| import json
|
| import os
|
| from contextlib import nullcontext
|
|
|
| import torch
|
| from safetensors.torch import load_file
|
| from transformers import AutoTokenizer, T5EncoderModel
|
|
|
| from diffusers import (
|
| AutoencoderOobleck,
|
| CosineDPMSolverMultistepScheduler,
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| StableAudioDiTModel,
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| StableAudioPipeline,
|
| StableAudioProjectionModel,
|
| )
|
| from diffusers.models.model_loading_utils import load_model_dict_into_meta
|
| from diffusers.utils import is_accelerate_available
|
|
|
| if is_accelerate_available():
|
| from accelerate import init_empty_weights
|
|
|
|
|
|
|
|
|
| CHECKPOINT_PATH = r"\Foundation_1.safetensors"
|
| CONFIG_PATH = r"\model_config.json"
|
| SAVE_DIRECTORY = r"\foundation_diffusers"
|
|
|
| device = "cpu"
|
| dtype = torch.float32
|
|
|
|
|
|
|
| def convert_stable_audio_state_dict_to_diffusers(state_dict, num_autoencoder_layers=5):
|
| projection_model_state_dict = {
|
| k.replace("conditioner.conditioners.", "").replace("embedder.embedding", "time_positional_embedding"): v
|
| for (k, v) in state_dict.items()
|
| if "conditioner.conditioners" in k
|
| }
|
|
|
| for key, value in list(projection_model_state_dict.items()):
|
| new_key = key.replace("seconds_start", "start_number_conditioner").replace(
|
| "seconds_total", "end_number_conditioner"
|
| )
|
| projection_model_state_dict[new_key] = projection_model_state_dict.pop(key)
|
|
|
| model_state_dict = {k.replace("model.model.", ""): v for (k, v) in state_dict.items() if "model.model." in k}
|
| for key, value in list(model_state_dict.items()):
|
| new_key = (
|
| key.replace("transformer.", "")
|
| .replace("layers", "transformer_blocks")
|
| .replace("self_attn", "attn1")
|
| .replace("cross_attn", "attn2")
|
| .replace("ff.ff", "ff.net")
|
| )
|
| new_key = (
|
| new_key.replace("pre_norm", "norm1")
|
| .replace("cross_attend_norm", "norm2")
|
| .replace("ff_norm", "norm3")
|
| .replace("to_out", "to_out.0")
|
| )
|
| new_key = new_key.replace("gamma", "weight").replace("beta", "bias")
|
|
|
| new_key = (
|
| new_key.replace("project", "proj")
|
| .replace("to_timestep_embed", "timestep_proj")
|
| .replace("timestep_features", "time_proj")
|
| .replace("to_global_embed", "global_proj")
|
| .replace("to_cond_embed", "cross_attention_proj")
|
| )
|
|
|
| if new_key == "time_proj.weight":
|
| model_state_dict[key] = model_state_dict[key].squeeze(1)
|
|
|
| if "to_qkv" in new_key:
|
| q, k, v = torch.chunk(model_state_dict.pop(key), 3, dim=0)
|
| model_state_dict[new_key.replace("qkv", "q")] = q
|
| model_state_dict[new_key.replace("qkv", "k")] = k
|
| model_state_dict[new_key.replace("qkv", "v")] = v
|
| elif "to_kv" in new_key:
|
| k, v = torch.chunk(model_state_dict.pop(key), 2, dim=0)
|
| model_state_dict[new_key.replace("kv", "k")] = k
|
| model_state_dict[new_key.replace("kv", "v")] = v
|
| else:
|
| model_state_dict[new_key] = model_state_dict.pop(key)
|
|
|
| autoencoder_state_dict = {
|
| k.replace("pretransform.model.", "").replace("coder.layers.0", "coder.conv1"): v
|
| for (k, v) in state_dict.items()
|
| if "pretransform.model." in k
|
| }
|
|
|
| for key, _ in list(autoencoder_state_dict.items()):
|
| new_key = key
|
| if "coder.layers" in new_key:
|
| idx = int(new_key.split("coder.layers.")[1].split(".")[0])
|
| new_key = new_key.replace(f"coder.layers.{idx}", f"coder.block.{idx - 1}")
|
|
|
| if "encoder" in new_key:
|
| for i in range(3):
|
| new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i + 1}")
|
| new_key = new_key.replace(f"block.{idx - 1}.layers.3", f"block.{idx - 1}.snake1")
|
| new_key = new_key.replace(f"block.{idx - 1}.layers.4", f"block.{idx - 1}.conv1")
|
| else:
|
| for i in range(2, 5):
|
| new_key = new_key.replace(f"block.{idx - 1}.layers.{i}", f"block.{idx - 1}.res_unit{i - 1}")
|
| new_key = new_key.replace(f"block.{idx - 1}.layers.0", f"block.{idx - 1}.snake1")
|
| new_key = new_key.replace(f"block.{idx - 1}.layers.1", f"block.{idx - 1}.conv_t1")
|
|
|
| new_key = new_key.replace("layers.0.beta", "snake1.beta")
|
| new_key = new_key.replace("layers.0.alpha", "snake1.alpha")
|
| new_key = new_key.replace("layers.2.beta", "snake2.beta")
|
| new_key = new_key.replace("layers.2.alpha", "snake2.alpha")
|
| new_key = new_key.replace("layers.1.bias", "conv1.bias")
|
| new_key = new_key.replace("layers.1.weight_", "conv1.weight_")
|
| new_key = new_key.replace("layers.3.bias", "conv2.bias")
|
| new_key = new_key.replace("layers.3.weight_", "conv2.weight_")
|
|
|
| if idx == num_autoencoder_layers + 1:
|
| new_key = new_key.replace(f"block.{idx - 1}", "snake1")
|
| elif idx == num_autoencoder_layers + 2:
|
| new_key = new_key.replace(f"block.{idx - 1}", "conv2")
|
|
|
| value = autoencoder_state_dict.pop(key)
|
| if "snake" in new_key:
|
| value = value.unsqueeze(0).unsqueeze(-1)
|
| if new_key in autoencoder_state_dict:
|
| raise ValueError(f"{new_key} already in state dict.")
|
| autoencoder_state_dict[new_key] = value
|
|
|
| return model_state_dict, projection_model_state_dict, autoencoder_state_dict
|
|
|
| print("Reading config...")
|
| with open(CONFIG_PATH) as f_in:
|
| config_dict = json.load(f_in)
|
|
|
| conditioning_dict = {
|
| conditioning["id"]: conditioning["config"] for conditioning in config_dict["model"]["conditioning"]["configs"]
|
| }
|
|
|
| t5_model_config = conditioning_dict["prompt"]
|
|
|
| print("Downloading/Loading T5 text encoder...")
|
| text_encoder = T5EncoderModel.from_pretrained(t5_model_config["t5_model_name"])
|
| tokenizer = AutoTokenizer.from_pretrained(
|
| t5_model_config["t5_model_name"], truncation=True, model_max_length=t5_model_config["max_length"]
|
| )
|
|
|
| scheduler = CosineDPMSolverMultistepScheduler(
|
| sigma_min=0.3,
|
| sigma_max=500,
|
| solver_order=2,
|
| prediction_type="v_prediction",
|
| sigma_data=1.0,
|
| sigma_schedule="exponential",
|
| )
|
| ctx = init_empty_weights if is_accelerate_available() else nullcontext
|
|
|
| print("Loading SafeTensors checkpoint...")
|
| orig_state_dict = load_file(CHECKPOINT_PATH, device=device)
|
|
|
| model_config = config_dict["model"]["diffusion"]["config"]
|
|
|
| print("Converting weights (this might take a moment)...")
|
| model_state_dict, projection_model_state_dict, autoencoder_state_dict = convert_stable_audio_state_dict_to_diffusers(
|
| orig_state_dict
|
| )
|
|
|
| print("Building Models...")
|
| with ctx():
|
| projection_model = StableAudioProjectionModel(
|
| text_encoder_dim=text_encoder.config.d_model,
|
| conditioning_dim=config_dict["model"]["conditioning"]["cond_dim"],
|
| min_value=conditioning_dict["seconds_start"]["min_val"],
|
| max_value=conditioning_dict["seconds_start"]["max_val"],
|
| )
|
| if is_accelerate_available():
|
| load_model_dict_into_meta(projection_model, projection_model_state_dict)
|
| else:
|
| projection_model.load_state_dict(projection_model_state_dict)
|
|
|
| attention_head_dim = model_config["embed_dim"] // model_config["num_heads"]
|
| with ctx():
|
| model = StableAudioDiTModel(
|
| sample_size=int(config_dict["sample_size"])
|
| / int(config_dict["model"]["pretransform"]["config"]["downsampling_ratio"]),
|
| in_channels=model_config["io_channels"],
|
| num_layers=model_config["depth"],
|
| attention_head_dim=attention_head_dim,
|
| num_key_value_attention_heads=model_config["cond_token_dim"] // attention_head_dim,
|
| num_attention_heads=model_config["num_heads"],
|
| out_channels=model_config["io_channels"],
|
| cross_attention_dim=model_config["cond_token_dim"],
|
| time_proj_dim=256,
|
| global_states_input_dim=model_config["global_cond_dim"],
|
| cross_attention_input_dim=model_config["cond_token_dim"],
|
| )
|
| if is_accelerate_available():
|
| load_model_dict_into_meta(model, model_state_dict)
|
| else:
|
| model.load_state_dict(model_state_dict)
|
|
|
| autoencoder_config = config_dict["model"]["pretransform"]["config"]
|
| with ctx():
|
| autoencoder = AutoencoderOobleck(
|
| encoder_hidden_size=autoencoder_config["encoder"]["config"]["channels"],
|
| downsampling_ratios=autoencoder_config["encoder"]["config"]["strides"],
|
| decoder_channels=autoencoder_config["decoder"]["config"]["channels"],
|
| decoder_input_channels=autoencoder_config["decoder"]["config"]["latent_dim"],
|
| audio_channels=autoencoder_config["io_channels"],
|
| channel_multiples=autoencoder_config["encoder"]["config"]["c_mults"],
|
| sampling_rate=config_dict["sample_rate"],
|
| )
|
|
|
| if is_accelerate_available():
|
| load_model_dict_into_meta(autoencoder, autoencoder_state_dict)
|
| else:
|
| autoencoder.load_state_dict(autoencoder_state_dict)
|
|
|
| print("Saving final diffusers pipeline...")
|
| os.makedirs(SAVE_DIRECTORY, exist_ok=True)
|
| pipeline = StableAudioPipeline(
|
| transformer=model,
|
| tokenizer=tokenizer,
|
| text_encoder=text_encoder,
|
| scheduler=scheduler,
|
| vae=autoencoder,
|
| projection_model=projection_model,
|
| )
|
|
|
| pipeline.to(dtype).save_pretrained(SAVE_DIRECTORY)
|
|
|
| print(f"✅ DONE! Pipeline successfully saved to {SAVE_DIRECTORY}") |
