# Copyright 2025 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from contextlib import nullcontext from ..models.embeddings import ( ImageProjection, MultiIPAdapterImageProjection, ) from ..models.model_loading_utils import load_model_dict_into_meta from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT from ..utils import is_accelerate_available, is_torch_version, logging from ..utils.torch_utils import empty_device_cache if is_accelerate_available(): pass logger = logging.get_logger(__name__) class FluxTransformer2DLoadersMixin: """ Load layers into a [`FluxTransformer2DModel`]. """ def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT): if low_cpu_mem_usage: if is_accelerate_available(): from accelerate import init_empty_weights else: low_cpu_mem_usage = False logger.warning( "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" " install accelerate\n```\n." ) if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" " `low_cpu_mem_usage=False`." ) updated_state_dict = {} image_projection = None init_context = init_empty_weights if low_cpu_mem_usage else nullcontext if "proj.weight" in state_dict: # IP-Adapter num_image_text_embeds = 4 if state_dict["proj.weight"].shape[0] == 65536: num_image_text_embeds = 16 clip_embeddings_dim = state_dict["proj.weight"].shape[-1] cross_attention_dim = state_dict["proj.weight"].shape[0] // num_image_text_embeds with init_context(): image_projection = ImageProjection( cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim, num_image_text_embeds=num_image_text_embeds, ) for key, value in state_dict.items(): diffusers_name = key.replace("proj", "image_embeds") updated_state_dict[diffusers_name] = value if not low_cpu_mem_usage: image_projection.load_state_dict(updated_state_dict, strict=True) else: device_map = {"": self.device} load_model_dict_into_meta(image_projection, updated_state_dict, device_map=device_map, dtype=self.dtype) empty_device_cache() return image_projection def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT): from ..models.transformers.transformer_flux import FluxIPAdapterAttnProcessor if low_cpu_mem_usage: if is_accelerate_available(): from accelerate import init_empty_weights else: low_cpu_mem_usage = False logger.warning( "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the" " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install" " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip" " install accelerate\n```\n." ) if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"): raise NotImplementedError( "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set" " `low_cpu_mem_usage=False`." ) # set ip-adapter cross-attention processors & load state_dict attn_procs = {} key_id = 0 init_context = init_empty_weights if low_cpu_mem_usage else nullcontext for name in self.attn_processors.keys(): if name.startswith("single_transformer_blocks"): attn_processor_class = self.attn_processors[name].__class__ attn_procs[name] = attn_processor_class() else: cross_attention_dim = self.config.joint_attention_dim hidden_size = self.inner_dim attn_processor_class = FluxIPAdapterAttnProcessor num_image_text_embeds = [] for state_dict in state_dicts: if "proj.weight" in state_dict["image_proj"]: num_image_text_embed = 4 if state_dict["image_proj"]["proj.weight"].shape[0] == 65536: num_image_text_embed = 16 # IP-Adapter num_image_text_embeds += [num_image_text_embed] with init_context(): attn_procs[name] = attn_processor_class( hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, num_tokens=num_image_text_embeds, dtype=self.dtype, device=self.device, ) value_dict = {} for i, state_dict in enumerate(state_dicts): value_dict.update({f"to_k_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_k_ip.weight"]}) value_dict.update({f"to_v_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_v_ip.weight"]}) value_dict.update({f"to_k_ip.{i}.bias": state_dict["ip_adapter"][f"{key_id}.to_k_ip.bias"]}) value_dict.update({f"to_v_ip.{i}.bias": state_dict["ip_adapter"][f"{key_id}.to_v_ip.bias"]}) if not low_cpu_mem_usage: attn_procs[name].load_state_dict(value_dict) else: device_map = {"": self.device} dtype = self.dtype load_model_dict_into_meta(attn_procs[name], value_dict, device_map=device_map, dtype=dtype) key_id += 1 empty_device_cache() return attn_procs def _load_ip_adapter_weights(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT): if not isinstance(state_dicts, list): state_dicts = [state_dicts] self.encoder_hid_proj = None attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage) self.set_attn_processor(attn_procs) image_projection_layers = [] for state_dict in state_dicts: image_projection_layer = self._convert_ip_adapter_image_proj_to_diffusers( state_dict["image_proj"], low_cpu_mem_usage=low_cpu_mem_usage ) image_projection_layers.append(image_projection_layer) self.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers) self.config.encoder_hid_dim_type = "ip_image_proj"