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| # Copyright 2024 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.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta | |
| from ..utils import ( | |
| is_accelerate_available, | |
| is_torch_version, | |
| logging, | |
| ) | |
| 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) | |
| return image_projection | |
| def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT): | |
| from ..models.attention_processor import ( | |
| FluxIPAdapterJointAttnProcessor2_0, | |
| ) | |
| 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 = FluxIPAdapterJointAttnProcessor2_0 | |
| 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 | |
| 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" | |