Delete convert_repo_to_safetensors.py

convert_repo_to_safetensors.py

@@ -1,364 +0,0 @@
-# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint.
-# *Only* converts the UNet, VAE, and Text Encoder.
-# Does not convert optimizer state or any other thing.
-
-import argparse
-import os.path as osp
-import re
-
-import torch
-from safetensors.torch import load_file, save_file
-
-
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-    # the following are for sdxl
-    ("label_emb.0.0.weight", "add_embedding.linear_1.weight"),
-    ("label_emb.0.0.bias", "add_embedding.linear_1.bias"),
-    ("label_emb.0.2.weight", "add_embedding.linear_2.weight"),
-    ("label_emb.0.2.bias", "add_embedding.linear_2.bias"),
-]
-
-unet_conversion_map_resnet = [
-    # (stable-diffusion, HF Diffusers)
-    ("in_layers.0", "norm1"),
-    ("in_layers.2", "conv1"),
-    ("out_layers.0", "norm2"),
-    ("out_layers.3", "conv2"),
-    ("emb_layers.1", "time_emb_proj"),
-    ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(3):
-    # loop over downblocks/upblocks
-
-    for j in range(2):
-        # loop over resnets/attentions for downblocks
-        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-        sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0."
-        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-        if i > 0:
-            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-            sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1."
-            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-    for j in range(4):
-        # loop over resnets/attentions for upblocks
-        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-        sd_up_res_prefix = f"output_blocks.{3*i + j}.0."
-        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-        if i < 2:
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-    if i < 3:
-        # no downsample in down_blocks.3
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-        sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op."
-        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        # no upsample in up_blocks.3
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
-        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-unet_conversion_map_layer.append(("output_blocks.2.2.conv.", "output_blocks.2.1.conv."))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-for j in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{j}."
-    sd_mid_res_prefix = f"middle_block.{2*j}."
-    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
-    # buyer beware: this is a *brittle* function,
-    # and correct output requires that all of these pieces interact in
-    # the exact order in which I have arranged them.
-    mapping = {k: k for k in unet_state_dict.keys()}
-    for sd_name, hf_name in unet_conversion_map:
-        mapping[hf_name] = sd_name
-    for k, v in mapping.items():
-        if "resnets" in k:
-            for sd_part, hf_part in unet_conversion_map_resnet:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    for k, v in mapping.items():
-        for sd_part, hf_part in unet_conversion_map_layer:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    new_state_dict = {sd_name: unet_state_dict[hf_name] for hf_name, sd_name in mapping.items()}
-    return new_state_dict
-
-
-# ================#
-# VAE Conversion #
-# ================#
-
-vae_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("nin_shortcut", "conv_shortcut"),
-    ("norm_out", "conv_norm_out"),
-    ("mid.attn_1.", "mid_block.attentions.0."),
-]
-
-for i in range(4):
-    # down_blocks have two resnets
-    for j in range(2):
-        hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}."
-        sd_down_prefix = f"encoder.down.{i}.block.{j}."
-        vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
-
-    if i < 3:
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0."
-        sd_downsample_prefix = f"down.{i}.downsample."
-        vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"up.{3-i}.upsample."
-        vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
-
-    # up_blocks have three resnets
-    # also, up blocks in hf are numbered in reverse from sd
-    for j in range(3):
-        hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}."
-        sd_up_prefix = f"decoder.up.{3-i}.block.{j}."
-        vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
-
-# this part accounts for mid blocks in both the encoder and the decoder
-for i in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{i}."
-    sd_mid_res_prefix = f"mid.block_{i+1}."
-    vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-vae_conversion_map_attn = [
-    # (stable-diffusion, HF Diffusers)
-    ("norm.", "group_norm."),
-    # the following are for SDXL
-    ("q.", "to_q."),
-    ("k.", "to_k."),
-    ("v.", "to_v."),
-    ("proj_out.", "to_out.0."),
-]
-
-
-def reshape_weight_for_sd(w):
-    # convert HF linear weights to SD conv2d weights
-    if not w.ndim == 1:
-        return w.reshape(*w.shape, 1, 1)
-    else:
-        return w
-
-
-def convert_vae_state_dict(vae_state_dict):
-    mapping = {k: k for k in vae_state_dict.keys()}
-    for k, v in mapping.items():
-        for sd_part, hf_part in vae_conversion_map:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    for k, v in mapping.items():
-        if "attentions" in k:
-            for sd_part, hf_part in vae_conversion_map_attn:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()}
-    weights_to_convert = ["q", "k", "v", "proj_out"]
-    for k, v in new_state_dict.items():
-        for weight_name in weights_to_convert:
-            if f"mid.attn_1.{weight_name}.weight" in k:
-                print(f"Reshaping {k} for SD format")
-                new_state_dict[k] = reshape_weight_for_sd(v)
-    return new_state_dict
-
-
-# =========================#
-# Text Encoder Conversion #
-# =========================#
-
-
-textenc_conversion_lst = [
-    # (stable-diffusion, HF Diffusers)
-    ("transformer.resblocks.", "text_model.encoder.layers."),
-    ("ln_1", "layer_norm1"),
-    ("ln_2", "layer_norm2"),
-    (".c_fc.", ".fc1."),
-    (".c_proj.", ".fc2."),
-    (".attn", ".self_attn"),
-    ("ln_final.", "text_model.final_layer_norm."),
-    ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
-    ("positional_embedding", "text_model.embeddings.position_embedding.weight"),
-]
-protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
-textenc_pattern = re.compile("|".join(protected.keys()))
-
-# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
-code2idx = {"q": 0, "k": 1, "v": 2}
-
-
-def convert_openclip_text_enc_state_dict(text_enc_dict):
-    new_state_dict = {}
-    capture_qkv_weight = {}
-    capture_qkv_bias = {}
-    for k, v in text_enc_dict.items():
-        if (
-            k.endswith(".self_attn.q_proj.weight")
-            or k.endswith(".self_attn.k_proj.weight")
-            or k.endswith(".self_attn.v_proj.weight")
-        ):
-            k_pre = k[: -len(".q_proj.weight")]
-            k_code = k[-len("q_proj.weight")]
-            if k_pre not in capture_qkv_weight:
-                capture_qkv_weight[k_pre] = [None, None, None]
-            capture_qkv_weight[k_pre][code2idx[k_code]] = v
-            continue
-
-        if (
-            k.endswith(".self_attn.q_proj.bias")
-            or k.endswith(".self_attn.k_proj.bias")
-            or k.endswith(".self_attn.v_proj.bias")
-        ):
-            k_pre = k[: -len(".q_proj.bias")]
-            k_code = k[-len("q_proj.bias")]
-            if k_pre not in capture_qkv_bias:
-                capture_qkv_bias[k_pre] = [None, None, None]
-            capture_qkv_bias[k_pre][code2idx[k_code]] = v
-            continue
-
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
-        new_state_dict[relabelled_key] = v
-
-    for k_pre, tensors in capture_qkv_weight.items():
-        if None in tensors:
-            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)
-
-    for k_pre, tensors in capture_qkv_bias.items():
-        if None in tensors:
-            raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)
-
-    return new_state_dict
-
-
-def convert_openai_text_enc_state_dict(text_enc_dict):
-    return text_enc_dict
-
-
-def convert_diffusers_to_safetensors(model_path, checkpoint_path, dtype="fp16"):
-    # Path for safetensors
-    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors")
-    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors")
-    text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors")
-    text_enc_2_path = osp.join(model_path, "text_encoder_2", "model.safetensors")
-
-    # Load models from safetensors if it exists, if it doesn't pytorch
-    if osp.exists(unet_path):
-        unet_state_dict = load_file(unet_path, device="cpu")
-    else:
-        unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
-        unet_state_dict = torch.load(unet_path, map_location="cpu")
-
-    if osp.exists(vae_path):
-        vae_state_dict = load_file(vae_path, device="cpu")
-    else:
-        vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
-        vae_state_dict = torch.load(vae_path, map_location="cpu")
-
-    if osp.exists(text_enc_path):
-        text_enc_dict = load_file(text_enc_path, device="cpu")
-    else:
-        text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
-        text_enc_dict = torch.load(text_enc_path, map_location="cpu")
-
-    if osp.exists(text_enc_2_path):
-        text_enc_2_dict = load_file(text_enc_2_path, device="cpu")
-    else:
-        text_enc_2_path = osp.join(model_path, "text_encoder_2", "pytorch_model.bin")
-        text_enc_2_dict = torch.load(text_enc_2_path, map_location="cpu")
-
-    # Convert the UNet model
-    unet_state_dict = convert_unet_state_dict(unet_state_dict)
-    unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
-
-    # Convert the VAE model
-    vae_state_dict = convert_vae_state_dict(vae_state_dict)
-    vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
-
-    # Convert text encoder 1
-    text_enc_dict = convert_openai_text_enc_state_dict(text_enc_dict)
-    text_enc_dict = {"conditioner.embedders.0.transformer." + k: v for k, v in text_enc_dict.items()}
-
-    # Convert text encoder 2
-    text_enc_2_dict = convert_openclip_text_enc_state_dict(text_enc_2_dict)
-    text_enc_2_dict = {"conditioner.embedders.1.model." + k: v for k, v in text_enc_2_dict.items()}
-    # We call the `.T.contiguous()` to match what's done in
-    # https://github.com/huggingface/diffusers/blob/84905ca7287876b925b6bf8e9bb92fec21c78764/src/diffusers/loaders/single_file_utils.py#L1085
-    text_enc_2_dict["conditioner.embedders.1.model.text_projection"] = text_enc_2_dict.pop(
-        "conditioner.embedders.1.model.text_projection.weight"
-    ).T.contiguous()
-
-    # Put together new checkpoint
-    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict, **text_enc_2_dict}
-
-    if dtype == "fp16": state_dict = {k: v.half() for k, v in state_dict.items()}
-    elif dtype == "fp32": state_dict = {k: v.to(torch.float32) for k, v in state_dict.items()}
-    elif dtype == "bf16": state_dict = {k: v.to(torch.bfloat16) for k, v in state_dict.items()}
-
-    save_file(state_dict, checkpoint_path)
-
-
-def download_repo(repo_id, dir_path):
-    from huggingface_hub import snapshot_download
-    try:
-        snapshot_download(repo_id=repo_id, local_dir=dir_path)
-    except Exception as e:
-        print(f"Error: Failed to download {repo_id}. {e}")
-        return
-
-
-def convert_repo_to_safetensors(repo_id, dtype="fp16"):
-    download_dir = f"{repo_id.split('/')[0]}_{repo_id.split('/')[-1]}"
-    output_filename = f"{repo_id.split('/')[0]}_{repo_id.split('/')[-1]}.safetensors"
-    download_repo(repo_id, download_dir)
-    convert_diffusers_to_safetensors(download_dir, output_filename, dtype)
-    return output_filename
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--repo_id", default=None, type=str, required=True, help="HF Repo ID of the model to convert.")
-    parser.add_argument("--dtype", default="fp16", type=str, choices=["fp16", "fp32", "bf16", "default"], help='Output data type. (Default: "fp16")')
-
-    args = parser.parse_args()
-    assert args.repo_id is not None, "Must provide a Repo ID!"
-
-    convert_repo_to_safetensors(args.repo_id, args.dtype)
-
-
-# Usage: python convert_repo_to_safetensors.py --repo_id GraydientPlatformAPI/goodfit-pony41-xl