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pipeline.py

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+# Copyright 2026 Echo Team and 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.
+
+"""Echo-Memory community pipeline for official Wan 2.1 Diffusers weights.
+
+Loads `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`, then overlays the released
+`context_k1` row from `Echo-Team/Echo-Memory` after remapping original
+DiffSynth / Wan keys onto the Diffusers transformer.
+
+Paper: https://arxiv.org/abs/2606.09803
+Code: https://github.com/Echo-Team-Joy-Future-Academy-JD/Echo-Memory
+"""
+
+from typing import Dict, Iterable, List, Optional, Tuple
+
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+
+from diffusers import WanPipeline
+
+
+DEFAULT_BASE_MODEL = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DEFAULT_REPO_ID = "Echo-Team/Echo-Memory"
+DEFAULT_FILENAME = "context_k1/epoch-0.safetensors"
+DEFAULT_CONVERTED_REPO_ID = "Wayne-King/echo-memory-diffusers"
+DEFAULT_CONVERTED_FILENAME = "context_k1-diffusers/diffusion_pytorch_model.safetensors"
+
+SKIP_SUBSTRINGS = (
+    "action_mlp",
+    "self_attn_with_action",
+    "block_wise_ssm",
+    "videossm_hybrid",
+    "spatial_memory_module",
+)
+
+# Same mapping as `scripts/convert_wan_to_diffusers.py` for Wan 2.1 T2V.
+TRANSFORMER_KEYS_RENAME_DICT = {
+    "time_embedding.0": "condition_embedder.time_embedder.linear_1",
+    "time_embedding.2": "condition_embedder.time_embedder.linear_2",
+    "text_embedding.0": "condition_embedder.text_embedder.linear_1",
+    "text_embedding.2": "condition_embedder.text_embedder.linear_2",
+    "time_projection.1": "condition_embedder.time_proj",
+    "head.modulation": "scale_shift_table",
+    "head.head": "proj_out",
+    "modulation": "scale_shift_table",
+    "ffn.0": "ffn.net.0.proj",
+    "ffn.2": "ffn.net.2",
+    # The original model names norms as norm1, norm3, norm2.
+    # Diffusers uses norm1, norm2, norm3.
+    "norm2": "norm__placeholder",
+    "norm3": "norm2",
+    "norm__placeholder": "norm3",
+    "self_attn.q": "attn1.to_q",
+    "self_attn.k": "attn1.to_k",
+    "self_attn.v": "attn1.to_v",
+    "self_attn.o": "attn1.to_out.0",
+    "self_attn.norm_q": "attn1.norm_q",
+    "self_attn.norm_k": "attn1.norm_k",
+    "cross_attn.q": "attn2.to_q",
+    "cross_attn.k": "attn2.to_k",
+    "cross_attn.v": "attn2.to_v",
+    "cross_attn.o": "attn2.to_out.0",
+    "cross_attn.norm_q": "attn2.norm_q",
+    "cross_attn.norm_k": "attn2.norm_k",
+}
+
+
+def is_diffusers_transformer_state_dict(keys: Iterable[str]) -> bool:
+    keys = list(keys)
+    return any(key.startswith("condition_embedder.") or ".attn1." in key for key in keys)
+
+
+def convert_echo_memory_transformer_state_dict(
+    state_dict: Dict[str, torch.Tensor],
+    skip_substrings: Iterable[str] = SKIP_SUBSTRINGS,
+) -> Tuple[Dict[str, torch.Tensor], List[str]]:
+    """Convert original Echo-Memory / DiffSynth Wan keys to Diffusers names."""
+    skip_substrings = tuple(skip_substrings)
+    if is_diffusers_transformer_state_dict(state_dict):
+        converted = {
+            key: value
+            for key, value in state_dict.items()
+            if not any(token in key for token in skip_substrings)
+        }
+        skipped = [key for key in state_dict if key not in converted]
+        return converted, skipped
+
+    converted = {}
+    skipped = []
+    for key, value in state_dict.items():
+        if any(token in key for token in skip_substrings):
+            skipped.append(key)
+            continue
+        new_key = key
+        for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
+            new_key = new_key.replace(replace_key, rename_key)
+        converted[new_key] = value
+    return converted, skipped
+
+
+class EchoMemoryPipeline(WanPipeline):
+    """Wan 2.1 T2V pipeline with an Echo-Memory `context_k1` overlay."""
+
+    def load_echo_memory_weights(
+        self,
+        repo_id: str = DEFAULT_REPO_ID,
+        filename: str = DEFAULT_FILENAME,
+        local_path: Optional[str] = None,
+        strict: bool = False,
+    ):
+        """Download one Echo-Memory row and overlay it on `self.transformer`."""
+        ckpt_path = local_path or hf_hub_download(repo_id=repo_id, filename=filename)
+        raw = load_file(ckpt_path)
+        converted, skipped = convert_echo_memory_transformer_state_dict(raw)
+        missing, unexpected = self.transformer.load_state_dict(converted, strict=strict)
+        print(
+            f"[Echo-Memory] overlaid {len(converted)}/{len(raw)} transformer keys from {ckpt_path} "
+            f"(skipped={len(skipped)}, missing={len(missing)}, unexpected={len(unexpected)})"
+        )
+        return missing, unexpected, skipped
+
+    def load_converted_echo_memory_weights(
+        self,
+        repo_id: str = DEFAULT_CONVERTED_REPO_ID,
+        filename: str = DEFAULT_CONVERTED_FILENAME,
+        local_path: Optional[str] = None,
+        strict: bool = False,
+    ):
+        """Overlay the already-remapped `context_k1` transformer weights."""
+        return self.load_echo_memory_weights(
+            repo_id=repo_id,
+            filename=filename,
+            local_path=local_path,
+            strict=strict,
+        )
+
+    @classmethod
+    def from_echo_memory(
+        cls,
+        pretrained_model_name_or_path: str = DEFAULT_BASE_MODEL,
+        echo_memory_repo: str = DEFAULT_REPO_ID,
+        echo_memory_filename: str = DEFAULT_FILENAME,
+        **kwargs,
+    ):
+        pipe = cls.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        pipe.load_echo_memory_weights(repo_id=echo_memory_repo, filename=echo_memory_filename)
+        return pipe