diffusers/scripts/convert_anyflow_to_diffusers.py at dev · xlite-dev/diffusers · GitHub

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# 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.

"""Convert AnyFlow training checkpoints to the diffusers ``save_pretrained`` layout.

The AnyFlow training pipeline emits ``.pt`` files containing an ``ema`` key whose value is a flat state

dict for the transformer. This script:

1. Loads the matching base Wan2.1 pipeline from the Hub (provides VAE, tokenizer, and text encoder).

2. Constructs an ``AnyFlowTransformer3DModel`` with the right config flags for the chosen variant.

3. Loads the ``ema`` weights into the transformer.

4. Wraps everything in an ``AnyFlowPipeline`` (bidirectional) or ``AnyFlowFARPipeline`` (FAR causal).

5. Calls ``pipeline.save_pretrained(output_dir)``.

Example:

```bash

python scripts/convert_anyflow_to_diffusers.py \\

--variant AnyFlow-FAR-Wan2.1-1.3B-Diffusers \\

--ckpt /path/to/anyflow-checkpoint.pt \\

--output-dir /path/to/output/AnyFlow-FAR-Wan2.1-1.3B-Diffusers

```

"""

import argparse

import logging

import os

import torch

from diffusers import (

AnyFlowFARPipeline,

AnyFlowFARTransformer3DModel,

AnyFlowPipeline,

AnyFlowTransformer3DModel,

FlowMapEulerDiscreteScheduler,

)

logger = logging.getLogger(__name__)

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")

# Per-variant configuration. ``base_model`` is fetched from the Hub to source the matching VAE / text encoder.

VARIANTS = {

"AnyFlow-FAR-Wan2.1-1.3B-Diffusers": {

"base_model": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",

"transformer_cls": AnyFlowFARTransformer3DModel,

"transformer_kwargs": {

"full_chunk_limit": 3,

"compressed_patch_size": [1, 4, 4],

"chunk_partition": [1, 3, 3, 3, 3, 3, 3, 2],

"pipeline_cls": AnyFlowFARPipeline,

"AnyFlow-FAR-Wan2.1-14B-Diffusers": {

"base_model": "Wan-AI/Wan2.1-T2V-14B-Diffusers",

"transformer_cls": AnyFlowFARTransformer3DModel,

"transformer_kwargs": {

"full_chunk_limit": 3,

"compressed_patch_size": [1, 4, 4],

"chunk_partition": [1, 3, 3, 3, 3, 3, 3, 2],

"pipeline_cls": AnyFlowFARPipeline,

"AnyFlow-Wan2.1-T2V-1.3B-Diffusers": {

"base_model": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",

"transformer_cls": AnyFlowTransformer3DModel,

"transformer_kwargs": {},

"pipeline_cls": AnyFlowPipeline,

"AnyFlow-Wan2.1-T2V-14B-Diffusers": {

"base_model": "Wan-AI/Wan2.1-T2V-14B-Diffusers",

"transformer_cls": AnyFlowTransformer3DModel,

"transformer_kwargs": {},

"pipeline_cls": AnyFlowPipeline,

}

def build_pipeline(variant: str, ckpt_path: str):

if variant not in VARIANTS:

raise ValueError(f"Unknown variant {variant!r}. Choices: {list(VARIANTS)}.")

spec = VARIANTS[variant]

transformer = spec["transformer_cls"].from_pretrained(

spec["base_model"],

subfolder="transformer",

gate_value=0.25,

deltatime_type="r",

**spec["transformer_kwargs"],

)

# NVlabs/AnyFlow training checkpoints are wrapped Python objects (the `ema` key carries metadata

# alongside tensors), so the unpickle is required. Only run this script on checkpoints you trust.

state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=False)["ema"]

missing, unexpected = transformer.load_state_dict(state_dict, strict=False)

if unexpected:

logger.warning(

"Unexpected keys in state dict (ignored): %s%s",

unexpected[:5],

"..." if len(unexpected) > 5 else "",

)

if missing:

logger.warning(

"Missing keys not loaded from state dict: %s%s",

missing[:5],

"..." if len(missing) > 5 else "",

)

scheduler = FlowMapEulerDiscreteScheduler(num_train_timesteps=1000, shift=5.0)

pipeline = spec["pipeline_cls"].from_pretrained(

spec["base_model"],

transformer=transformer,

scheduler=scheduler,

)

return pipeline

def main():

parser = argparse.ArgumentParser(

description="Convert an AnyFlow training checkpoint into a diffusers pipeline directory."

)

parser.add_argument(

"--variant",

required=True,

choices=list(VARIANTS),

help="Which AnyFlow variant the checkpoint corresponds to.",

)

parser.add_argument(

"--ckpt",

required=True,

help="Path to the AnyFlow training checkpoint (a .pt file containing an 'ema' key).",

)

parser.add_argument(

"--output-dir",

required=True,

help="Destination directory for pipeline.save_pretrained.",

)

args = parser.parse_args()

os.makedirs(args.output_dir, exist_ok=True)

pipeline = build_pipeline(args.variant, args.ckpt)

pipeline.save_pretrained(args.output_dir)

logger.info("Saved %s pipeline to %s", args.variant, args.output_dir)

if __name__ == "__main__":

main()

diffusers/scripts/convert_anyflow_to_diffusers.py at dev · xlite-dev/diffusers · GitHub

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