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README.md

@@ -1,12 +1,12 @@
----
-title: Convert Repo To Safetensors Sd
-emoji: 😻
-colorFrom: green
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.38.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Convert diffusers SD1.5 repo to single Safetensors
+emoji: 🐶
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 4.38.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,26 @@
+import gradio as gr
+import os
+from convert_repo_to_safetensors_sd_gr import convert_repo_to_safetensors_multi_sd
+os.environ['HF_OUTPUT_REPO'] = 'John6666/safetensors_converting_test'
+
+css = """"""
+
+with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo:
+    with gr.Column(): 
+        repo_id = gr.Textbox(label="Repo ID", placeholder="author/model", value="", lines=1)
+        is_half = gr.Checkbox(label="Half precision", value=True)
+        is_upload = gr.Checkbox(label="Upload safetensors to HF Repo", info="Fast download, but files will be public.", value=False)
+        uploaded_urls = gr.CheckboxGroup(visible=False, choices=[], value=None)
+        run_button = gr.Button(value="Convert")
+        st_file = gr.Files(label="Output", interactive=False)
+        st_md = gr.Markdown()
+
+    gr.on(
+        triggers=[repo_id.submit, run_button.click],
+        fn=convert_repo_to_safetensors_multi_sd,
+        inputs=[repo_id, st_file, is_upload, uploaded_urls, is_half],
+        outputs=[st_file, uploaded_urls, st_md],
+    )
+
+demo.queue()
+demo.launch()

convert_repo_to_safetensors_sd.py

@@ -0,0 +1,263 @@
+# 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.
+# Written by jachiam
+
+import argparse
+import os.path as osp
+
+import torch
+
+
+# =================#
+# 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"),
+]
+
+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(4):
+    # 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 < 3:
+            # no attention layers in down_blocks.3
+            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(3):
+        # 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 > 0:
+            # 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))
+
+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 = {v: unet_state_dict[k] for k, v 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."),
+    ("q.", "query."),
+    ("k.", "key."),
+    ("v.", "value."),
+    ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+    # convert HF linear weights to SD conv2d weights
+    return w.reshape(*w.shape, 1, 1)
+
+
+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 #
+# =========================#
+# pretty much a no-op
+
+
+def convert_text_enc_state_dict(text_enc_dict):
+    return text_enc_dict
+
+
+def convert_diffusers_to_safetensors(model_path, checkpoint_path, half = True):
+    from safetensors.torch import load_file
+    input_safetensors = False
+    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
+    if not osp.exists(unet_path):
+        unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors")
+        input_safetensors = True
+    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
+    if not osp.exists(vae_path):
+        vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors")
+        input_safetensors = True
+    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
+    if not osp.exists(text_enc_path):
+        text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors")
+        input_safetensors = True
+
+    # Convert the UNet model
+    unet_state_dict = torch.load(unet_path, map_location='cpu') if not input_safetensors else load_file(unet_path)
+    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 = torch.load(vae_path, map_location='cpu') if not input_safetensors else load_file(vae_path)
+    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 the text encoder model
+    text_enc_dict = torch.load(text_enc_path, map_location='cpu') if not input_safetensors else load_file(text_enc_path)
+    text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
+    text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
+
+    # Put together new checkpoint
+    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
+    if half:
+        state_dict = {k:v.half() for k,v in state_dict.items()}
+    state_dict = {"state_dict": state_dict}
+    torch.save(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}. ")
+        return
+
+
+def convert_repo_to_safetensors(repo_id, half = True):
+    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, half)
+    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("--half", action="store_true", help="Save weights in half precision.")
+
+    args = parser.parse_args()
+    assert args.repo_id is not None, "Must provide a Repo ID!"
+
+    convert_repo_to_safetensors(args.repo_id, args.half)

convert_repo_to_safetensors_sd_gr.py

@@ -0,0 +1,300 @@
+# 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.
+# Written by jachiam
+
+import argparse
+import os.path as osp
+
+import torch
+import gradio as gr
+
+# =================#
+# 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"),
+]
+
+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(4):
+    # 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 < 3:
+            # no attention layers in down_blocks.3
+            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(3):
+        # 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 > 0:
+            # 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))
+
+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 = {v: unet_state_dict[k] for k, v 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."),
+    ("q.", "query."),
+    ("k.", "key."),
+    ("v.", "value."),
+    ("proj_out.", "proj_attn."),
+]
+
+
+def reshape_weight_for_sd(w):
+    # convert HF linear weights to SD conv2d weights
+    return w.reshape(*w.shape, 1, 1)
+
+
+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 #
+# =========================#
+# pretty much a no-op
+
+
+def convert_text_enc_state_dict(text_enc_dict):
+    return text_enc_dict
+
+
+def convert_diffusers_to_safetensors(model_path, checkpoint_path, half = True, progress=gr.Progress(track_tqdm=True)):
+    progress(0, desc="Start converting...")
+    from safetensors.torch import load_file
+    input_safetensors = False
+    unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.bin")
+    if not osp.exists(unet_path):
+        unet_path = osp.join(model_path, "unet", "diffusion_pytorch_model.safetensors")
+        input_safetensors = True
+    vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.bin")
+    if not osp.exists(vae_path):
+        vae_path = osp.join(model_path, "vae", "diffusion_pytorch_model.safetensors")
+        input_safetensors = True
+    text_enc_path = osp.join(model_path, "text_encoder", "pytorch_model.bin")
+    if not osp.exists(text_enc_path):
+        text_enc_path = osp.join(model_path, "text_encoder", "model.safetensors")
+        input_safetensors = True
+
+    # Convert the UNet model
+    unet_state_dict = torch.load(unet_path, map_location='cpu') if not input_safetensors else load_file(unet_path)
+    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 = torch.load(vae_path, map_location='cpu') if not input_safetensors else load_file(vae_path)
+    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 the text encoder model
+    text_enc_dict = torch.load(text_enc_path, map_location='cpu') if not input_safetensors else load_file(text_enc_path)
+    text_enc_dict = convert_text_enc_state_dict(text_enc_dict)
+    text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
+
+    # Put together new checkpoint
+    state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
+    if half:
+        state_dict = {k:v.half() for k,v in state_dict.items()}
+    state_dict = {"state_dict": state_dict}
+    torch.save(state_dict, checkpoint_path)
+
+    progress(1, desc="Converted.")
+
+
+def download_repo(repo_id, dir_path, progress=gr.Progress(track_tqdm=True)):
+    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}. ")
+        return
+
+
+def upload_safetensors_to_repo(filename, progress=gr.Progress(track_tqdm=True)):
+    from huggingface_hub import HfApi, hf_hub_url
+    import os
+    from pathlib import Path
+    output_filename = Path(filename).name
+    hf_token = os.environ.get("HF_TOKEN")
+    repo_id = os.environ.get("HF_OUTPUT_REPO")
+    api = HfApi()
+    try:
+        progress(0, desc="Start uploading...")
+        api.upload_file(path_or_fileobj=filename, path_in_repo=output_filename, repo_id=repo_id, token=hf_token)
+        progress(1, desc="Uploaded.")
+        url  = hf_hub_url(repo_id=repo_id, filename=output_filename)
+    except Exception as e:
+        print(f"Error: Failed to upload to {repo_id}. ")
+        return None
+    return url
+
+
+def convert_repo_to_safetensors(repo_id, half = True, progress=gr.Progress(track_tqdm=True)):
+    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, half)
+    return output_filename
+
+
+def convert_repo_to_safetensors_multi_sd(repo_id, files, is_upload, urls, half=True, progress=gr.Progress(track_tqdm=True)):
+    file = convert_repo_to_safetensors(repo_id, half)
+    if not urls: urls = []
+    url = ""
+    if is_upload:
+        url = upload_safetensors_to_repo(file, half)
+        if url: urls.append(url)
+    md = ""
+    for u in urls:
+        md += f"[Download {str(u).split('/')[-1]}]({str(u)})<br>"
+    if not files: files = []
+    files.append(file)
+    return gr.update(value=files), gr.update(value=urls, choices=urls), gr.update(value=md)
+
+
+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("--half", action="store_true", help="Save weights in half precision.")
+
+    args = parser.parse_args()
+    assert args.repo_id is not None, "Must provide a Repo ID!"
+
+    convert_repo_to_safetensors(args.repo_id, args.half)

requirements.txt

@@ -0,0 +1,3 @@
+torch
+safetensors
+huggingface-hub