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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 typing import Dict | |
| from ..models.attention_processor import SD3IPAdapterJointAttnProcessor2_0 | |
| from ..models.embeddings import IPAdapterTimeImageProjection | |
| 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 | |
| logger = logging.get_logger(__name__) | |
| class SD3Transformer2DLoadersMixin: | |
| """Load IP-Adapters and LoRA layers into a `[SD3Transformer2DModel]`.""" | |
| def _convert_ip_adapter_attn_to_diffusers( | |
| self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT | |
| ) -> Dict: | |
| 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`." | |
| ) | |
| # IP-Adapter cross attention parameters | |
| hidden_size = self.config.attention_head_dim * self.config.num_attention_heads | |
| ip_hidden_states_dim = self.config.attention_head_dim * self.config.num_attention_heads | |
| timesteps_emb_dim = state_dict["0.norm_ip.linear.weight"].shape[1] | |
| # Dict where key is transformer layer index, value is attention processor's state dict | |
| # ip_adapter state dict keys example: "0.norm_ip.linear.weight" | |
| layer_state_dict = {idx: {} for idx in range(len(self.attn_processors))} | |
| for key, weights in state_dict.items(): | |
| idx, name = key.split(".", maxsplit=1) | |
| layer_state_dict[int(idx)][name] = weights | |
| # Create IP-Adapter attention processor & load state_dict | |
| attn_procs = {} | |
| init_context = init_empty_weights if low_cpu_mem_usage else nullcontext | |
| for idx, name in enumerate(self.attn_processors.keys()): | |
| with init_context(): | |
| attn_procs[name] = SD3IPAdapterJointAttnProcessor2_0( | |
| hidden_size=hidden_size, | |
| ip_hidden_states_dim=ip_hidden_states_dim, | |
| head_dim=self.config.attention_head_dim, | |
| timesteps_emb_dim=timesteps_emb_dim, | |
| ) | |
| if not low_cpu_mem_usage: | |
| attn_procs[name].load_state_dict(layer_state_dict[idx], strict=True) | |
| else: | |
| device_map = {"": self.device} | |
| load_model_dict_into_meta( | |
| attn_procs[name], layer_state_dict[idx], device_map=device_map, dtype=self.dtype | |
| ) | |
| return attn_procs | |
| def _convert_ip_adapter_image_proj_to_diffusers( | |
| self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT | |
| ) -> IPAdapterTimeImageProjection: | |
| 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`." | |
| ) | |
| init_context = init_empty_weights if low_cpu_mem_usage else nullcontext | |
| # Convert to diffusers | |
| updated_state_dict = {} | |
| for key, value in state_dict.items(): | |
| # InstantX/SD3.5-Large-IP-Adapter | |
| if key.startswith("layers."): | |
| idx = key.split(".")[1] | |
| key = key.replace(f"layers.{idx}.0.norm1", f"layers.{idx}.ln0") | |
| key = key.replace(f"layers.{idx}.0.norm2", f"layers.{idx}.ln1") | |
| key = key.replace(f"layers.{idx}.0.to_q", f"layers.{idx}.attn.to_q") | |
| key = key.replace(f"layers.{idx}.0.to_kv", f"layers.{idx}.attn.to_kv") | |
| key = key.replace(f"layers.{idx}.0.to_out", f"layers.{idx}.attn.to_out.0") | |
| key = key.replace(f"layers.{idx}.1.0", f"layers.{idx}.adaln_norm") | |
| key = key.replace(f"layers.{idx}.1.1", f"layers.{idx}.ff.net.0.proj") | |
| key = key.replace(f"layers.{idx}.1.3", f"layers.{idx}.ff.net.2") | |
| key = key.replace(f"layers.{idx}.2.1", f"layers.{idx}.adaln_proj") | |
| updated_state_dict[key] = value | |
| # Image projection parameters | |
| embed_dim = updated_state_dict["proj_in.weight"].shape[1] | |
| output_dim = updated_state_dict["proj_out.weight"].shape[0] | |
| hidden_dim = updated_state_dict["proj_in.weight"].shape[0] | |
| heads = updated_state_dict["layers.0.attn.to_q.weight"].shape[0] // 64 | |
| num_queries = updated_state_dict["latents"].shape[1] | |
| timestep_in_dim = updated_state_dict["time_embedding.linear_1.weight"].shape[1] | |
| # Image projection | |
| with init_context(): | |
| image_proj = IPAdapterTimeImageProjection( | |
| embed_dim=embed_dim, | |
| output_dim=output_dim, | |
| hidden_dim=hidden_dim, | |
| heads=heads, | |
| num_queries=num_queries, | |
| timestep_in_dim=timestep_in_dim, | |
| ) | |
| if not low_cpu_mem_usage: | |
| image_proj.load_state_dict(updated_state_dict, strict=True) | |
| else: | |
| device_map = {"": self.device} | |
| load_model_dict_into_meta(image_proj, updated_state_dict, device_map=device_map, dtype=self.dtype) | |
| return image_proj | |
| def _load_ip_adapter_weights(self, state_dict: Dict, low_cpu_mem_usage: bool = _LOW_CPU_MEM_USAGE_DEFAULT) -> None: | |
| """Sets IP-Adapter attention processors, image projection, and loads state_dict. | |
| Args: | |
| state_dict (`Dict`): | |
| State dict with keys "ip_adapter", which contains parameters for attention processors, and | |
| "image_proj", which contains parameters for image projection net. | |
| low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`): | |
| Speed up model loading only loading the pretrained weights and not initializing the weights. This also | |
| tries to not use more than 1x model size in CPU memory (including peak memory) while loading the model. | |
| Only supported for PyTorch >= 1.9.0. If you are using an older version of PyTorch, setting this | |
| argument to `True` will raise an error. | |
| """ | |
| attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dict["ip_adapter"], low_cpu_mem_usage) | |
| self.set_attn_processor(attn_procs) | |
| self.image_proj = self._convert_ip_adapter_image_proj_to_diffusers(state_dict["image_proj"], low_cpu_mem_usage) | |