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"""Convert JoyImage Edit / Edit Plus checkpoints to diffusers format.
Supports both JoyImage-Edit (single-image editing) and JoyImage-Edit-Plus
(multi-image editing). The transformer weight layout is identical; only the
target model class and pipeline differ.
Usage:
# Convert JoyImage Edit (default)
python convert_joyimage_edit_to_diffusers.py \
--transformer_ckpt_path /path/to/transformer.pt \
--vae_ckpt_path /path/to/vae.pt \
--text_encoder_path Qwen/Qwen3-VL-8B-Instruct \
--output_path /path/to/output \
--save_pipeline
# Convert JoyImage Edit Plus
python convert_joyimage_edit_to_diffusers.py \
--model_type edit_plus \
--transformer_ckpt_path /path/to/transformer.pt \
--vae_ckpt_path /path/to/vae.pt \
--text_encoder_path Qwen/Qwen3-VL-8B-Instruct \
--output_path /path/to/output \
--save_pipeline
"""
import argparse
import torch
from accelerate import init_empty_weights
from transformers import AutoProcessor, AutoTokenizer, Qwen3VLForConditionalGeneration
from diffusers import (
AutoencoderKLWan,
JoyImageEditPipeline,
JoyImageEditTransformer3DModel,
)
from diffusers.models.transformers.transformer_joyimage_edit_plus import JoyImageEditPlusTransformer3DModel
from diffusers.pipelines.joyimage.pipeline_joyimage_edit_plus import JoyImageEditPlusPipeline
from diffusers.schedulers.scheduling_flow_match_euler_discrete import (
FlowMatchEulerDiscreteScheduler,
)
def convert_vae(vae_ckpt_path):
old_state_dict = torch.load(vae_ckpt_path, weights_only=True)
new_state_dict = {}
middle_key_mapping = {
"encoder.middle.0.residual.0.gamma": "encoder.mid_block.resnets.0.norm1.gamma",
"encoder.middle.0.residual.2.bias": "encoder.mid_block.resnets.0.conv1.bias",
"encoder.middle.0.residual.2.weight": "encoder.mid_block.resnets.0.conv1.weight",
"encoder.middle.0.residual.3.gamma": "encoder.mid_block.resnets.0.norm2.gamma",
"encoder.middle.0.residual.6.bias": "encoder.mid_block.resnets.0.conv2.bias",
"encoder.middle.0.residual.6.weight": "encoder.mid_block.resnets.0.conv2.weight",
"encoder.middle.2.residual.0.gamma": "encoder.mid_block.resnets.1.norm1.gamma",
"encoder.middle.2.residual.2.bias": "encoder.mid_block.resnets.1.conv1.bias",
"encoder.middle.2.residual.2.weight": "encoder.mid_block.resnets.1.conv1.weight",
"encoder.middle.2.residual.3.gamma": "encoder.mid_block.resnets.1.norm2.gamma",
"encoder.middle.2.residual.6.bias": "encoder.mid_block.resnets.1.conv2.bias",
"encoder.middle.2.residual.6.weight": "encoder.mid_block.resnets.1.conv2.weight",
"decoder.middle.0.residual.0.gamma": "decoder.mid_block.resnets.0.norm1.gamma",
"decoder.middle.0.residual.2.bias": "decoder.mid_block.resnets.0.conv1.bias",
"decoder.middle.0.residual.2.weight": "decoder.mid_block.resnets.0.conv1.weight",
"decoder.middle.0.residual.3.gamma": "decoder.mid_block.resnets.0.norm2.gamma",
"decoder.middle.0.residual.6.bias": "decoder.mid_block.resnets.0.conv2.bias",
"decoder.middle.0.residual.6.weight": "decoder.mid_block.resnets.0.conv2.weight",
"decoder.middle.2.residual.0.gamma": "decoder.mid_block.resnets.1.norm1.gamma",
"decoder.middle.2.residual.2.bias": "decoder.mid_block.resnets.1.conv1.bias",
"decoder.middle.2.residual.2.weight": "decoder.mid_block.resnets.1.conv1.weight",
"decoder.middle.2.residual.3.gamma": "decoder.mid_block.resnets.1.norm2.gamma",
"decoder.middle.2.residual.6.bias": "decoder.mid_block.resnets.1.conv2.bias",
"decoder.middle.2.residual.6.weight": "decoder.mid_block.resnets.1.conv2.weight",
}
attention_mapping = {
"encoder.middle.1.norm.gamma": "encoder.mid_block.attentions.0.norm.gamma",
"encoder.middle.1.to_qkv.weight": "encoder.mid_block.attentions.0.to_qkv.weight",
"encoder.middle.1.to_qkv.bias": "encoder.mid_block.attentions.0.to_qkv.bias",
"encoder.middle.1.proj.weight": "encoder.mid_block.attentions.0.proj.weight",
"encoder.middle.1.proj.bias": "encoder.mid_block.attentions.0.proj.bias",
"decoder.middle.1.norm.gamma": "decoder.mid_block.attentions.0.norm.gamma",
"decoder.middle.1.to_qkv.weight": "decoder.mid_block.attentions.0.to_qkv.weight",
"decoder.middle.1.to_qkv.bias": "decoder.mid_block.attentions.0.to_qkv.bias",
"decoder.middle.1.proj.weight": "decoder.mid_block.attentions.0.proj.weight",
"decoder.middle.1.proj.bias": "decoder.mid_block.attentions.0.proj.bias",
}
head_mapping = {
"encoder.head.0.gamma": "encoder.norm_out.gamma",
"encoder.head.2.bias": "encoder.conv_out.bias",
"encoder.head.2.weight": "encoder.conv_out.weight",
"decoder.head.0.gamma": "decoder.norm_out.gamma",
"decoder.head.2.bias": "decoder.conv_out.bias",
"decoder.head.2.weight": "decoder.conv_out.weight",
}
quant_mapping = {
"conv1.weight": "quant_conv.weight",
"conv1.bias": "quant_conv.bias",
"conv2.weight": "post_quant_conv.weight",
"conv2.bias": "post_quant_conv.bias",
}
for key, value in old_state_dict.items():
if key in middle_key_mapping:
new_state_dict[middle_key_mapping[key]] = value
elif key in attention_mapping:
new_state_dict[attention_mapping[key]] = value
elif key in head_mapping:
new_state_dict[head_mapping[key]] = value
elif key in quant_mapping:
new_state_dict[quant_mapping[key]] = value
elif key == "encoder.conv1.weight":
new_state_dict["encoder.conv_in.weight"] = value
elif key == "encoder.conv1.bias":
new_state_dict["encoder.conv_in.bias"] = value
elif key == "decoder.conv1.weight":
new_state_dict["decoder.conv_in.weight"] = value
elif key == "decoder.conv1.bias":
new_state_dict["decoder.conv_in.bias"] = value
elif key.startswith("encoder.downsamples."):
new_key = key.replace("encoder.downsamples.", "encoder.down_blocks.")
if ".residual.0.gamma" in new_key:
new_key = new_key.replace(".residual.0.gamma", ".norm1.gamma")
elif ".residual.2.bias" in new_key:
new_key = new_key.replace(".residual.2.bias", ".conv1.bias")
elif ".residual.2.weight" in new_key:
new_key = new_key.replace(".residual.2.weight", ".conv1.weight")
elif ".residual.3.gamma" in new_key:
new_key = new_key.replace(".residual.3.gamma", ".norm2.gamma")
elif ".residual.6.bias" in new_key:
new_key = new_key.replace(".residual.6.bias", ".conv2.bias")
elif ".residual.6.weight" in new_key:
new_key = new_key.replace(".residual.6.weight", ".conv2.weight")
elif ".shortcut.bias" in new_key:
new_key = new_key.replace(".shortcut.bias", ".conv_shortcut.bias")
elif ".shortcut.weight" in new_key:
new_key = new_key.replace(".shortcut.weight", ".conv_shortcut.weight")
new_state_dict[new_key] = value
elif key.startswith("decoder.upsamples."):
parts = key.split(".")
block_idx = int(parts[2])
if "residual" in key:
if block_idx in [0, 1, 2]:
new_block_idx = 0
resnet_idx = block_idx
elif block_idx in [4, 5, 6]:
new_block_idx = 1
resnet_idx = block_idx - 4
elif block_idx in [8, 9, 10]:
new_block_idx = 2
resnet_idx = block_idx - 8
elif block_idx in [12, 13, 14]:
new_block_idx = 3
resnet_idx = block_idx - 12
else:
new_state_dict[key] = value
continue
if ".residual.0.gamma" in key:
new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm1.gamma"
elif ".residual.2.bias" in key:
new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.bias"
elif ".residual.2.weight" in key:
new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv1.weight"
elif ".residual.3.gamma" in key:
new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.norm2.gamma"
elif ".residual.6.bias" in key:
new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.bias"
elif ".residual.6.weight" in key:
new_key = f"decoder.up_blocks.{new_block_idx}.resnets.{resnet_idx}.conv2.weight"
else:
new_key = key
new_state_dict[new_key] = value
elif ".shortcut." in key:
if block_idx == 4:
new_key = key.replace(".shortcut.", ".resnets.0.conv_shortcut.")
new_key = new_key.replace("decoder.upsamples.4", "decoder.up_blocks.1")
else:
new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
new_key = new_key.replace(".shortcut.", ".conv_shortcut.")
new_state_dict[new_key] = value
elif ".resample." in key or ".time_conv." in key:
if block_idx == 3:
new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.0.upsamplers.0")
elif block_idx == 7:
new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.1.upsamplers.0")
elif block_idx == 11:
new_key = key.replace(f"decoder.upsamples.{block_idx}", "decoder.up_blocks.2.upsamplers.0")
else:
new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
new_state_dict[new_key] = value
else:
new_key = key.replace("decoder.upsamples.", "decoder.up_blocks.")
new_state_dict[new_key] = value
else:
new_state_dict[key] = value
with init_empty_weights():
vae = AutoencoderKLWan()
vae.load_state_dict(new_state_dict, strict=True, assign=True)
return vae
TRANSFORMER_CONFIG = {
"hidden_size": 4096,
"in_channels": 16,
"num_attention_heads": 32,
"num_layers": 40,
"out_channels": 16,
"patch_size": [1, 2, 2],
"rope_dim_list": [16, 56, 56],
"text_dim": 4096,
"rope_type": "rope",
"theta": 10000,
}
def convert_transformer(ckpt_path: str, model_type: str = "edit"):
checkpoint = torch.load(ckpt_path, weights_only=True)
if "model" in checkpoint:
original_state_dict = checkpoint["model"]
else:
original_state_dict = checkpoint
attn_suffixes = (
"img_attn_qkv.",
"img_attn_q_norm.",
"img_attn_k_norm.",
"img_attn_proj.",
"txt_attn_qkv.",
"txt_attn_q_norm.",
"txt_attn_k_norm.",
"txt_attn_proj.",
)
remapped = {}
for key, value in original_state_dict.items():
new_key = key
if key.startswith("double_blocks."):
for suffix in attn_suffixes:
if "." + suffix in key and ".attn." + suffix not in key:
new_key = key.replace("." + suffix, ".attn." + suffix)
break
remapped[new_key] = value
transformer_cls = (
JoyImageEditPlusTransformer3DModel if model_type == "edit_plus" else JoyImageEditTransformer3DModel
)
with init_empty_weights():
transformer = transformer_cls(**TRANSFORMER_CONFIG)
transformer.load_state_dict(remapped, strict=True, assign=True)
return transformer
def get_args():
parser = argparse.ArgumentParser(description="Convert JoyImage Edit / Edit Plus checkpoints to diffusers format")
parser.add_argument(
"--model_type",
type=str,
choices=["edit", "edit_plus"],
default="edit",
help="Model type: 'edit' for JoyImage-Edit, 'edit_plus' for JoyImage-Edit-Plus",
)
parser.add_argument(
"--transformer_ckpt_path",
type=str,
default=None,
help="Path to original transformer checkpoint",
)
parser.add_argument(
"--vae_ckpt_path",
type=str,
default=None,
help="Path to original VAE checkpoint",
)
parser.add_argument(
"--text_encoder_path",
type=str,
default=None,
help="Path to Qwen3-VL text encoder (e.g. Qwen/Qwen3-VL-8B-Instruct)",
)
parser.add_argument("--save_pipeline", action="store_true")
parser.add_argument(
"--output_path",
type=str,
required=True,
help="Path where converted model should be saved",
)
parser.add_argument("--dtype", default="bf16", help="Torch dtype (fp32, fp16, bf16)")
parser.add_argument("--flow_shift", type=float, default=1.5)
return parser.parse_args()
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
if __name__ == "__main__":
args = get_args()
transformer = None
vae = None
dtype = DTYPE_MAPPING[args.dtype]
if args.save_pipeline:
assert args.transformer_ckpt_path is not None and args.vae_ckpt_path is not None
assert args.text_encoder_path is not None
if args.transformer_ckpt_path is not None:
transformer = convert_transformer(args.transformer_ckpt_path, model_type=args.model_type)
transformer = transformer.to(dtype=dtype)
if not args.save_pipeline:
transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
if args.vae_ckpt_path is not None:
vae = convert_vae(args.vae_ckpt_path)
vae = vae.to(dtype=dtype)
if not args.save_pipeline:
vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
if args.save_pipeline:
processor = AutoProcessor.from_pretrained(args.text_encoder_path)
text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
args.text_encoder_path, torch_dtype=torch.bfloat16
).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(args.text_encoder_path)
scheduler = FlowMatchEulerDiscreteScheduler(num_train_timesteps=1000, shift=args.flow_shift)
transformer = transformer.to("cuda")
vae = vae.to("cuda")
if args.model_type == "edit_plus":
pipe = JoyImageEditPlusPipeline(
processor=processor,
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
).to("cuda")
else:
pipe = JoyImageEditPipeline(
processor=processor,
transformer=transformer,
text_encoder=text_encoder,
tokenizer=tokenizer,
vae=vae,
scheduler=scheduler,
).to("cuda")
pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
processor.save_pretrained(f"{args.output_path}/processor")