Convert weights and config

convert_original_audioldm_to_diffusers.py

@@ -0,0 +1,1015 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# 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.
+""" Conversion script for the AudioLDM checkpoints."""
+
+import argparse
+import re
+
+import torch
+from transformers import (
+    AutoTokenizer,
+    ClapTextConfig,
+    ClapTextModelWithProjection,
+    SpeechT5HifiGan,
+    SpeechT5HifiGanConfig,
+)
+
+from diffusers import (
+    AudioLDMPipeline,
+    AutoencoderKL,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    UNet2DConditionModel,
+)
+from diffusers.utils import is_omegaconf_available, is_safetensors_available
+from diffusers.utils.import_utils import BACKENDS_MAPPING
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.shave_segments
+def shave_segments(path, n_shave_prefix_segments=1):
+    """
+    Removes segments. Positive values shave the first segments, negative shave the last segments.
+    """
+    if n_shave_prefix_segments >= 0:
+        return ".".join(path.split(".")[n_shave_prefix_segments:])
+    else:
+        return ".".join(path.split(".")[:n_shave_prefix_segments])
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_resnet_paths
+def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item.replace("in_layers.0", "norm1")
+        new_item = new_item.replace("in_layers.2", "conv1")
+
+        new_item = new_item.replace("out_layers.0", "norm2")
+        new_item = new_item.replace("out_layers.3", "conv2")
+
+        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
+        new_item = new_item.replace("skip_connection", "conv_shortcut")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_resnet_paths
+def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_attention_paths
+def renew_attention_paths(old_list):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
+        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
+
+        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
+        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
+
+        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.renew_vae_attention_paths
+def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("norm.weight", "group_norm.weight")
+        new_item = new_item.replace("norm.bias", "group_norm.bias")
+
+        new_item = new_item.replace("q.weight", "query.weight")
+        new_item = new_item.replace("q.bias", "query.bias")
+
+        new_item = new_item.replace("k.weight", "key.weight")
+        new_item = new_item.replace("k.bias", "key.bias")
+
+        new_item = new_item.replace("v.weight", "value.weight")
+        new_item = new_item.replace("v.bias", "value.bias")
+
+        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
+        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.assign_to_checkpoint
+def assign_to_checkpoint(
+    paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
+):
+    """
+    This does the final conversion step: take locally converted weights and apply a global renaming to them. It splits
+    attention layers, and takes into account additional replacements that may arise.
+
+    Assigns the weights to the new checkpoint.
+    """
+    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
+
+    # Splits the attention layers into three variables.
+    if attention_paths_to_split is not None:
+        for path, path_map in attention_paths_to_split.items():
+            old_tensor = old_checkpoint[path]
+            channels = old_tensor.shape[0] // 3
+
+            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
+
+            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
+
+            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
+            query, key, value = old_tensor.split(channels // num_heads, dim=1)
+
+            checkpoint[path_map["query"]] = query.reshape(target_shape)
+            checkpoint[path_map["key"]] = key.reshape(target_shape)
+            checkpoint[path_map["value"]] = value.reshape(target_shape)
+
+    for path in paths:
+        new_path = path["new"]
+
+        # These have already been assigned
+        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
+            continue
+
+        # Global renaming happens here
+        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
+        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
+        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
+
+        if additional_replacements is not None:
+            for replacement in additional_replacements:
+                new_path = new_path.replace(replacement["old"], replacement["new"])
+
+        # proj_attn.weight has to be converted from conv 1D to linear
+        if "proj_attn.weight" in new_path:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
+        else:
+            checkpoint[new_path] = old_checkpoint[path["old"]]
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.conv_attn_to_linear
+def conv_attn_to_linear(checkpoint):
+    keys = list(checkpoint.keys())
+    attn_keys = ["query.weight", "key.weight", "value.weight"]
+    for key in keys:
+        if ".".join(key.split(".")[-2:]) in attn_keys:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0, 0]
+        elif "proj_attn.weight" in key:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0]
+
+
+def create_unet_diffusers_config(original_config, image_size: int):
+    """
+    Creates a UNet config for diffusers based on the config of the original AudioLDM model.
+    """
+    unet_params = original_config.model.params.unet_config.params
+    vae_params = original_config.model.params.first_stage_config.params.ddconfig
+
+    block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
+
+    down_block_types = []
+    resolution = 1
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
+        down_block_types.append(block_type)
+        if i != len(block_out_channels) - 1:
+            resolution *= 2
+
+    up_block_types = []
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
+        up_block_types.append(block_type)
+        resolution //= 2
+
+    vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
+
+    cross_attention_dim = (
+        unet_params.cross_attention_dim if "cross_attention_dim" in unet_params else block_out_channels
+    )
+
+    class_embed_type = "simple_projection" if "extra_film_condition_dim" in unet_params else None
+    projection_class_embeddings_input_dim = (
+        unet_params.extra_film_condition_dim if "extra_film_condition_dim" in unet_params else None
+    )
+    class_embeddings_concat = unet_params.extra_film_use_concat if "extra_film_use_concat" in unet_params else None
+
+    config = {
+        "sample_size": image_size // vae_scale_factor,
+        "in_channels": unet_params.in_channels,
+        "out_channels": unet_params.out_channels,
+        "down_block_types": tuple(down_block_types),
+        "up_block_types": tuple(up_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "layers_per_block": unet_params.num_res_blocks,
+        "cross_attention_dim": cross_attention_dim,
+        "class_embed_type": class_embed_type,
+        "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim,
+        "class_embeddings_concat": class_embeddings_concat,
+    }
+
+    return config
+
+
+# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_vae_diffusers_config
+def create_vae_diffusers_config(original_config, checkpoint, image_size: int):
+    """
+    Creates a VAE config for diffusers based on the config of the original AudioLDM model. Compared to the original
+    Stable Diffusion conversion, this function passes a *learnt* VAE scaling factor to the diffusers VAE.
+    """
+    vae_params = original_config.model.params.first_stage_config.params.ddconfig
+    _ = original_config.model.params.first_stage_config.params.embed_dim
+
+    block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
+    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
+    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
+
+    scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config.model.params else 0.18215
+
+    config = {
+        "sample_size": image_size,
+        "in_channels": vae_params.in_channels,
+        "out_channels": vae_params.out_ch,
+        "down_block_types": tuple(down_block_types),
+        "up_block_types": tuple(up_block_types),
+        "block_out_channels": tuple(block_out_channels),
+        "latent_channels": vae_params.z_channels,
+        "layers_per_block": vae_params.num_res_blocks,
+        "scaling_factor": float(scaling_factor),
+    }
+    return config
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.create_diffusers_schedular
+def create_diffusers_schedular(original_config):
+    schedular = DDIMScheduler(
+        num_train_timesteps=original_config.model.params.timesteps,
+        beta_start=original_config.model.params.linear_start,
+        beta_end=original_config.model.params.linear_end,
+        beta_schedule="scaled_linear",
+    )
+    return schedular
+
+
+# Adapted from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_unet_checkpoint
+def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):
+    """
+    Takes a state dict and a config, and returns a converted checkpoint. Compared to the original Stable Diffusion
+    conversion, this function additionally converts the learnt film embedding linear layer.
+    """
+
+    # extract state_dict for UNet
+    unet_state_dict = {}
+    keys = list(checkpoint.keys())
+
+    unet_key = "model.diffusion_model."
+    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
+    if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
+        print(f"Checkpoint {path} has both EMA and non-EMA weights.")
+        print(
+            "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
+            " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
+        )
+        for key in keys:
+            if key.startswith("model.diffusion_model"):
+                flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
+                unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
+    else:
+        if sum(k.startswith("model_ema") for k in keys) > 100:
+            print(
+                "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
+                " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
+            )
+
+        for key in keys:
+            if key.startswith(unet_key):
+                unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
+    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
+    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
+    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
+
+    new_checkpoint["class_embedding.weight"] = unet_state_dict["film_emb.weight"]
+    new_checkpoint["class_embedding.bias"] = unet_state_dict["film_emb.bias"]
+
+    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
+    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
+
+    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
+    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
+    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
+    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
+
+    # Retrieves the keys for the input blocks only
+    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
+    input_blocks = {
+        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
+        for layer_id in range(num_input_blocks)
+    }
+
+    # Retrieves the keys for the middle blocks only
+    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
+    middle_blocks = {
+        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
+        for layer_id in range(num_middle_blocks)
+    }
+
+    # Retrieves the keys for the output blocks only
+    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
+    output_blocks = {
+        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
+        for layer_id in range(num_output_blocks)
+    }
+
+    for i in range(1, num_input_blocks):
+        block_id = (i - 1) // (config["layers_per_block"] + 1)
+        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
+
+        resnets = [
+            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+        ]
+        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
+
+        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.weight"
+            )
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.bias"
+            )
+
+        paths = renew_resnet_paths(resnets)
+        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
+        assign_to_checkpoint(
+            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+        )
+
+        if len(attentions):
+            paths = renew_attention_paths(attentions)
+            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+    resnet_0 = middle_blocks[0]
+    attentions = middle_blocks[1]
+    resnet_1 = middle_blocks[2]
+
+    resnet_0_paths = renew_resnet_paths(resnet_0)
+    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
+
+    resnet_1_paths = renew_resnet_paths(resnet_1)
+    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
+
+    attentions_paths = renew_attention_paths(attentions)
+    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(
+        attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+    )
+
+    for i in range(num_output_blocks):
+        block_id = i // (config["layers_per_block"] + 1)
+        layer_in_block_id = i % (config["layers_per_block"] + 1)
+        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
+        output_block_list = {}
+
+        for layer in output_block_layers:
+            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
+            if layer_id in output_block_list:
+                output_block_list[layer_id].append(layer_name)
+            else:
+                output_block_list[layer_id] = [layer_name]
+
+        if len(output_block_list) > 1:
+            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
+            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
+
+            resnet_0_paths = renew_resnet_paths(resnets)
+            paths = renew_resnet_paths(resnets)
+
+            meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+            output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
+            if ["conv.bias", "conv.weight"] in output_block_list.values():
+                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.weight"
+                ]
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.bias"
+                ]
+
+                # Clear attentions as they have been attributed above.
+                if len(attentions) == 2:
+                    attentions = []
+
+            if len(attentions):
+                paths = renew_attention_paths(attentions)
+                meta_path = {
+                    "old": f"output_blocks.{i}.1",
+                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
+                }
+                assign_to_checkpoint(
+                    paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+                )
+        else:
+            resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
+            for path in resnet_0_paths:
+                old_path = ".".join(["output_blocks", str(i), path["old"]])
+                new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
+
+                new_checkpoint[new_path] = unet_state_dict[old_path]
+
+    return new_checkpoint
+
+
+# Copied from diffusers.pipelines.stable_diffusion.convert_from_ckpt.convert_ldm_vae_checkpoint
+def convert_ldm_vae_checkpoint(checkpoint, config):
+    # extract state dict for VAE
+    vae_state_dict = {}
+    vae_key = "first_stage_model."
+    keys = list(checkpoint.keys())
+    for key in keys:
+        if key.startswith(vae_key):
+            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
+    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
+    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
+    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
+    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
+    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
+
+    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
+    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
+    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
+    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
+    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
+    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
+
+    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
+    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
+    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
+    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
+
+    # Retrieves the keys for the encoder down blocks only
+    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
+    down_blocks = {
+        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
+    }
+
+    # Retrieves the keys for the decoder up blocks only
+    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
+    up_blocks = {
+        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
+    }
+
+    for i in range(num_down_blocks):
+        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
+
+        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.weight"
+            )
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.bias"
+            )
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+
+    for i in range(num_up_blocks):
+        block_id = num_up_blocks - 1 - i
+        resnets = [
+            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+        ]
+
+        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.weight"
+            ]
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.bias"
+            ]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+    return new_checkpoint
+
+
+CLAP_KEYS_TO_MODIFY_MAPPING = {
+    "text_branch": "text_model",
+    "attn": "attention.self",
+    "self.proj": "output.dense",
+    "attention.self_mask": "attn_mask",
+    "mlp.fc1": "intermediate.dense",
+    "mlp.fc2": "output.dense",
+    "norm1": "layernorm_before",
+    "norm2": "layernorm_after",
+    "bn0": "batch_norm",
+}
+
+CLAP_KEYS_TO_IGNORE = ["text_transform"]
+
+CLAP_EXPECTED_MISSING_KEYS = ["text_model.embeddings.token_type_ids"]
+
+
+def convert_open_clap_checkpoint(checkpoint):
+    """
+    Takes a state dict and returns a converted CLAP checkpoint.
+    """
+    # extract state dict for CLAP text embedding model, discarding the audio component
+    model_state_dict = {}
+    model_key = "cond_stage_model.model.text_"
+    keys = list(checkpoint.keys())
+    for key in keys:
+        if key.startswith(model_key):
+            model_state_dict[key.replace(model_key, "text_")] = checkpoint.get(key)
+
+    new_checkpoint = {}
+
+    sequential_layers_pattern = r".*sequential.(\d+).*"
+    text_projection_pattern = r".*_projection.(\d+).*"
+
+    for key, value in model_state_dict.items():
+        # check if key should be ignored in mapping
+        if key.split(".")[0] in CLAP_KEYS_TO_IGNORE:
+            continue
+
+        # check if any key needs to be modified
+        for key_to_modify, new_key in CLAP_KEYS_TO_MODIFY_MAPPING.items():
+            if key_to_modify in key:
+                key = key.replace(key_to_modify, new_key)
+
+        if re.match(sequential_layers_pattern, key):
+            # replace sequential layers with list
+            sequential_layer = re.match(sequential_layers_pattern, key).group(1)
+
+            key = key.replace(f"sequential.{sequential_layer}.", f"layers.{int(sequential_layer)//3}.linear.")
+        elif re.match(text_projection_pattern, key):
+            projecton_layer = int(re.match(text_projection_pattern, key).group(1))
+
+            # Because in CLAP they use `nn.Sequential`...
+            transformers_projection_layer = 1 if projecton_layer == 0 else 2
+
+            key = key.replace(f"_projection.{projecton_layer}.", f"_projection.linear{transformers_projection_layer}.")
+
+        if "audio" and "qkv" in key:
+            # split qkv into query key and value
+            mixed_qkv = value
+            qkv_dim = mixed_qkv.size(0) // 3
+
+            query_layer = mixed_qkv[:qkv_dim]
+            key_layer = mixed_qkv[qkv_dim : qkv_dim * 2]
+            value_layer = mixed_qkv[qkv_dim * 2 :]
+
+            new_checkpoint[key.replace("qkv", "query")] = query_layer
+            new_checkpoint[key.replace("qkv", "key")] = key_layer
+            new_checkpoint[key.replace("qkv", "value")] = value_layer
+        else:
+            new_checkpoint[key] = value
+
+    return new_checkpoint
+
+
+def create_transformers_vocoder_config(original_config):
+    """
+    Creates a config for transformers SpeechT5HifiGan based on the config of the vocoder model.
+    """
+    vocoder_params = original_config.model.params.vocoder_config.params
+
+    config = {
+        "model_in_dim": vocoder_params.num_mels,
+        "sampling_rate": vocoder_params.sampling_rate,
+        "upsample_initial_channel": vocoder_params.upsample_initial_channel,
+        "upsample_rates": list(vocoder_params.upsample_rates),
+        "upsample_kernel_sizes": list(vocoder_params.upsample_kernel_sizes),
+        "resblock_kernel_sizes": list(vocoder_params.resblock_kernel_sizes),
+        "resblock_dilation_sizes": [
+            list(resblock_dilation) for resblock_dilation in vocoder_params.resblock_dilation_sizes
+        ],
+        "normalize_before": False,
+    }
+
+    return config
+
+
+def convert_hifigan_checkpoint(checkpoint, config):
+    """
+    Takes a state dict and config, and returns a converted HiFiGAN vocoder checkpoint.
+    """
+    # extract state dict for vocoder
+    vocoder_state_dict = {}
+    vocoder_key = "first_stage_model.vocoder."
+    keys = list(checkpoint.keys())
+    for key in keys:
+        if key.startswith(vocoder_key):
+            vocoder_state_dict[key.replace(vocoder_key, "")] = checkpoint.get(key)
+
+    # fix upsampler keys, everything else is correct already
+    for i in range(len(config.upsample_rates)):
+        vocoder_state_dict[f"upsampler.{i}.weight"] = vocoder_state_dict.pop(f"ups.{i}.weight")
+        vocoder_state_dict[f"upsampler.{i}.bias"] = vocoder_state_dict.pop(f"ups.{i}.bias")
+
+    if not config.normalize_before:
+        # if we don't set normalize_before then these variables are unused, so we set them to their initialised values
+        vocoder_state_dict["mean"] = torch.zeros(config.model_in_dim)
+        vocoder_state_dict["scale"] = torch.ones(config.model_in_dim)
+
+    return vocoder_state_dict
+
+
+# Adapted from https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation/blob/84a0384742a22bd80c44e903e241f0623e874f1d/audioldm/utils.py#L72-L73
+DEFAULT_CONFIG = {
+    "model": {
+        "params": {
+            "linear_start": 0.0015,
+            "linear_end": 0.0195,
+            "timesteps": 1000,
+            "channels": 8,
+            "scale_by_std": True,
+            "unet_config": {
+                "target": "audioldm.latent_diffusion.openaimodel.UNetModel",
+                "params": {
+                    "extra_film_condition_dim": 512,
+                    "extra_film_use_concat": True,
+                    "in_channels": 8,
+                    "out_channels": 8,
+                    "model_channels": 256,
+                    "attention_resolutions": [8, 4, 2],
+                    "num_res_blocks": 2,
+                    "channel_mult": [1, 2, 3, 5],
+                    "num_head_channels": 64,
+                },
+            },
+            "first_stage_config": {
+                "target": "audioldm.variational_autoencoder.autoencoder.AutoencoderKL",
+                "params": {
+                    "embed_dim": 8,
+                    "ddconfig": {
+                        "z_channels": 8,
+                        "resolution": 256,
+                        "in_channels": 1,
+                        "out_ch": 1,
+                        "ch": 128,
+                        "ch_mult": [1, 2, 4],
+                        "num_res_blocks": 2,
+                    },
+                },
+            },
+            "vocoder_config": {
+                "target": "audioldm.first_stage_model.vocoder",
+                "params": {
+                    "upsample_rates": [5, 4, 2, 2, 2],
+                    "upsample_kernel_sizes": [16, 16, 8, 4, 4],
+                    "upsample_initial_channel": 1024,
+                    "resblock_kernel_sizes": [3, 7, 11],
+                    "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+                    "num_mels": 64,
+                    "sampling_rate": 16000,
+                },
+            },
+        },
+    },
+}
+
+
+def load_pipeline_from_original_audioldm_ckpt(
+    checkpoint_path: str,
+    original_config_file: str = None,
+    image_size: int = 512,
+    prediction_type: str = None,
+    extract_ema: bool = False,
+    scheduler_type: str = "ddim",
+    num_in_channels: int = None,
+    device: str = None,
+    from_safetensors: bool = False,
+) -> AudioLDMPipeline:
+    """
+    Load an AudioLDM pipeline object from a `.ckpt`/`.safetensors` file and (ideally) a `.yaml` config file.
+
+    Although many of the arguments can be automatically inferred, some of these rely on brittle checks against the
+    global step count, which will likely fail for models that have undergone further fine-tuning. Therefore, it is
+    recommended that you override the default values and/or supply an `original_config_file` wherever possible.
+
+    :param checkpoint_path: Path to `.ckpt` file. :param original_config_file: Path to `.yaml` config file
+    corresponding to the original architecture.
+            If `None`, will be automatically instantiated based on default values.
+    :param image_size: The image size that the model was trained on. Use 512 for original AudioLDM checkpoints. :param
+    prediction_type: The prediction type that the model was trained on. Use `'epsilon'` for original
+            AudioLDM checkpoints.
+    :param num_in_channels: The number of input channels. If `None` number of input channels will be automatically
+            inferred.
+    :param scheduler_type: Type of scheduler to use. Should be one of `["pndm", "lms", "heun", "euler",
+            "euler-ancestral", "dpm", "ddim"]`.
+    :param extract_ema: Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract
+            the EMA weights or not. Defaults to `False`. Pass `True` to extract the EMA weights. EMA weights usually
+            yield higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.
+    :param device: The device to use. Pass `None` to determine automatically. :param from_safetensors: If
+    `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors
+            instead of PyTorch.
+    :return: An AudioLDMPipeline object representing the passed-in `.ckpt`/`.safetensors` file.
+    """
+
+    if not is_omegaconf_available():
+        raise ValueError(BACKENDS_MAPPING["omegaconf"][1])
+
+    from omegaconf import OmegaConf
+
+    if from_safetensors:
+        if not is_safetensors_available():
+            raise ValueError(BACKENDS_MAPPING["safetensors"][1])
+
+        from safetensors import safe_open
+
+        checkpoint = {}
+        with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                checkpoint[key] = f.get_tensor(key)
+    else:
+        if device is None:
+            device = "cuda" if torch.cuda.is_available() else "cpu"
+            checkpoint = torch.load(checkpoint_path, map_location=device)
+        else:
+            checkpoint = torch.load(checkpoint_path, map_location=device)
+
+    if "state_dict" in checkpoint:
+        checkpoint = checkpoint["state_dict"]
+
+    if original_config_file is None:
+        original_config = DEFAULT_CONFIG
+        original_config = OmegaConf.create(original_config)
+    else:
+        original_config = OmegaConf.load(original_config_file)
+
+    if num_in_channels is not None:
+        original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
+
+    if (
+        "parameterization" in original_config["model"]["params"]
+        and original_config["model"]["params"]["parameterization"] == "v"
+    ):
+        if prediction_type is None:
+            prediction_type = "v_prediction"
+    else:
+        if prediction_type is None:
+            prediction_type = "epsilon"
+
+    if image_size is None:
+        image_size = 512
+
+    num_train_timesteps = original_config.model.params.timesteps
+    beta_start = original_config.model.params.linear_start
+    beta_end = original_config.model.params.linear_end
+
+    scheduler = DDIMScheduler(
+        beta_end=beta_end,
+        beta_schedule="scaled_linear",
+        beta_start=beta_start,
+        num_train_timesteps=num_train_timesteps,
+        steps_offset=1,
+        clip_sample=False,
+        set_alpha_to_one=False,
+        prediction_type=prediction_type,
+    )
+    # make sure scheduler works correctly with DDIM
+    scheduler.register_to_config(clip_sample=False)
+
+    if scheduler_type == "pndm":
+        config = dict(scheduler.config)
+        config["skip_prk_steps"] = True
+        scheduler = PNDMScheduler.from_config(config)
+    elif scheduler_type == "lms":
+        scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "heun":
+        scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler":
+        scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler-ancestral":
+        scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "dpm":
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+    elif scheduler_type == "ddim":
+        scheduler = scheduler
+    else:
+        raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
+
+    # Convert the UNet2DModel
+    unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
+    unet = UNet2DConditionModel(**unet_config)
+
+    converted_unet_checkpoint = convert_ldm_unet_checkpoint(
+        checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
+    )
+
+    unet.load_state_dict(converted_unet_checkpoint)
+
+    # Convert the VAE model
+    vae_config = create_vae_diffusers_config(original_config, checkpoint=checkpoint, image_size=image_size)
+    converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
+
+    vae = AutoencoderKL(**vae_config)
+    vae.load_state_dict(converted_vae_checkpoint)
+
+    # Convert the text model
+    # AudioLDM uses the same configuration and tokenizer as the original CLAP model
+    config = ClapTextConfig.from_pretrained("laion/clap-htsat-unfused")
+    tokenizer = AutoTokenizer.from_pretrained("laion/clap-htsat-unfused")
+
+    converted_text_model = convert_open_clap_checkpoint(checkpoint)
+    text_model = ClapTextModelWithProjection(config)
+
+    missing_keys, unexpected_keys = text_model.load_state_dict(converted_text_model, strict=False)
+    # we expect not to have token_type_ids in our original state dict so let's ignore them
+    missing_keys = list(set(missing_keys) - set(CLAP_EXPECTED_MISSING_KEYS))
+
+    if len(unexpected_keys) > 0:
+        raise ValueError(f"Unexpected keys when loading CLAP model: {unexpected_keys}")
+
+    if len(missing_keys) > 0:
+        raise ValueError(f"Missing keys when loading CLAP model: {missing_keys}")
+
+    # Convert the vocoder model
+    vocoder_config = create_transformers_vocoder_config(original_config)
+    vocoder_config = SpeechT5HifiGanConfig(**vocoder_config)
+    converted_vocoder_checkpoint = convert_hifigan_checkpoint(checkpoint, vocoder_config)
+
+    vocoder = SpeechT5HifiGan(vocoder_config)
+    vocoder.load_state_dict(converted_vocoder_checkpoint)
+
+    # Instantiate the diffusers pipeline
+    pipe = AudioLDMPipeline(
+        vae=vae,
+        text_encoder=text_model,
+        tokenizer=tokenizer,
+        unet=unet,
+        scheduler=scheduler,
+        vocoder=vocoder,
+    )
+
+    return pipe
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
+    )
+    parser.add_argument(
+        "--original_config_file",
+        default=None,
+        type=str,
+        help="The YAML config file corresponding to the original architecture.",
+    )
+    parser.add_argument(
+        "--num_in_channels",
+        default=None,
+        type=int,
+        help="The number of input channels. If `None` number of input channels will be automatically inferred.",
+    )
+    parser.add_argument(
+        "--scheduler_type",
+        default="ddim",
+        type=str,
+        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
+    )
+    parser.add_argument(
+        "--image_size",
+        default=None,
+        type=int,
+        help=("The image size that the model was trained on."),
+    )
+    parser.add_argument(
+        "--prediction_type",
+        default=None,
+        type=str,
+        help=("The prediction type that the model was trained on."),
+    )
+    parser.add_argument(
+        "--extract_ema",
+        action="store_true",
+        help=(
+            "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
+            " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
+            " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
+        ),
+    )
+    parser.add_argument(
+        "--from_safetensors",
+        action="store_true",
+        help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
+    )
+    parser.add_argument(
+        "--to_safetensors",
+        action="store_true",
+        help="Whether to store pipeline in safetensors format or not.",
+    )
+    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
+    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
+    args = parser.parse_args()
+
+    pipe = load_pipeline_from_original_audioldm_ckpt(
+        checkpoint_path=args.checkpoint_path,
+        original_config_file=args.original_config_file,
+        image_size=args.image_size,
+        prediction_type=args.prediction_type,
+        extract_ema=args.extract_ema,
+        scheduler_type=args.scheduler_type,
+        num_in_channels=args.num_in_channels,
+        from_safetensors=args.from_safetensors,
+        device=args.device,
+    )
+    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

model_index.json

@@ -0,0 +1,28 @@
+{
+  "_class_name": "AudioLDMPipeline",
+  "_diffusers_version": "0.15.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "DDIMScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "ClapTextModelWithProjection"
+  ],
+  "tokenizer": [
+    "transformers",
+    "RobertaTokenizerFast"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ],
+  "vocoder": [
+    "transformers",
+    "SpeechT5HifiGan"
+  ]
+}

run_conversion.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+python convert_original_audioldm_to_diffusers.py \
+      --checkpoint_path "/home/sanchit_huggingface_co/.cache/audioldm/audioldm-l-full.ckpt" \
+      --extract_ema \
+      --dump_path "./"      

scheduler/scheduler_config.json

@@ -0,0 +1,17 @@
+{
+  "_class_name": "DDIMScheduler",
+  "_diffusers_version": "0.15.0.dev0",
+  "beta_end": 0.0195,
+  "beta_schedule": "scaled_linear",
+  "beta_start": 0.0015,
+  "clip_sample": false,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "sample_max_value": 1.0,
+  "set_alpha_to_one": false,
+  "steps_offset": 1,
+  "thresholding": false,
+  "trained_betas": null
+}

text_encoder/config.json

@@ -0,0 +1,32 @@
+{
+  "architectures": [
+    "ClapTextModelWithProjection"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "fusion_hidden_size": 768,
+  "fusion_num_hidden_layers": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 514,
+  "model_type": "clap_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "projection_dim": 512,
+  "projection_hidden_act": "relu",
+  "projection_hidden_size": 768,
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.0.dev0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

text_encoder/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0292e65e99024f2d21f0a59b718f0e4914546e4287e2dabdd2d7e4a95c169f7b
+size 501284353

tokenizer/special_tokens_map.json

@@ -0,0 +1,15 @@
+{
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer/tokenizer_config.json

@@ -0,0 +1,17 @@
+{
+  "add_prefix_space": false,
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "processor_class": "ClapProcessor",
+  "sep_token": "</s>",
+  "special_tokens_map_file": null,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}

unet/config.json

@@ -0,0 +1,56 @@
+{
+  "_class_name": "UNet2DConditionModel",
+  "_diffusers_version": "0.15.0.dev0",
+  "act_fn": "silu",
+  "attention_head_dim": 8,
+  "block_out_channels": [
+    256,
+    512,
+    768,
+    1280
+  ],
+  "center_input_sample": false,
+  "class_embed_type": "simple_projection",
+  "class_embeddings_concat": true,
+  "conv_in_kernel": 3,
+  "conv_out_kernel": 3,
+  "cross_attention_dim": [
+    256,
+    512,
+    768,
+    1280
+  ],
+  "down_block_types": [
+    "DownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D",
+    "CrossAttnDownBlock2D"
+  ],
+  "downsample_padding": 1,
+  "dual_cross_attention": false,
+  "flip_sin_to_cos": true,
+  "freq_shift": 0,
+  "in_channels": 8,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2DCrossAttn",
+  "norm_eps": 1e-05,
+  "norm_num_groups": 32,
+  "num_class_embeds": null,
+  "only_cross_attention": false,
+  "out_channels": 8,
+  "projection_class_embeddings_input_dim": 512,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 128,
+  "time_cond_proj_dim": null,
+  "time_embedding_type": "positional",
+  "timestep_post_act": null,
+  "up_block_types": [
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "CrossAttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upcast_attention": false,
+  "use_linear_projection": false
+}

unet/diffusion_pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
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+size 2956840221

vae/config.json

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+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.15.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "in_channels": 1,
+  "latent_channels": 8,
+  "layers_per_block": 2,
+  "norm_num_groups": 32,
+  "out_channels": 1,
+  "sample_size": 512,
+  "scaling_factor": 0.9654927849769592,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ]
+}

vae/diffusion_pytorch_model.bin

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+oid sha256:e3fc8ccecb1849c8a23cd4f9dd959eb7aaa203cc010386288418dbc551cdaaf7
+size 221586505

vocoder/config.json

@@ -0,0 +1,50 @@
+{
+  "architectures": [
+    "SpeechT5HifiGan"
+  ],
+  "initializer_range": 0.01,
+  "leaky_relu_slope": 0.1,
+  "model_in_dim": 64,
+  "model_type": "hifigan",
+  "normalize_before": false,
+  "resblock_dilation_sizes": [
+    [
+      1,
+      3,
+      5
+    ],
+    [
+      1,
+      3,
+      5
+    ],
+    [
+      1,
+      3,
+      5
+    ]
+  ],
+  "resblock_kernel_sizes": [
+    3,
+    7,
+    11
+  ],
+  "sampling_rate": 16000,
+  "torch_dtype": "float32",
+  "transformers_version": "4.28.0.dev0",
+  "upsample_initial_channel": 1024,
+  "upsample_kernel_sizes": [
+    16,
+    16,
+    8,
+    4,
+    4
+  ],
+  "upsample_rates": [
+    5,
+    4,
+    2,
+    2,
+    2
+  ]
+}

vocoder/pytorch_model.bin

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+oid sha256:f9fbefc2b31c85d1dabe98e53d09ac88039af411162a7e641040a9c2b5f62364
+size 221120349