import os import transformers import diffusers from modules import shared, devices, sd_models, model_quant, sd_hijack_te def load_transformer(repo_id, diffusers_load_config={}): load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model', device_map=True) fn = None if shared.opts.sd_unet is not None and shared.opts.sd_unet != 'Default': from modules import sd_unet if shared.opts.sd_unet not in list(sd_unet.unet_dict): shared.log.error(f'Load module: type=Transformer not found: {shared.opts.sd_unet}') return None fn = sd_unet.unet_dict[shared.opts.sd_unet] if os.path.exists(sd_unet.unet_dict[shared.opts.sd_unet]) else None if fn is not None and 'gguf' in fn.lower(): shared.log.error('Load model: type=Cosmos format="gguf" unsupported') transformer = None elif fn is not None and 'safetensors' in fn.lower(): shared.log.debug(f'Load model: type=Cosmos transformer="{repo_id}" quant="{model_quant.get_quant(repo_id)}" args={load_args}') transformer = diffusers.CosmosTransformer3DModel.from_single_file(fn, cache_dir=shared.opts.hfcache_dir, **load_args) else: shared.log.debug(f'Load model: type=Cosmos transformer="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}') transformer = diffusers.CosmosTransformer3DModel.from_pretrained( repo_id, subfolder="transformer", cache_dir=shared.opts.hfcache_dir, **load_args, **quant_args, ) if shared.opts.diffusers_offload_mode != 'none' and transformer is not None: sd_models.move_model(transformer, devices.cpu) return transformer def load_text_encoder(repo_id, diffusers_load_config={}): load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True) shared.log.debug(f'Load model: type=Cosmos te="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}') text_encoder = transformers.T5EncoderModel.from_pretrained( repo_id, subfolder="text_encoder", cache_dir=shared.opts.hfcache_dir, **load_args, **quant_args, ) if shared.opts.diffusers_offload_mode != 'none' and text_encoder is not None: sd_models.move_model(text_encoder, devices.cpu) load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True) llama_repo = shared.opts.model_h1_llama_repo if shared.opts.model_h1_llama_repo != 'Default' else 'meta-llama/Meta-Llama-3.1-8B-Instruct' shared.log.debug(f'Load model: type=HiDream te4="{llama_repo}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}') return text_encoder def load_cosmos_t2i(checkpoint_info, diffusers_load_config={}): repo_id = sd_models.path_to_repo(checkpoint_info) sd_models.hf_auth_check(checkpoint_info) transformer = load_transformer(repo_id, diffusers_load_config) text_encoder = load_text_encoder(repo_id, diffusers_load_config) safety_checker = Fake_safety_checker() load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model') shared.log.debug(f'Load model: type=Cosmos model="{checkpoint_info.name}" repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}') cls = diffusers.Cosmos2TextToImagePipeline pipe = cls.from_pretrained( repo_id, transformer=transformer, text_encoder=text_encoder, safety_checker=safety_checker, cache_dir=shared.opts.diffusers_dir, **load_args, ) sd_hijack_te.init_hijack(pipe) del text_encoder del transformer devices.torch_gc() return pipe class Fake_safety_checker: def __init__(self): from diffusers.utils import import_utils import_utils._cosmos_guardrail_available = True # pylint: disable=protected-access def __call__(self, *args, **kwargs): # pylint: disable=unused-argument return def to(self, _device): pass def check_text_safety(self, _prompt): return True def check_video_safety(self, vid): return vid