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Copy pathconvert_prx_to_diffusers.py
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345 lines (254 loc) · 11.5 KB
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#!/usr/bin/env python3
"""
Script to convert PRX checkpoint from original codebase to diffusers format.
"""
import argparse
import json
import os
import sys
from dataclasses import asdict, dataclass
from typing import Dict, Tuple
import torch
from safetensors.torch import save_file
from diffusers.models.transformers.transformer_prx import PRXTransformer2DModel
from diffusers.pipelines.prx import PRXPipeline
DEFAULT_RESOLUTION = 512
@dataclass(frozen=True)
class PRXBase:
context_in_dim: int = 2304
hidden_size: int = 1792
mlp_ratio: float = 3.5
num_heads: int = 28
depth: int = 16
axes_dim: Tuple[int, int] = (32, 32)
theta: int = 10_000
time_factor: float = 1000.0
time_max_period: int = 10_000
@dataclass(frozen=True)
class PRXFlux(PRXBase):
in_channels: int = 16
patch_size: int = 2
@dataclass(frozen=True)
class PRXDCAE(PRXBase):
in_channels: int = 32
patch_size: int = 1
def build_config(vae_type: str) -> Tuple[dict, int]:
if vae_type == "flux":
cfg = PRXFlux()
elif vae_type == "dc-ae":
cfg = PRXDCAE()
else:
raise ValueError(f"Unsupported VAE type: {vae_type}. Use 'flux' or 'dc-ae'")
config_dict = asdict(cfg)
config_dict["axes_dim"] = list(config_dict["axes_dim"]) # type: ignore[index]
return config_dict
def create_parameter_mapping(depth: int) -> dict:
"""Create mapping from old parameter names to new diffusers names."""
# Key mappings for structural changes
mapping = {}
# Map old structure (layers in PRXBlock) to new structure (layers in PRXAttention)
for i in range(depth):
# QKV projections moved to attention module
mapping[f"blocks.{i}.img_qkv_proj.weight"] = f"blocks.{i}.attention.img_qkv_proj.weight"
mapping[f"blocks.{i}.txt_kv_proj.weight"] = f"blocks.{i}.attention.txt_kv_proj.weight"
# QK norm moved to attention module and renamed to match Attention's qk_norm structure
mapping[f"blocks.{i}.qk_norm.query_norm.scale"] = f"blocks.{i}.attention.norm_q.weight"
mapping[f"blocks.{i}.qk_norm.key_norm.scale"] = f"blocks.{i}.attention.norm_k.weight"
mapping[f"blocks.{i}.qk_norm.query_norm.weight"] = f"blocks.{i}.attention.norm_q.weight"
mapping[f"blocks.{i}.qk_norm.key_norm.weight"] = f"blocks.{i}.attention.norm_k.weight"
# K norm for text tokens moved to attention module
mapping[f"blocks.{i}.k_norm.scale"] = f"blocks.{i}.attention.norm_added_k.weight"
mapping[f"blocks.{i}.k_norm.weight"] = f"blocks.{i}.attention.norm_added_k.weight"
# Attention output projection
mapping[f"blocks.{i}.attn_out.weight"] = f"blocks.{i}.attention.to_out.0.weight"
return mapping
def convert_checkpoint_parameters(old_state_dict: Dict[str, torch.Tensor], depth: int) -> dict[str, torch.Tensor]:
"""Convert old checkpoint parameters to new diffusers format."""
print("Converting checkpoint parameters...")
mapping = create_parameter_mapping(depth)
converted_state_dict = {}
for key, value in old_state_dict.items():
new_key = key
# Apply specific mappings if needed
if key in mapping:
new_key = mapping[key]
print(f" Mapped: {key} -> {new_key}")
converted_state_dict[new_key] = value
print(f"✓ Converted {len(converted_state_dict)} parameters")
return converted_state_dict
def create_transformer_from_checkpoint(checkpoint_path: str, config: dict) -> PRXTransformer2DModel:
"""Create and load PRXTransformer2DModel from old checkpoint."""
print(f"Loading checkpoint from: {checkpoint_path}")
# Load old checkpoint
if not os.path.exists(checkpoint_path):
raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
old_checkpoint = torch.load(checkpoint_path, map_location="cpu")
# Handle different checkpoint formats
if isinstance(old_checkpoint, dict):
if "model" in old_checkpoint:
state_dict = old_checkpoint["model"]
elif "state_dict" in old_checkpoint:
state_dict = old_checkpoint["state_dict"]
else:
state_dict = old_checkpoint
else:
state_dict = old_checkpoint
print(f"✓ Loaded checkpoint with {len(state_dict)} parameters")
# Convert parameter names if needed
model_depth = int(config.get("depth", 16))
converted_state_dict = convert_checkpoint_parameters(state_dict, depth=model_depth)
# Create transformer with config
print("Creating PRXTransformer2DModel...")
transformer = PRXTransformer2DModel(**config)
# Load state dict
print("Loading converted parameters...")
missing_keys, unexpected_keys = transformer.load_state_dict(converted_state_dict, strict=False)
if missing_keys:
print(f"⚠ Missing keys: {missing_keys}")
if unexpected_keys:
print(f"⚠ Unexpected keys: {unexpected_keys}")
if not missing_keys and not unexpected_keys:
print("✓ All parameters loaded successfully!")
return transformer
def create_scheduler_config(output_path: str, shift: float):
"""Create FlowMatchEulerDiscreteScheduler config."""
scheduler_config = {"_class_name": "FlowMatchEulerDiscreteScheduler", "num_train_timesteps": 1000, "shift": shift}
scheduler_path = os.path.join(output_path, "scheduler")
os.makedirs(scheduler_path, exist_ok=True)
with open(os.path.join(scheduler_path, "scheduler_config.json"), "w") as f:
json.dump(scheduler_config, f, indent=2)
print("✓ Created scheduler config")
def download_and_save_vae(vae_type: str, output_path: str):
"""Download and save VAE to local directory."""
from diffusers import AutoencoderDC, AutoencoderKL
vae_path = os.path.join(output_path, "vae")
os.makedirs(vae_path, exist_ok=True)
if vae_type == "flux":
print("Downloading FLUX VAE from black-forest-labs/FLUX.1-dev...")
vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae")
else: # dc-ae
print("Downloading DC-AE VAE from mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers...")
vae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers")
vae.save_pretrained(vae_path)
print(f"✓ Saved VAE to {vae_path}")
def download_and_save_text_encoder(output_path: str):
"""Download and save T5Gemma text encoder and tokenizer."""
from transformers import GemmaTokenizerFast
from transformers.models.t5gemma.modeling_t5gemma import T5GemmaModel
text_encoder_path = os.path.join(output_path, "text_encoder")
tokenizer_path = os.path.join(output_path, "tokenizer")
os.makedirs(text_encoder_path, exist_ok=True)
os.makedirs(tokenizer_path, exist_ok=True)
print("Downloading T5Gemma model from google/t5gemma-2b-2b-ul2...")
t5gemma_model = T5GemmaModel.from_pretrained("google/t5gemma-2b-2b-ul2")
# Extract and save only the encoder
t5gemma_encoder = t5gemma_model.encoder
t5gemma_encoder.save_pretrained(text_encoder_path)
print(f"✓ Saved T5GemmaEncoder to {text_encoder_path}")
print("Downloading tokenizer from google/t5gemma-2b-2b-ul2...")
tokenizer = GemmaTokenizerFast.from_pretrained("google/t5gemma-2b-2b-ul2")
tokenizer.model_max_length = 256
tokenizer.save_pretrained(tokenizer_path)
print(f"✓ Saved tokenizer to {tokenizer_path}")
def create_model_index(vae_type: str, default_image_size: int, output_path: str):
"""Create model_index.json for the pipeline."""
if vae_type == "flux":
vae_class = "AutoencoderKL"
else: # dc-ae
vae_class = "AutoencoderDC"
model_index = {
"_class_name": "PRXPipeline",
"_diffusers_version": "0.31.0.dev0",
"_name_or_path": os.path.basename(output_path),
"default_sample_size": default_image_size,
"scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
"text_encoder": ["prx", "T5GemmaEncoder"],
"tokenizer": ["transformers", "GemmaTokenizerFast"],
"transformer": ["diffusers", "PRXTransformer2DModel"],
"vae": ["diffusers", vae_class],
}
model_index_path = os.path.join(output_path, "model_index.json")
with open(model_index_path, "w") as f:
json.dump(model_index, f, indent=2)
def main(args):
# Validate inputs
if not os.path.exists(args.checkpoint_path):
raise FileNotFoundError(f"Checkpoint not found: {args.checkpoint_path}")
config = build_config(args.vae_type)
# Create output directory
os.makedirs(args.output_path, exist_ok=True)
print(f"✓ Output directory: {args.output_path}")
# Create transformer from checkpoint
transformer = create_transformer_from_checkpoint(args.checkpoint_path, config)
# Save transformer
transformer_path = os.path.join(args.output_path, "transformer")
os.makedirs(transformer_path, exist_ok=True)
# Save config
with open(os.path.join(transformer_path, "config.json"), "w") as f:
json.dump(config, f, indent=2)
# Save model weights as safetensors
state_dict = transformer.state_dict()
save_file(state_dict, os.path.join(transformer_path, "diffusion_pytorch_model.safetensors"))
print(f"✓ Saved transformer to {transformer_path}")
# Create scheduler config
create_scheduler_config(args.output_path, args.shift)
download_and_save_vae(args.vae_type, args.output_path)
download_and_save_text_encoder(args.output_path)
# Create model_index.json
create_model_index(args.vae_type, args.resolution, args.output_path)
# Verify the pipeline can be loaded
try:
pipeline = PRXPipeline.from_pretrained(args.output_path)
print("Pipeline loaded successfully!")
print(f"Transformer: {type(pipeline.transformer).__name__}")
print(f"VAE: {type(pipeline.vae).__name__}")
print(f"Text Encoder: {type(pipeline.text_encoder).__name__}")
print(f"Scheduler: {type(pipeline.scheduler).__name__}")
# Display model info
num_params = sum(p.numel() for p in pipeline.transformer.parameters())
print(f"✓ Transformer parameters: {num_params:,}")
except Exception as e:
print(f"Pipeline verification failed: {e}")
return False
print("Conversion completed successfully!")
print(f"Converted pipeline saved to: {args.output_path}")
print(f"VAE type: {args.vae_type}")
return True
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Convert PRX checkpoint to diffusers format")
parser.add_argument(
"--checkpoint_path", type=str, required=True, help="Path to the original PRX checkpoint (.pth file )"
)
parser.add_argument(
"--output_path", type=str, required=True, help="Output directory for the converted diffusers pipeline"
)
parser.add_argument(
"--vae_type",
type=str,
choices=["flux", "dc-ae"],
required=True,
help="VAE type to use: 'flux' for AutoencoderKL (16 channels) or 'dc-ae' for AutoencoderDC (32 channels)",
)
parser.add_argument(
"--resolution",
type=int,
choices=[256, 512, 1024],
default=DEFAULT_RESOLUTION,
help="Target resolution for the model (256, 512, or 1024). Affects the transformer's sample_size.",
)
parser.add_argument(
"--shift",
type=float,
default=3.0,
help="Shift for the scheduler",
)
args = parser.parse_args()
try:
success = main(args)
if not success:
sys.exit(1)
except Exception as e:
print(f"Conversion failed: {e}")
import traceback
traceback.print_exc()
sys.exit(1)
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