import transformers import diffusers from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae from modules.logger import log from pipelines import generic def load_cosmos_t2i(checkpoint_info, diffusers_load_config=None): if diffusers_load_config is None: diffusers_load_config = {} repo_id = sd_models.path_to_repo(checkpoint_info) sd_models.hf_auth_check(checkpoint_info) load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False) log.debug(f'Load model: type=Cosmos repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}') transformer = generic.load_transformer(repo_id, cls_name=diffusers.CosmosTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer") repo_te = 'nvidia/Cosmos-Predict2-2B-Text2Image' if 'Cosmos-Predict2-14B-Text2Image' in repo_id else repo_id text_encoder = generic.load_text_encoder(repo_te, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder", allow_shared=False) # cosmos does use standard t5 safety_checker = Fake_safety_checker() pipe = diffusers.Cosmos2TextToImagePipeline.from_pretrained( repo_id, transformer=transformer, text_encoder=text_encoder, safety_checker=safety_checker, cache_dir=shared.opts.diffusers_dir, **load_args, ) del text_encoder del transformer sd_hijack_te.init_hijack(pipe) sd_hijack_vae.init_hijack(pipe) 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