
from backend.utils import read_arbitrary_config, load_torch_file, beautiful_print_gguf_state_dict_statics
from backend.state_dict import try_filter_state_dict, load_state_dict
from backend.operations import using_forge_operations
from backend.nn.vae import IntegratedAutoencoderKL
from backend.nn.clip import IntegratedCLIP
from backend.nn.unet import IntegratedUNet2DConditionModel
from backend.diffusion_engine.sd15 import StableDiffusion
from backend.diffusion_engine.sd20 import StableDiffusion2
from backend.diffusion_engine.sdxl import StableDiffusionXL
from backend.diffusion_engine.flux import Flux
possible_models = [StableDiffusion, StableDiffusion2, StableDiffusionXL, Flux]
logging.getLogger("diffusers").setLevel(logging.ERROR)
dir_path = os.path.dirname(__file__)
def load_huggingface_component(guess, component_name, lib_name, cls_name, repo_path, state_dict):
config_path = os.path.join(repo_path, component_name)
if component_name in ['feature_extractor', 'safety_checker']:
return None
if lib_name in ['transformers', 'diffusers']:
if component_name in ['scheduler']:
cls = getattr(importlib.import_module(lib_name), cls_name)
return cls.from_pretrained(os.path.join(repo_path, component_name))
if component_name.startswith('tokenizer'):
cls = getattr(importlib.import_module(lib_name), cls_name)
comp = cls.from_pretrained(os.path.join(repo_path, component_name))
comp._eventual_warn_about_too_long_sequence = lambda *args, **kwargs: None
return comp
if cls_name in ['AutoencoderKL']:
assert isinstance(state_dict, dict) and len(state_dict) > 16, 'You do not have VAE state dict!'
config = IntegratedAutoencoderKL.load_config(config_path)
with using_forge_operations(device=memory_management.cpu, dtype=memory_management.vae_dtype()):
model = IntegratedAutoencoderKL.from_config(config)
if 'decoder.up_blocks.0.resnets.0.norm1.weight' in state_dict.keys(): #diffusers format
state_dict = huggingface_guess.diffusers_convert.convert_vae_state_dict(state_dict)
load_state_dict(model, state_dict, ignore_start='loss.')
return model
if component_name.startswith('text_encoder') and cls_name in ['CLIPTextModel', 'CLIPTextModelWithProjection']:
assert isinstance(state_dict, dict) and len(state_dict) > 16, 'You do not have CLIP state dict!'
from transformers import CLIPTextConfig, CLIPTextModel
config = CLIPTextConfig.from_pretrained(config_path)
to_args = dict(device=memory_management.cpu, dtype=memory_management.text_encoder_dtype())
with modeling_utils.no_init_weights():
with using_forge_operations(**to_args, manual_cast_enabled=True):
model = IntegratedCLIP(CLIPTextModel, config, add_text_projection=True).to(**to_args)
load_state_dict(model, state_dict, ignore_errors=[
'transformer.text_projection.weight',
'transformer.text_model.embeddings.position_ids',
'logit_scale'
], log_name=cls_name)
return model
if cls_name == 'T5EncoderModel':
assert isinstance(state_dict, dict) and len(state_dict) > 16, 'You do not have T5 state dict!'
from backend.nn.t5 import IntegratedT5
config = read_arbitrary_config(config_path)
storage_dtype = memory_management.text_encoder_dtype()
state_dict_dtype = memory_management.state_dict_dtype(state_dict)
if state_dict_dtype in [torch.float8_e4m3fn, torch.float8_e5m2, 'nf4', 'fp4', 'gguf']:
print(f'Using Detected T5 Data Type: {state_dict_dtype}')
storage_dtype = state_dict_dtype
if state_dict_dtype in ['nf4', 'fp4', 'gguf']:
print(f'Using pre-quant state dict!')
if state_dict_dtype in ['gguf']:
beautiful_print_gguf_state_dict_statics(state_dict)
else:
print(f'Using Default T5 Data Type: {storage_dtype}')
if storage_dtype in ['nf4', 'fp4', 'gguf']:
with modeling_utils.no_init_weights():
with using_forge_operations(device=memory_management.cpu, dtype=memory_management.text_encoder_dtype(), manual_cast_enabled=False, bnb_dtype=storage_dtype):
model = IntegratedT5(config)
else:
with modeling_utils.no_init_weights():
with using_forge_operations(device=memory_management.cpu, dtype=storage_dtype, manual_cast_enabled=True):
model = IntegratedT5(config)
load_state_dict(model, state_dict, log_name=cls_name, ignore_errors=['transformer.encoder.embed_tokens.weight', 'logit_scale'])
return model
if cls_name in ['UNet2DConditionModel', 'FluxTransformer2DModel']:
assert isinstance(state_dict, dict) and len(state_dict) > 16, 'You do not have model state dict!'
model_loader = None
if cls_name == 'UNet2DConditionModel':
model_loader = lambda c: IntegratedUNet2DConditionModel.from_config(c)
if cls_name == 'FluxTransformer2DModel':
from backend.nn.flux import IntegratedFluxTransformer2DModel
model_loader = lambda c: IntegratedFluxTransformer2DModel(**c)
unet_config = guess.unet_config.copy()
state_dict_parameters = memory_management.state_dict_parameters(state_dict)
state_dict_dtype = memory_management.state_dict_dtype(state_dict)
storage_dtype = memory_management.unet_dtype(model_params=state_dict_parameters, supported_dtypes=guess.supported_inference_dtypes)
unet_storage_dtype_overwrite = backend.args.dynamic_args.get('forge_unet_storage_dtype')
if unet_storage_dtype_overwrite is not None:
storage_dtype = unet_storage_dtype_overwrite
elif state_dict_dtype in [torch.float8_e4m3fn, torch.float8_e5m2, 'nf4', 'fp4', 'gguf']:
print(f'Using Detected UNet Type: {state_dict_dtype}')
storage_dtype = state_dict_dtype
if state_dict_dtype in ['nf4', 'fp4', 'gguf']:
print(f'Using pre-quant state dict!')
if state_dict_dtype in ['gguf']:
beautiful_print_gguf_state_dict_statics(state_dict)
load_device = memory_management.get_torch_device()
computation_dtype = memory_management.get_computation_dtype(load_device, parameters=state_dict_parameters, supported_dtypes=guess.supported_inference_dtypes)
offload_device = memory_management.unet_offload_device()
if storage_dtype in ['nf4', 'fp4', 'gguf']:
initial_device = memory_management.unet_inital_load_device(parameters=state_dict_parameters, dtype=computation_dtype)
with using_forge_operations(device=initial_device, dtype=computation_dtype, manual_cast_enabled=False, bnb_dtype=storage_dtype):
model = model_loader(unet_config)
else:
initial_device = memory_management.unet_inital_load_device(parameters=state_dict_parameters, dtype=storage_dtype)
need_manual_cast = storage_dtype != computation_dtype
to_args = dict(device=initial_device, dtype=storage_dtype)
with using_forge_operations(**to_args, manual_cast_enabled=need_manual_cast):
model = model_loader(unet_config).to(**to_args)
load_state_dict(model, state_dict)
if hasattr(model, '_internal_dict'):
model._internal_dict = unet_config
else:
model.config = unet_config
model.storage_dtype = storage_dtype
model.computation_dtype = computation_dtype
model.load_device = load_device
model.initial_device = initial_device
model.offload_device = offload_device
return model
print(f'Skipped: {component_name} = {lib_name}.{cls_name}')
return None
def replace_state_dict(sd, asd, guess):
vae_key_prefix = guess.vae_key_prefix[0]
text_encoder_key_prefix = guess.text_encoder_key_prefix[0]
if 'enc.blk.0.attn_k.weight' in asd:
wierd_t5_format_from_city96 = {
"enc.": "encoder.",
".blk.": ".block.",
"token_embd": "shared",
"output_norm": "final_layer_norm",
"attn_q": "layer.0.SelfAttention.q",
"attn_k": "layer.0.SelfAttention.k",
"attn_v": "layer.0.SelfAttention.v",
"attn_o": "layer.0.SelfAttention.o",
"attn_norm": "layer.0.layer_norm",
"attn_rel_b": "layer.0.SelfAttention.relative_attention_bias",
"ffn_up": "layer.1.DenseReluDense.wi_1",
"ffn_down": "layer.1.DenseReluDense.wo",
"ffn_gate": "layer.1.DenseReluDense.wi_0",
"ffn_norm": "layer.1.layer_norm",
}
wierd_t5_pre_quant_keys_from_city96 = ['shared.weight']
asd_new = {}
for k, v in asd.items():
for s, d in wierd_t5_format_from_city96.items():
k = k.replace(s, d)
asd_new[k] = v
for k in wierd_t5_pre_quant_keys_from_city96:
asd_new[k] = asd_new[k].dequantize_as_pytorch_parameter()
asd.clear()
asd = asd_new
if "decoder.conv_in.weight" in asd:
keys_to_delete = [k for k in sd if k.startswith(vae_key_prefix)]
for k in keys_to_delete:
del sd[k]
for k, v in asd.items():
sd[vae_key_prefix + k] = v
if 'text_model.encoder.layers.0.layer_norm1.weight' in asd:
keys_to_delete = [k for k in sd if k.startswith(f"{text_encoder_key_prefix}clip_l.")]
for k in keys_to_delete:
del sd[k]
for k, v in asd.items():
sd[f"{text_encoder_key_prefix}clip_l.transformer.{k}"] = v
if 'encoder.block.0.layer.0.SelfAttention.k.weight' in asd:
keys_to_delete = [k for k in sd if k.startswith(f"{text_encoder_key_prefix}t5xxl.")]
for k in keys_to_delete:
del sd[k]
for k, v in asd.items():
sd[f"{text_encoder_key_prefix}t5xxl.transformer.{k}"] = v
return sd
def preprocess_state_dict(sd):
if any("double_block" in k for k in sd.keys()):
if not any(k.startswith("model.diffusion_model") for k in sd.keys()):
sd = {f"model.diffusion_model.{k}": v for k, v in sd.items()}
return sd
def split_state_dict(sd, additional_state_dicts: list = None):
sd = load_torch_file(sd)
sd = preprocess_state_dict(sd)
guess = huggingface_guess.guess(sd)
if isinstance(additional_state_dicts, list):
for asd in additional_state_dicts:
asd = load_torch_file(asd)
sd = replace_state_dict(sd, asd, guess)
guess.clip_target = guess.clip_target(sd)
guess.model_type = guess.model_type(sd)
guess.ztsnr = 'ztsnr' in sd
state_dict = {
guess.unet_target: try_filter_state_dict(sd, guess.unet_key_prefix),
guess.vae_target: try_filter_state_dict(sd, guess.vae_key_prefix)
}
sd = guess.process_clip_state_dict(sd)
for k, v in guess.clip_target.items():
state_dict[v] = try_filter_state_dict(sd, [k + '.'])
state_dict['ignore'] = sd
print_dict = {k: len(v) for k, v in state_dict.items()}
print(f'StateDict Keys: {print_dict}')
del state_dict['ignore']
return state_dict, guess
@torch.inference_mode()
def forge_loader(sd, additional_state_dicts=None):
try:
state_dicts, estimated_config = split_state_dict(sd, additional_state_dicts=additional_state_dicts)
except:
raise ValueError('Failed to recognize model type!')
repo_name = estimated_config.huggingface_repo
local_path = os.path.join(dir_path, 'huggingface', repo_name)
config: dict = DiffusionPipeline.load_config(local_path)
huggingface_components = {}
for component_name, v in config.items():
if isinstance(v, list) and len(v) == 2:
lib_name, cls_name = v
component_sd = state_dicts.get(component_name, None)
component = load_huggingface_component(estimated_config, component_name, lib_name, cls_name, local_path, component_sd)
if component_sd is not None:
del state_dicts[component_name]
if component is not None:
huggingface_components[component_name] = component
yaml_config = None
yaml_config_prediction_type = None
try:
import yaml
from pathlib import Path
config_filename = os.path.splitext(sd)[0] + '.yaml'
if Path(config_filename).is_file():
with open(config_filename, 'r') as stream:
yaml_config = yaml.safe_load(stream)
except ImportError:
pass
# Fix Huggingface prediction type using .yaml config or estimated config detection
prediction_types = {
'EPS': 'epsilon',
'V_PREDICTION': 'v_prediction',
'EDM': 'edm',
}
has_prediction_type = 'scheduler' in huggingface_components and hasattr(huggingface_components['scheduler'], 'config') and 'prediction_type' in huggingface_components['scheduler'].config
if yaml_config is not None:
yaml_config_prediction_type: str = (
yaml_config.get('model', {}).get('params', {}).get('parameterization', '')
or yaml_config.get('model', {}).get('params', {}).get('denoiser_config', {}).get('params', {}).get('scaling_config', {}).get('target', '')
)
if yaml_config_prediction_type == 'v' or yaml_config_prediction_type.endswith(".VScaling"):
yaml_config_prediction_type = 'v_prediction'
else:
# Use estimated prediction config if no suitable prediction type found
yaml_config_prediction_type = ''
if has_prediction_type:
if yaml_config_prediction_type:
huggingface_components['scheduler'].config.prediction_type = yaml_config_prediction_type
else:
huggingface_components['scheduler'].config.prediction_type = prediction_types.get(estimated_config.model_type.name, huggingface_components['scheduler'].config.prediction_type)
for M in possible_models:
if any(isinstance(estimated_config, x) for x in M.matched_guesses):
return M(estimated_config=estimated_config, huggingface_components=huggingface_components)
print('Failed to recognize model type!')
return None