app.py

import gradio as gr
import torch
import os
import gc
import shutil
import requests
import json
import struct
import numpy as np
import re
from pathlib import Path
from typing import Dict, Any, Optional, List
from huggingface_hub import HfApi, hf_hub_download, list_repo_files, login
from safetensors.torch import load_file, save_file
from tqdm import tqdm

# --- Memory Efficient Safetensors ---
class MemoryEfficientSafeOpen:
    """
    Reads safetensors metadata and tensors without mmap, keeping RAM usage low.
    """
    def __init__(self, filename):
        self.filename = filename
        self.file = open(filename, "rb")
        self.header, self.header_size = self._read_header()

    def __enter__(self):
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.file.close()

    def keys(self) -> list[str]:
        return [k for k in self.header.keys() if k != "__metadata__"]

    def metadata(self) -> Dict[str, str]:
        return self.header.get("__metadata__", {})

    def get_tensor(self, key):
        if key not in self.header:
            raise KeyError(f"Tensor '{key}' not found in the file")
        metadata = self.header[key]
        offset_start, offset_end = metadata["data_offsets"]
        self.file.seek(self.header_size + 8 + offset_start)
        tensor_bytes = self.file.read(offset_end - offset_start)
        return self._deserialize_tensor(tensor_bytes, metadata)

    def _read_header(self):
        header_size = struct.unpack("<Q", self.file.read(8))[0]
        header_json = self.file.read(header_size).decode("utf-8")
        return json.loads(header_json), header_size

    def _deserialize_tensor(self, tensor_bytes, metadata):
        dtype_map = {
            "F32": torch.float32, "F16": torch.float16, "BF16": torch.bfloat16,
            "I64": torch.int64, "I32": torch.int32, "I16": torch.int16, "I8": torch.int8,
            "U8": torch.uint8, "BOOL": torch.bool
        }
        dtype = dtype_map[metadata["dtype"]]
        shape = metadata["shape"]
        return torch.frombuffer(tensor_bytes, dtype=torch.uint8).view(dtype).reshape(shape)

# --- Constants & Setup ---
try:
    TempDir = Path("/tmp/temp_tool")
    os.makedirs(TempDir, exist_ok=True)
except:
    TempDir = Path("./temp_tool")
    os.makedirs(TempDir, exist_ok=True)

api = HfApi()

def cleanup_temp():
    if TempDir.exists():
        shutil.rmtree(TempDir)
    os.makedirs(TempDir, exist_ok=True)
    gc.collect()

def download_file(input_path, token, filename=None):
    local_path = TempDir / (filename if filename else "model.safetensors")
    if input_path.startswith("http"):
        print(f"Downloading {filename} from URL...")
        try:
            response = requests.get(input_path, stream=True, timeout=30)
            response.raise_for_status()
            with open(local_path, 'wb') as f:
                for chunk in response.iter_content(chunk_size=8192):
                    f.write(chunk)
        except Exception as e: raise ValueError(f"Download failed: {e}")
    else:
        print(f"Downloading {filename} from Hub...")
        if not filename:
            try:
                files = list_repo_files(repo_id=input_path, token=token)
                safetensors = [f for f in files if f.endswith(".safetensors")]
                filename = safetensors[0] if safetensors else "adapter_model.safetensors"
            except: filename = "adapter_model.safetensors"
        
        try:
            hf_hub_download(repo_id=input_path, filename=filename, token=token, local_dir=TempDir, local_dir_use_symlinks=False)
            if not (TempDir / filename).exists():
                found = list(TempDir.rglob(filename))
                if found: shutil.move(found[0], local_path)
        except Exception as e: raise ValueError(f"Hub download failed: {e}")
    
    return local_path

def get_key_stem(key):
    key = key.replace(".weight", "").replace(".bias", "")
    key = key.replace(".lora_down", "").replace(".lora_up", "")
    key = key.replace(".lora_A", "").replace(".lora_B", "")
    key = key.replace(".alpha", "")
    prefixes = [
        "model.diffusion_model.", "diffusion_model.", "model.", 
        "transformer.", "text_encoder.", "lora_unet_", "lora_te_", "base_model.model."
    ]
    changed = True
    while changed:
        changed = False
        for p in prefixes:
            if key.startswith(p):
                key = key[len(p):]
                changed = True
    return key

# =================================================================================
# TAB 1: MERGE & RESHARD (Fixes Folder Structure & Aux Files)
# =================================================================================

def load_lora_to_memory(lora_path, precision_dtype=torch.bfloat16):
    print(f"Loading LoRA from {lora_path}...")
    state_dict = load_file(lora_path, device="cpu")
    pairs = {} 
    alphas = {}
    for k, v in state_dict.items():
        stem = get_key_stem(k)
        if "alpha" in k:
            alphas[stem] = v.item() if isinstance(v, torch.Tensor) else v
        else:
            if stem not in pairs: pairs[stem] = {}
            if "lora_down" in k or "lora_A" in k:
                pairs[stem]["down"] = v.to(dtype=precision_dtype)
                pairs[stem]["rank"] = v.shape[0]
            elif "lora_up" in k or "lora_B" in k:
                pairs[stem]["up"] = v.to(dtype=precision_dtype)
    for stem in pairs:
        pairs[stem]["alpha"] = alphas.get(stem, float(pairs[stem].get("rank", 1.0)))
    return pairs

class ShardBuffer:
    def __init__(self, max_size_gb, output_dir, output_repo, subfolder, hf_token, filename_prefix="model"):
        self.max_bytes = int(max_size_gb * 1024**3)
        self.output_dir = output_dir
        self.output_repo = output_repo
        self.subfolder = subfolder
        self.hf_token = hf_token
        self.filename_prefix = filename_prefix
        self.buffer = []
        self.current_bytes = 0
        self.shard_count = 0
        self.index_map = {}
        self.total_size = 0  # Accumulates total model size for index.json

    def add_tensor(self, key, tensor):
        # Determine bytes for size calculation and storage
        if tensor.dtype == torch.bfloat16:
            raw_bytes = tensor.view(torch.int16).numpy().tobytes()
            dtype_str = "BF16"
        elif tensor.dtype == torch.float16:
            raw_bytes = tensor.numpy().tobytes()
            dtype_str = "F16"
        else:
            raw_bytes = tensor.numpy().tobytes()
            dtype_str = "F32"
            
        size = len(raw_bytes)
        
        self.buffer.append({
            "key": key,
            "data": raw_bytes,
            "dtype": dtype_str,
            "shape": tensor.shape
        })
        
        self.current_bytes += size
        self.total_size += size  # Explicitly increment total size
        
        if self.current_bytes >= self.max_bytes:
            self.flush()
            
    def flush(self):
        if not self.buffer: return
        self.shard_count += 1
        
        # Naming: prefix-0000X.safetensors
        # This is standard for indexed loading.
        filename = f"{self.filename_prefix}-{self.shard_count:05d}.safetensors"
        
        # Proper Subfolder Handling
        path_in_repo = f"{self.subfolder}/{filename}" if self.subfolder else filename
        
        print(f"Flushing {path_in_repo} ({self.current_bytes / 1024**3:.2f} GB)...")
        
        header = {"__metadata__": {"format": "pt"}}
        current_offset = 0
        for item in self.buffer:
            header[item["key"]] = {
                "dtype": item["dtype"],
                "shape": item["shape"],
                "data_offsets": [current_offset, current_offset + len(item["data"])]
            }
            current_offset += len(item["data"])
            self.index_map[item["key"]] = filename # Relative filename for index
            
        header_json = json.dumps(header).encode('utf-8')
        
        out_path = self.output_dir / filename
        with open(out_path, 'wb') as f:
            f.write(struct.pack('<Q', len(header_json)))
            f.write(header_json)
            for item in self.buffer:
                f.write(item["data"])
                
        print(f"Uploading {path_in_repo}...")
        api.upload_file(path_or_fileobj=out_path, path_in_repo=path_in_repo, repo_id=self.output_repo, token=self.hf_token)
        
        os.remove(out_path)
        self.buffer = []
        self.current_bytes = 0
        gc.collect()

def download_lora_smart(input_str, token):
    """
    Handles Repo IDs (user/repo) and Direct URLs.
    """
    local_path = TempDir / "adapter.safetensors"
    
    # 1. Direct URL (Private/Public)
    if input_str.startswith("http"):
        print(f"Downloading LoRA from URL: {input_str}")
        headers = {"Authorization": f"Bearer {token}"} if token else {}
        try:
            response = requests.get(input_str, stream=True, headers=headers, timeout=30)
            response.raise_for_status()
            with open(local_path, 'wb') as f:
                for chunk in response.iter_content(chunk_size=8192):
                    f.write(chunk)
            # Basic validation
            with open(local_path, "rb") as f:
                if len(f.read(8)) == 8: return local_path
        except Exception as e:
            print(f"URL download failed: {e}. Trying as Repo ID...")

    # 2. Repo ID (Fallback or Primary)
    # If the user entered a repo ID (e.g. "AlekseyCalvin/MyLora"), this catches it.
    print(f"Attempting download from Hub Repo: {input_str}")
    try:
        # Try finding the specific file
        candidates = ["adapter_model.safetensors", "model.safetensors"]
        target_file = None
        
        try:
            files = list_repo_files(repo_id=input_str, token=token)
            safetensors = [f for f in files if f.endswith(".safetensors")]
            for c in candidates:
                if c in safetensors:
                    target_file = c
                    break
            if not target_file and safetensors:
                target_file = safetensors[0]
        except:
            # If listing fails, try default
            target_file = "adapter_model.safetensors"

        hf_hub_download(repo_id=input_str, filename=target_file, token=token, local_dir=TempDir, local_dir_use_symlinks=False)
        
        # Rename to generic name
        downloaded = TempDir / target_file
        if downloaded != local_path:
            if local_path.exists(): os.remove(local_path)
            shutil.move(downloaded, local_path)
            
        return local_path
    except Exception as e:
        raise ValueError(f"Failed to download LoRA from {input_str}. \nError: {e}")

def task_merge(hf_token, base_repo, base_subfolder, lora_input, scale, precision, shard_size, output_repo, structure_repo, private, progress=gr.Progress()):
    cleanup_temp()
    login(hf_token)
    
    # 1. Output Setup
    try:
        api.create_repo(repo_id=output_repo, private=private, exist_ok=True, token=hf_token)
    except Exception as e: return f"Error creating repo: {e}"
    
    # Define modes
    output_subfolder = base_subfolder if base_subfolder else "" 
    
    # 2. Clone Structure
    if structure_repo:
        print(f"Cloning structure from {structure_repo}...")
        # Ignore the folder we are overwriting (if any)
        ignore = output_subfolder if output_subfolder else None
        # Root merge mode (LLM) usually implies we skip weights in the root
        is_root_merge = not bool(output_subfolder)
        streaming_copy_structure(hf_token, structure_repo, output_repo, ignore_prefix=ignore, is_root_merge=is_root_merge)

    # 3. Download Input Shards
    progress(0.1, desc="Downloading Base Model...")
    try:
        files = list_repo_files(repo_id=base_repo, token=hf_token)
    except Exception as e: return f"Error accessing base repo: {e}"

    input_shards = []
    for f in files:
        if f.endswith(".safetensors"):
            # Filter by subfolder if specified
            if output_subfolder and not f.startswith(output_subfolder): continue
            
            local_path = TempDir / "input_shards" / os.path.basename(f)
            os.makedirs(local_path.parent, exist_ok=True)
            
            hf_hub_download(repo_id=base_repo, filename=f, token=hf_token, local_dir=local_path.parent, local_dir_use_symlinks=False)
            
            # Locate file (handle nested download paths)
            found = list(local_path.parent.rglob(os.path.basename(f)))
            if found: input_shards.append(found[0])

    if not input_shards: return "No base safetensors found in specified location."
    input_shards.sort()
    
    # --- NAMING CONVENTION LOGIC ---
    # 1. Check for Diffusers specific subfolders -> force 'diffusion_pytorch_model'
    if output_subfolder in ["transformer", "unet"]:
        filename_prefix = "diffusion_pytorch_model"
        index_filename = "diffusion_pytorch_model.safetensors.index.json"
    # 2. Check input file naming -> adopt input convention
    elif "diffusion_pytorch_model" in os.path.basename(input_shards[0]):
        filename_prefix = "diffusion_pytorch_model"
        index_filename = "diffusion_pytorch_model.safetensors.index.json"
    # 3. Default to LLM style
    else:
        filename_prefix = "model"
        index_filename = "model.safetensors.index.json"
        
    print(f"Naming scheme: {filename_prefix} (Index: {index_filename})")

    # 4. Load LoRA
    dtype = torch.bfloat16 if precision == "bf16" else torch.float16 if precision == "fp16" else torch.float32
    try:
        progress(0.15, desc="Downloading LoRA...")
        lora_path = download_lora_smart(lora_input, hf_token)
        lora_pairs = load_lora_to_memory(lora_path, precision_dtype=dtype)
    except Exception as e: return f"Error loading LoRA: {e}"

    # 5. Stream Process
    buffer = ShardBuffer(shard_size, TempDir, output_repo, output_subfolder, hf_token, filename_prefix=filename_prefix)
    
    for i, shard_file in enumerate(input_shards):
        progress(0.2 + (0.7 * i / len(input_shards)), desc=f"Processing {os.path.basename(shard_file)}")
        
        with MemoryEfficientSafeOpen(shard_file) as f:
            keys = f.keys()
            for k in keys:
                v = f.get_tensor(k)
                base_stem = get_key_stem(k)
                lora_keys = set(lora_pairs.keys())
                match = None
                
                if base_stem in lora_keys: match = lora_pairs[base_stem]
                # QKV Heuristics (Z-Image/Flux specific)
                if not match:
                     if "to_q" in base_stem:
                        qkv_stem = base_stem.replace("to_q", "qkv")
                        if qkv_stem in lora_keys: match = lora_pairs[qkv_stem]
                     elif "to_k" in base_stem:
                        qkv_stem = base_stem.replace("to_k", "qkv")
                        if qkv_stem in lora_keys: match = lora_pairs[qkv_stem]
                     elif "to_v" in base_stem:
                        qkv_stem = base_stem.replace("to_v", "qkv")
                        if qkv_stem in lora_keys: match = lora_pairs[qkv_stem]

                if match and "down" in match and "up" in match:
                    down = match["down"]
                    up = match["up"]
                    scaling = scale * (match["alpha"] / match["rank"])
                    
                    if len(v.shape) == 4 and len(down.shape) == 2:
                        down = down.unsqueeze(-1).unsqueeze(-1)
                        up = up.unsqueeze(-1).unsqueeze(-1)
                    
                    try:
                        if len(up.shape) == 4:
                            delta = (up.squeeze() @ down.squeeze()).reshape(up.shape[0], down.shape[1], 1, 1)
                        else:
                            delta = up @ down
                    except:
                        delta = up.T @ down 
                    
                    delta = delta * scaling
                    valid_delta = True
                    
                    if delta.shape == v.shape: pass
                    elif delta.shape[0] == v.shape[0] * 3:
                        chunk = v.shape[0]
                        if "to_q" in k: delta = delta[0:chunk, ...]
                        elif "to_k" in k: delta = delta[chunk:2*chunk, ...]
                        elif "to_v" in k: delta = delta[2*chunk:, ...]
                        else: valid_delta = False
                    elif delta.numel() == v.numel():
                        delta = delta.reshape(v.shape)
                    else: valid_delta = False
                        
                    if valid_delta:
                        v = v.to(dtype)
                        delta = delta.to(dtype)
                        v.add_(delta)
                        del delta

                if v.dtype != dtype: v = v.to(dtype)
                buffer.add_tensor(k, v)
                del v
        
        os.remove(shard_file)
        gc.collect()

    buffer.flush()
    
    # 6. Upload Index (Now using correct total_size)
    print(f"Uploading Index: {index_filename} (Total Size: {buffer.total_size})")
    index_data = {"metadata": {"total_size": buffer.total_size}, "weight_map": buffer.index_map}
    
    with open(TempDir / index_filename, "w") as f:
        json.dump(index_data, f, indent=4)
        
    path_in_repo = f"{output_subfolder}/{index_filename}" if output_subfolder else index_filename
    api.upload_file(path_or_fileobj=TempDir / index_filename, path_in_repo=path_in_repo, repo_id=output_repo, token=hf_token)
    
    cleanup_temp()
    return f"Done! Merged into {buffer.shard_count} shards at {output_repo}"

# =================================================================================
# TAB 2: EXTRACT LORA
# =================================================================================

def extract_lora_layer_by_layer(model_org, model_tuned, rank, clamp):
    org = MemoryEfficientSafeOpen(model_org)
    tuned = MemoryEfficientSafeOpen(model_tuned)
    lora_sd = {}
    print("Calculating diffs...")
    for key in tqdm(org.keys()):
        if key not in tuned.keys(): continue
        mat_org = org.get_tensor(key).float()
        mat_tuned = tuned.get_tensor(key).float()
        diff = mat_tuned - mat_org
        if torch.max(torch.abs(diff)) < 1e-4: continue
        
        out_dim, in_dim = diff.shape[:2]
        r = min(rank, in_dim, out_dim)
        is_conv = len(diff.shape) == 4
        if is_conv: diff = diff.flatten(start_dim=1)
            
        try:
            U, S, Vh = torch.linalg.svd(diff, full_matrices=False)
            U, S, Vh = U[:, :r], S[:r], Vh[:r, :]
            U = U @ torch.diag(S)
            dist = torch.cat([U.flatten(), Vh.flatten()])
            hi_val = torch.quantile(dist, clamp)
            U = U.clamp(-hi_val, hi_val)
            Vh = Vh.clamp(-hi_val, hi_val)
            if is_conv:
                U = U.reshape(out_dim, r, 1, 1)
                Vh = Vh.reshape(r, in_dim, mat_org.shape[2], mat_org.shape[3])
            else:
                U = U.reshape(out_dim, r)
                Vh = Vh.reshape(r, in_dim)
            stem = key.replace(".weight", "")
            lora_sd[f"{stem}.lora_up.weight"] = U
            lora_sd[f"{stem}.lora_down.weight"] = Vh
            lora_sd[f"{stem}.alpha"] = torch.tensor(r).float()
        except: pass
    out = TempDir / "extracted.safetensors"
    save_file(lora_sd, out)
    return str(out)

def task_extract(hf_token, org, tun, rank, out):
    cleanup_temp()
    login(hf_token)
    try:
        p1 = download_file(org, hf_token, filename="org.safetensors")
        p2 = download_file(tun, hf_token, filename="tun.safetensors")
        f = extract_lora_layer_by_layer(p1, p2, int(rank), 0.99)
        api.create_repo(repo_id=out, exist_ok=True, token=hf_token)
        api.upload_file(path_or_fileobj=f, path_in_repo="extracted.safetensors", repo_id=out, token=hf_token)
        return "Done"
    except Exception as e: return f"Error: {e}"

# =================================================================================
# TAB 3: MERGE ADAPTERS (EMA) with Sigma Rel
# =================================================================================

def sigma_rel_to_gamma(sigma_rel):
    t = sigma_rel**-2
    coeffs = [1, 7, 16 - t, 12 - t]
    roots = np.roots(coeffs)
    gamma = roots[np.isreal(roots) & (roots.real >= 0)].real.max()
    return gamma

def task_merge_adapters(hf_token, lora_urls, beta, sigma_rel, out_repo):
    cleanup_temp()
    login(hf_token)
    
    urls = [u.strip() for u in lora_urls.split(",") if u.strip()]
    paths = []
    try:
        for i, url in enumerate(urls):
            paths.append(download_file(url, hf_token, filename=f"a_{i}.safetensors"))
    except Exception as e: return f"Download Error: {e}"
        
    if not paths: return "No models found"
    
    base_sd = load_file(paths[0], device="cpu")
    for k in base_sd:
        if base_sd[k].dtype.is_floating_point: base_sd[k] = base_sd[k].float()
            
    gamma = None
    if sigma_rel > 0:
        gamma = sigma_rel_to_gamma(sigma_rel)
        
    for i, path in enumerate(paths[1:]):
        print(f"Merging {path}")
        if gamma is not None:
            t = i + 1
            current_beta = (1 - 1 / t) ** (gamma + 1)
        else:
            current_beta = beta 
            
        curr = load_file(path, device="cpu")
        for k in base_sd:
            if k in curr and "alpha" not in k:
                base_sd[k] = base_sd[k] * current_beta + curr[k].float() * (1 - current_beta)
                
    out = TempDir / "merged_adapters.safetensors"
    save_file(base_sd, out)
    api.create_repo(repo_id=out_repo, exist_ok=True, token=hf_token)
    api.upload_file(path_or_fileobj=out, path_in_repo="merged_adapters.safetensors", repo_id=out_repo, token=hf_token)
    return "Done"

# =================================================================================
# TAB 4: RESIZE
# =================================================================================

def index_sv_ratio(S, target):
    max_sv = S[0]
    min_sv = max_sv / target
    index = int(torch.sum(S > min_sv).item())
    return max(1, min(index, len(S) - 1))

def task_resize(hf_token, lora_input, new_rank, dynamic_method, dynamic_param, out_repo):
    cleanup_temp()
    login(hf_token)
    try:
        path = download_file(lora_input, hf_token)
    except Exception as e: return f"Error: {e}"

    state = load_file(path, device="cpu")
    new_state = {}
    
    groups = {}
    for k in state:
        stem = get_key_stem(k)
        simple = k.split(".lora_")[0] 
        if simple not in groups: groups[simple] = {}
        if "lora_down" in k or "lora_A" in k: groups[simple]["down"] = state[k]
        if "lora_up" in k or "lora_B" in k: groups[simple]["up"] = state[k]
        if "alpha" in k: groups[simple]["alpha"] = state[k]

    for stem, g in tqdm(groups.items()):
        if "down" in g and "up" in g:
            down, up = g["down"].float(), g["up"].float()
            
            if len(down.shape) == 4:
                merged = (up.squeeze() @ down.squeeze()).reshape(up.shape[0], down.shape[1], down.shape[2], down.shape[3])
                flat = merged.flatten(1)
            else:
                merged = up @ down
                flat = merged
            
            U, S, Vh = torch.linalg.svd(flat, full_matrices=False)
            
            target_rank = int(new_rank)
            if dynamic_method == "sv_ratio":
                target_rank = index_sv_ratio(S, dynamic_param)
            
            target_rank = min(target_rank, S.shape[0])
            
            U = U[:, :target_rank]
            S = S[:target_rank]
            U = U @ torch.diag(S)
            Vh = Vh[:target_rank, :]
            
            if len(down.shape) == 4:
                U = U.reshape(up.shape[0], target_rank, 1, 1)
                Vh = Vh.reshape(target_rank, down.shape[1], down.shape[2], down.shape[3])
                
            new_state[f"{stem}.lora_down.weight"] = Vh
            new_state[f"{stem}.lora_up.weight"] = U
            new_state[f"{stem}.alpha"] = torch.tensor(target_rank).float()

    out = TempDir / "resized.safetensors"
    save_file(new_state, out)
    api.create_repo(repo_id=out_repo, exist_ok=True, token=hf_token)
    api.upload_file(path_or_fileobj=out, path_in_repo="resized.safetensors", repo_id=out_repo, token=hf_token)
    return "Done"

# =================================================================================
# UI
# =================================================================================

css = ".container { max-width: 900px; margin: auto; }"

with gr.Blocks() as demo:
    gr.Markdown("# 🧰SOONmerge® LoRA Toolkit")
    
    with gr.Tabs():
        with gr.Tab("Merge to Base + Reshard Output"):
            t1_token = gr.Textbox(label="Token", type="password")
            t1_base = gr.Textbox(label="Base Repo (Diffusers)", value="ostris/Z-Image-De-Turbo")
            t1_sub = gr.Textbox(label="Subfolder", value="transformer")
            t1_lora = gr.Textbox(label="LoRA Direct Link", value="https://huggingface.co/GuangyuanSD/Z-Image-Re-Turbo-LoRA/resolve/main/Z-image_re_turbo_lora_8steps_rank_32_v1_fp16.safetensors")
            with gr.Row():
                t1_scale = gr.Slider(label="Scale", value=1.0, minimum=0, maximum=3.0, step=0.1)
                t1_prec = gr.Radio(["bf16", "fp16", "float32"], value="bf16", label="Precision")
                t1_shard = gr.Slider(label="Shard Size (GB)", value=2.0, minimum=0.1, maximum=10.0, step=0.1)
            t1_out = gr.Textbox(label="Output Repo")
            t1_struct = gr.Textbox(label="Diffusers Extras (Copies VAE/TextEnc/etc)", value="Tongyi-MAI/Z-Image-Turbo")
            t1_priv = gr.Checkbox(label="Private", value=True)
            t1_btn = gr.Button("Merge")
            t1_res = gr.Textbox(label="Result")
            t1_btn.click(task_merge, [t1_token, t1_base, t1_sub, t1_lora, t1_scale, t1_prec, t1_shard, t1_out, t1_struct, t1_priv], t1_res)

        with gr.Tab("Extract Adapter"):
            t2_token = gr.Textbox(label="Token", type="password")
            t2_org = gr.Textbox(label="Original Model")
            t2_tun = gr.Textbox(label="Tuned Model")
            t2_rank = gr.Number(label="Extract At Rank", value=32, minimum=1, maximum=1024, step=1)
            t2_out = gr.Textbox(label="Output Repo")
            t2_btn = gr.Button("Extract")
            t2_res = gr.Textbox(label="Result")
            t2_btn.click(task_extract, [t2_token, t2_org, t2_tun, t2_rank, t2_out], t2_res)

        with gr.Tab("Merge Multiple Adapters"):
            t3_token = gr.Textbox(label="Token", type="password")
            t3_urls = gr.Textbox(label="URLs")
            with gr.Row():
                t3_beta = gr.Slider(label="Beta", value=0.95, minimum=0.01, maximum=1.00, step=0.01)
                t3_sigma = gr.Slider(label="Sigma Rel (Overrides Beta)", value=0.21, minimum=0.01, maximum=1.00, step=0.01)
            t3_out = gr.Textbox(label="Output Repo")
            t3_btn = gr.Button("Merge")
            t3_res = gr.Textbox(label="Result")
            t3_btn.click(task_merge_adapters, [t3_token, t3_urls, t3_beta, t3_sigma, t3_out], t3_res)

        with gr.Tab("Resize Adapter"):
            t4_token = gr.Textbox(label="Token", type="password")
            t4_in = gr.Textbox(label="LoRA")
            with gr.Row():
                t4_rank = gr.Number(label="To Rank (Lower Only!)", value=8, minimum=1, maximum=256, step=1)
                t4_method = gr.Dropdown(["None", "sv_ratio"], value="None", label="Dynamic Method")
                t4_param = gr.Number(label="Dynamic Param", value=4.0)
            t4_out = gr.Textbox(label="Output")
            t4_btn = gr.Button("Resize")
            t4_res = gr.Textbox(label="Result")
            t4_btn.click(task_resize, [t4_token, t4_in, t4_rank, t4_method, t4_param, t4_out], t4_res)

if __name__ == "__main__":
    demo.queue().launch(css=css, ssr_mode=False)