180 lines
7.7 KiB
Python
180 lines
7.7 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from contextlib import nullcontext
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from ..models.embeddings import (
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ImageProjection,
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MultiIPAdapterImageProjection,
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)
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from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
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from ..utils import (
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is_accelerate_available,
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is_torch_version,
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logging,
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)
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if is_accelerate_available():
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pass
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logger = logging.get_logger(__name__)
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class FluxTransformer2DLoadersMixin:
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"""
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Load layers into a [`FluxTransformer2DModel`].
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"""
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def _convert_ip_adapter_image_proj_to_diffusers(self, state_dict, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT):
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if low_cpu_mem_usage:
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if is_accelerate_available():
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from accelerate import init_empty_weights
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else:
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low_cpu_mem_usage = False
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logger.warning(
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"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
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" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
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" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
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" install accelerate\n```\n."
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)
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if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
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raise NotImplementedError(
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"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
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" `low_cpu_mem_usage=False`."
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)
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updated_state_dict = {}
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image_projection = None
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init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
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if "proj.weight" in state_dict:
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# IP-Adapter
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num_image_text_embeds = 4
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if state_dict["proj.weight"].shape[0] == 65536:
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num_image_text_embeds = 16
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clip_embeddings_dim = state_dict["proj.weight"].shape[-1]
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cross_attention_dim = state_dict["proj.weight"].shape[0] // num_image_text_embeds
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with init_context():
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image_projection = ImageProjection(
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cross_attention_dim=cross_attention_dim,
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image_embed_dim=clip_embeddings_dim,
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num_image_text_embeds=num_image_text_embeds,
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)
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for key, value in state_dict.items():
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diffusers_name = key.replace("proj", "image_embeds")
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updated_state_dict[diffusers_name] = value
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if not low_cpu_mem_usage:
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image_projection.load_state_dict(updated_state_dict, strict=True)
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else:
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device_map = {"": self.device}
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load_model_dict_into_meta(image_projection, updated_state_dict, device_map=device_map, dtype=self.dtype)
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return image_projection
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def _convert_ip_adapter_attn_to_diffusers(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT):
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from ..models.attention_processor import (
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FluxIPAdapterJointAttnProcessor2_0,
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)
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if low_cpu_mem_usage:
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if is_accelerate_available():
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from accelerate import init_empty_weights
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else:
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low_cpu_mem_usage = False
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logger.warning(
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"Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
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" environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
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" `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
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" install accelerate\n```\n."
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)
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if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
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raise NotImplementedError(
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"Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or set"
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" `low_cpu_mem_usage=False`."
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)
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# set ip-adapter cross-attention processors & load state_dict
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attn_procs = {}
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key_id = 0
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init_context = init_empty_weights if low_cpu_mem_usage else nullcontext
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for name in self.attn_processors.keys():
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if name.startswith("single_transformer_blocks"):
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attn_processor_class = self.attn_processors[name].__class__
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attn_procs[name] = attn_processor_class()
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else:
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cross_attention_dim = self.config.joint_attention_dim
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hidden_size = self.inner_dim
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attn_processor_class = FluxIPAdapterJointAttnProcessor2_0
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num_image_text_embeds = []
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for state_dict in state_dicts:
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if "proj.weight" in state_dict["image_proj"]:
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num_image_text_embed = 4
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if state_dict["image_proj"]["proj.weight"].shape[0] == 65536:
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num_image_text_embed = 16
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# IP-Adapter
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num_image_text_embeds += [num_image_text_embed]
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with init_context():
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attn_procs[name] = attn_processor_class(
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hidden_size=hidden_size,
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cross_attention_dim=cross_attention_dim,
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scale=1.0,
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num_tokens=num_image_text_embeds,
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dtype=self.dtype,
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device=self.device,
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)
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value_dict = {}
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for i, state_dict in enumerate(state_dicts):
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value_dict.update({f"to_k_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_k_ip.weight"]})
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value_dict.update({f"to_v_ip.{i}.weight": state_dict["ip_adapter"][f"{key_id}.to_v_ip.weight"]})
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value_dict.update({f"to_k_ip.{i}.bias": state_dict["ip_adapter"][f"{key_id}.to_k_ip.bias"]})
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value_dict.update({f"to_v_ip.{i}.bias": state_dict["ip_adapter"][f"{key_id}.to_v_ip.bias"]})
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if not low_cpu_mem_usage:
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attn_procs[name].load_state_dict(value_dict)
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else:
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device_map = {"": self.device}
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dtype = self.dtype
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load_model_dict_into_meta(attn_procs[name], value_dict, device_map=device_map, dtype=dtype)
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key_id += 1
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return attn_procs
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def _load_ip_adapter_weights(self, state_dicts, low_cpu_mem_usage=_LOW_CPU_MEM_USAGE_DEFAULT):
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if not isinstance(state_dicts, list):
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state_dicts = [state_dicts]
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self.encoder_hid_proj = None
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attn_procs = self._convert_ip_adapter_attn_to_diffusers(state_dicts, low_cpu_mem_usage=low_cpu_mem_usage)
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self.set_attn_processor(attn_procs)
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image_projection_layers = []
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for state_dict in state_dicts:
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image_projection_layer = self._convert_ip_adapter_image_proj_to_diffusers(
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state_dict["image_proj"], low_cpu_mem_usage=low_cpu_mem_usage
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)
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image_projection_layers.append(image_projection_layer)
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self.encoder_hid_proj = MultiIPAdapterImageProjection(image_projection_layers)
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self.config.encoder_hid_dim_type = "ip_image_proj"
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