Create app.py

app.py

@@ -0,0 +1,1141 @@
+import gradio as gr
+import torch
+import os
+import gc
+import shutil
+import requests
+import json
+import struct
+import numpy as np
+import re
+import yaml
+from merge_utils import execute_mergekit
+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:
+    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 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
+# =================================================================================
+
+def parse_hf_url(url):
+    """Parses a direct HF URL into repo_id and filename."""
+    # Pattern: https://huggingface.co/{user}/{repo}/resolve/{branch}/{filename...}
+    if "huggingface.co" in url and "resolve" in url:
+        try:
+            parts = url.split("huggingface.co/")[-1].split("/")
+            # parts[0]=user, parts[1]=repo, parts[2]=resolve, parts[3]=branch, parts[4:]=file
+            repo_id = f"{parts[0]}/{parts[1]}"
+            filename = "/".join(parts[4:]).split("?")[0] # Strip query params
+            return repo_id, filename
+        except:
+            return None, None
+    return None, None
+
+def download_lora_smart(input_str, token):
+    local_path = TempDir / "adapter.safetensors"
+    if local_path.exists(): os.remove(local_path)
+    
+    print(f"Resolving LoRA Input: {input_str}")
+    
+    # 1. Try Parse as HF URL (Most Robust Method)
+    repo_id, filename = parse_hf_url(input_str)
+    if repo_id and filename:
+        print(f"Detected HF URL. Repo: {repo_id}, File: {filename}")
+        try:
+            hf_hub_download(repo_id=repo_id, filename=filename, token=token, local_dir=TempDir)
+            # Move to standard name
+            found = list(TempDir.rglob(filename.split("/")[-1]))[0] # Handle subfolder downloads
+            if found != local_path: shutil.move(found, local_path)
+            return local_path
+        except Exception as e:
+            print(f"HF Download failed: {e}. Falling back...")
+
+    # 2. Try as Raw Repo ID (User/Repo)
+    try:
+        # Check if user put "User/Repo/file.safetensors"
+        if ".safetensors" in input_str and input_str.count("/") >= 2:
+            parts = input_str.split("/")
+            repo_id = f"{parts[0]}/{parts[1]}"
+            filename = "/".join(parts[2:])
+            hf_hub_download(repo_id=repo_id, filename=filename, token=token, local_dir=TempDir)
+            found = list(TempDir.rglob(filename.split("/")[-1]))[0]
+            if found != local_path: shutil.move(found, local_path)
+            return local_path
+            
+        # Standard Auto-Discovery
+        candidates = ["adapter_model.safetensors", "model.safetensors"]
+        files = list_repo_files(repo_id=input_str, token=token)
+        target = next((f for f in files if f in candidates), None)
+        if not target:
+            safes = [f for f in files if f.endswith(".safetensors")]
+            if safes: target = safes[0]
+        
+        if not target: raise ValueError("No safetensors found")
+        
+        hf_hub_download(repo_id=input_str, filename=target, token=token, local_dir=TempDir)
+        found = list(TempDir.rglob(target.split("/")[-1]))[0]
+        if found != local_path: shutil.move(found, local_path)
+        return local_path
+        
+    except Exception as e:
+        # 3. Last Resort: Raw Requests (For non-HF links)
+        if input_str.startswith("http"):
+            try:
+                headers = {"Authorization": f"Bearer {token}"} if token else {}
+                r = requests.get(input_str, stream=True, headers=headers, timeout=60)
+                r.raise_for_status()
+                with open(local_path, 'wb') as f:
+                    for chunk in r.iter_content(chunk_size=8192): f.write(chunk)
+                return local_path
+            except Exception as req_e:
+                raise ValueError(f"All download methods failed.\nRepo Logic Error: {e}\nURL Logic Error: {req_e}")
+        raise e
+
+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
+
+    def add_tensor(self, key, tensor):
+        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
+        
+        if self.current_bytes >= self.max_bytes:
+            self.flush()
+            
+    def flush(self):
+        if not self.buffer: return
+        self.shard_count += 1
+        
+        filename = f"{self.filename_prefix}-{self.shard_count:05d}.safetensors"
+        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
+            
+        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 copy_auxiliary_configs(hf_token, base_repo, base_subfolder, output_repo, output_subfolder):
+    """Aggressively copy all config/misc files, only skipping heavy weights."""
+    print(f"Copying config files from {base_repo}...")
+    try:
+        files = list_repo_files(repo_id=base_repo, token=hf_token)
+        blocked_ext = ['.safetensors', '.bin', '.pt', '.pth', '.msgpack', '.h5', '.onnx']
+        
+        for f in files:
+            # Filter by subfolder if needed
+            if base_subfolder and not f.startswith(base_subfolder): continue
+                
+            # Block heavy weights
+            if any(f.endswith(ext) for ext in blocked_ext): continue
+            
+            print(f"Transferring {f}...")
+            local = hf_hub_download(repo_id=base_repo, filename=f, token=hf_token, local_dir=TempDir)
+            
+            # Determine path in new repo
+            rel_name = f[len(base_subfolder):].lstrip('/') if base_subfolder else f
+            target_path = f"{output_subfolder}/{rel_name}" if output_subfolder else rel_name
+            
+            api.upload_file(path_or_fileobj=local, path_in_repo=target_path, repo_id=output_repo, token=hf_token)
+            os.remove(local)
+            
+    except Exception as e:
+        print(f"Config copy warning: {e}")
+
+def streaming_copy_structure(token, src_repo, dst_repo, ignore_prefix=None, is_root_merge=False):
+    print(f"Scanning {src_repo} for structure cloning...")
+    try:
+        files = api.list_repo_files(repo_id=src_repo, token=token)
+        for f in tqdm(files, desc="Copying Structure"):
+            if ignore_prefix and f.startswith(ignore_prefix): continue
+            
+            if is_root_merge:
+                if any(f.endswith(ext) for ext in ['.safetensors', '.bin', '.pt', '.pth']):
+                    continue
+            
+            try:
+                local = hf_hub_download(repo_id=src_repo, filename=f, token=token, local_dir=TempDir)
+                api.upload_file(path_or_fileobj=local, path_in_repo=f, repo_id=dst_repo, token=token)
+                if os.path.exists(local): os.remove(local)
+            except: pass
+    except Exception as e: print(f"Structure clone error: {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()
+    if not hf_token: return "Error: HF Token required."
+    login(hf_token.strip())
+    
+    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}"
+    
+    # Logic: If using a subfolder like 'transformer', we want standard diffusers naming
+    output_subfolder = base_subfolder if base_subfolder else "" 
+    
+    # 2. Copy Configs from Base (Aggressive Copy)
+    if base_subfolder:
+        copy_auxiliary_configs(hf_token, base_repo, base_subfolder, output_repo, output_subfolder)
+    
+    # 3. Clone Structure Repo
+    if structure_repo:
+        ignore = output_subfolder if output_subfolder else None
+        streaming_copy_structure(hf_token, structure_repo, output_repo, ignore_prefix=ignore, is_root_merge=not bool(output_subfolder))
+
+    # 4. Download Shards
+    progress(0.1, desc="Downloading Input Model...")
+    files = list_repo_files(repo_id=base_repo, token=hf_token)
+    input_shards = []
+    
+    for f in files:
+        if f.endswith(".safetensors"):
+            if output_subfolder and not f.startswith(output_subfolder): continue
+            
+            local = TempDir / "inputs" / os.path.basename(f)
+            os.makedirs(local.parent, exist_ok=True)
+            hf_hub_download(repo_id=base_repo, filename=f, token=hf_token, local_dir=local.parent, local_dir_use_symlinks=False)
+            found = list(local.parent.rglob(os.path.basename(f)))
+            if found: input_shards.append(found[0])
+
+    if not input_shards: return "No safetensors found."
+    input_shards.sort()
+
+    # --- NAMING CONVENTION ---
+    # Force diffusion naming if target is transformer/unet
+    if output_subfolder in ["transformer", "unet", "qint4", "qint8"]:
+        filename_prefix = "diffusion_pytorch_model"
+        index_filename = "diffusion_pytorch_model.safetensors.index.json"
+    elif "diffusion_pytorch_model" in os.path.basename(input_shards[0]):
+        filename_prefix = "diffusion_pytorch_model"
+        index_filename = "diffusion_pytorch_model.safetensors.index.json"
+    else:
+        filename_prefix = "model"
+        index_filename = "model.safetensors.index.json"
+        
+    print(f"Naming scheme: {filename_prefix}")
+
+    # 5. 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}"
+
+    # 6. Stream
+    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)
+                match = lora_pairs.get(base_stem)
+                
+                # QKV Heuristics
+                if not match:
+                     if "to_q" in base_stem:
+                        qkv = base_stem.replace("to_q", "qkv")
+                        match = lora_pairs.get(qkv)
+                     elif "to_k" in base_stem:
+                        qkv = base_stem.replace("to_k", "qkv")
+                        match = lora_pairs.get(qkv)
+                     elif "to_v" in base_stem:
+                        qkv = base_stem.replace("to_v", "qkv")
+                        match = lora_pairs.get(qkv)
+
+                if 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 = 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 = False
+                    elif delta.numel() == v.numel(): delta = delta.reshape(v.shape)
+                    else: valid = False
+                        
+                    if valid:
+                        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()
+    
+    print(f"Uploading Index: {index_filename} (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 {buffer.shard_count} shards to {output_repo}"
+
+# =================================================================================
+# TAB 2: EXTRACT LORA
+# =================================================================================
+
+def identify_and_download_model(input_str, token):
+    """
+    Smart download: 
+    1. Checks if input is a direct URL -> downloads specific file.
+    2. If input is a Repo ID -> scans for diffusers format (unet/transformer) or standard safetensors.
+    """
+    print(f"Resolving model input: {input_str}")
+    
+    # --- STRATEGY A: Direct URL ---
+    repo_id_from_url, filename_from_url = parse_hf_url(input_str)
+    
+    if repo_id_from_url and filename_from_url:
+        print(f"Detected Direct Link. Repo: {repo_id_from_url}, File: {filename_from_url}")
+        local_path = TempDir / os.path.basename(filename_from_url)
+        # Clean up previous download if name conflicts
+        if local_path.exists(): os.remove(local_path)
+        
+        try:
+            hf_hub_download(repo_id=repo_id_from_url, filename=filename_from_url, token=token, local_dir=TempDir)
+            # Find where it landed (handling subfolders in local_dir)
+            found = list(TempDir.rglob(os.path.basename(filename_from_url)))[0]
+            return found
+        except Exception as e:
+            print(f"URL Download failed: {e}. Trying fallback...")
+
+    # --- STRATEGY B: Repo Discovery (Auto-Detect) ---
+    # If we are here, input_str is treated as a Repo ID (e.g. "ostris/Z-Image-De-Turbo")
+    print(f"Scanning Repo {input_str} for model weights...")
+    
+    try:
+        files = list_repo_files(repo_id=input_str, token=token)
+    except Exception as e:
+        raise ValueError(f"Failed to list repo '{input_str}'. If this is a URL, ensure it is formatted correctly. Error: {e}")
+
+    # Priority list for diffusers vs single file
+    priorities = [
+        "transformer/diffusion_pytorch_model.safetensors",
+        "unet/diffusion_pytorch_model.safetensors",
+        "model.safetensors",
+        # Fallback to any safetensors that isn't an adapter or lora
+        lambda f: f.endswith(".safetensors") and "lora" not in f and "adapter" not in f and "extracted" not in f
+    ]
+    
+    target_file = None
+    for p in priorities:
+        if callable(p):
+            candidates = [f for f in files if p(f)]
+            if candidates:
+                # Pick the largest file if multiple candidates (heuristic for "main" model)
+                target_file = candidates[0] 
+                break
+        elif p in files:
+            target_file = p
+            break
+            
+    if not target_file:
+        raise ValueError(f"Could not find a valid model weight file in {input_str}. Ensure it contains .safetensors weights.")
+        
+    print(f"Downloading auto-detected weight file: {target_file}")
+    hf_hub_download(repo_id=input_str, filename=target_file, token=token, local_dir=TempDir)
+    
+    # Locate actual path
+    found = list(TempDir.rglob(os.path.basename(target_file)))[0]
+    return found
+
+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 & extracting LoRA...")
+    
+    # Get intersection of keys
+    keys = set(org.keys()).intersection(set(tuned.keys()))
+    
+    for key in tqdm(keys, desc="Extracting"):
+        # Skip integer buffers/metadata
+        if "num_batches_tracked" in key or "running_mean" in key or "running_var" in key:
+            continue
+            
+        mat_org = org.get_tensor(key).float()
+        mat_tuned = tuned.get_tensor(key).float()
+        
+        # Skip if shapes mismatch (shouldn't happen if models match)
+        if mat_org.shape != mat_tuned.shape: continue
+        
+        diff = mat_tuned - mat_org
+        
+        # Skip if no difference
+        if torch.max(torch.abs(diff)) < 1e-4: continue
+        
+        out_dim = diff.shape[0]
+        in_dim = diff.shape[1] if len(diff.shape) > 1 else 1
+        
+        r = min(rank, in_dim, out_dim)
+        
+        is_conv = len(diff.shape) == 4
+        if is_conv: diff = diff.flatten(start_dim=1)
+        elif len(diff.shape) == 1: diff = diff.unsqueeze(1) # Handle biases if needed
+            
+        try:
+            # Use svd_lowrank for massive speedup on CPU vs linalg.svd
+            U, S, V = torch.svd_lowrank(diff, q=r+4, niter=4)
+            Vh = V.t()
+            
+            U = U[:, :r]
+            S = S[:r]
+            Vh = Vh[:r, :]
+            
+            # Merge S into U for standard LoRA format
+            U = U @ torch.diag(S)
+            
+            # Clamp outliers
+            dist = torch.cat([U.flatten(), Vh.flatten()])
+            hi_val = torch.quantile(torch.abs(dist), clamp)
+            if hi_val > 0:
+                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.contiguous()
+            lora_sd[f"{stem}.lora_down.weight"] = Vh.contiguous()
+            lora_sd[f"{stem}.alpha"] = torch.tensor(r).float()
+        except Exception as e:
+            print(f"Skipping {key} due to error: {e}")
+            pass
+            
+    out = TempDir / "extracted.safetensors"
+    save_file(lora_sd, out)
+    return str(out)
+
+def task_extract(hf_token, org, tun, rank, out):
+    cleanup_temp()
+    if hf_token: login(hf_token.strip())
+    try:
+        print("Downloading Original Model...")
+        p1 = identify_and_download_model(org, hf_token)
+        print("Downloading Tuned Model...")
+        p2 = identify_and_download_model(tun, hf_token)
+        
+        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_lora.safetensors", repo_id=out, token=hf_token)
+        return "Done! Extracted to " + out
+    except Exception as e: return f"Error: {e}"
+
+# =================================================================================
+# TAB 3: MERGE ADAPTERS (Multi-Method)
+# =================================================================================
+
+def load_full_state_dict(path):
+    """Loads a safetensor file and cleans keys for easier processing."""
+    raw = load_file(path, device="cpu")
+    cleaned = {}
+    for k, v in raw.items():
+        # Map common keys to standard "lora_up/lora_down"
+        if "lora_A" in k: new_k = k.replace("lora_A", "lora_down")
+        elif "lora_B" in k: new_k = k.replace("lora_B", "lora_up")
+        else: new_k = k
+        cleaned[new_k] = v.float()
+    return cleaned
+
+# --- Original EMA Method ---
+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 merge_lora_iterative_ema(paths, beta, sigma_rel):
+    print("Executing Iterative EMA Merge (Original Method)...")
+    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)
+    return base_sd
+
+# --- New Concatenation Method (DiffSynth) ---
+def merge_lora_concatenation(adapter_states, weights):
+    """
+    DiffSynth Method: Concatenates ranks.
+    New Rank = sum(ranks). Lossless merging.
+    """
+    print("Executing Concatenation Merge (Rank Summation)...")
+    merged_state = {}
+    
+    # Identify all stems (layers) present across all adapters
+    all_stems = set()
+    for state in adapter_states:
+        for k in state.keys():
+            stem = k.split(".lora_")[0]
+            if "lora_" in k: all_stems.add(stem)
+    
+    for stem in tqdm(all_stems, desc="Concatenating Layers"):
+        down_list = []
+        up_list = []
+        alpha_sum = 0.0
+        
+        for i, state in enumerate(adapter_states):
+            w = weights[i]
+            down_key = f"{stem}.lora_down.weight"
+            up_key = f"{stem}.lora_up.weight"
+            alpha_key = f"{stem}.alpha"
+            
+            if down_key in state and up_key in state:
+                d = state[down_key]
+                u = state[up_key] * w # weighted contribution applied to UP
+                
+                down_list.append(d)
+                up_list.append(u)
+                
+                if alpha_key in state:
+                    alpha_sum += state[alpha_key].item()
+                else:
+                    alpha_sum += d.shape[0] 
+                    
+        if down_list and up_list:
+            # Concat Down (A) along dim 0 (output of A, input to B) - Wait, lora_A is (rank, in)
+            # Concat Up (B) along dim 1 (input of B) - lora_B is (out, rank)
+            # Reference: DiffSynth code: lora_A = concat(tensors_A, dim=0), lora_B = concat(tensors_B, dim=1)
+            
+            new_down = torch.cat(down_list, dim=0) # (sum_rank, in)
+            new_up = torch.cat(up_list, dim=1)     # (out, sum_rank)
+            
+            merged_state[f"{stem}.lora_down.weight"] = new_down.contiguous()
+            merged_state[f"{stem}.lora_up.weight"] = new_up.contiguous()
+            merged_state[f"{stem}.alpha"] = torch.tensor(alpha_sum)
+            
+    return merged_state
+
+# --- New SVD/Task Arithmetic Method ---
+def merge_lora_svd(adapter_states, weights, target_rank):
+    """
+    SVD / Task Arithmetic Method:
+    1. Calculate Delta W for each adapter: dW = B @ A
+    2. Sum Delta Ws: Total dW = sum(weight_i * dW_i)
+    3. SVD(Total dW) -> New B, New A at target_rank
+    """
+    print(f"Executing SVD Merge (Target Rank: {target_rank})...")
+    merged_state = {}
+    
+    all_stems = set()
+    for state in adapter_states:
+        for k in state.keys():
+            stem = k.split(".lora_")[0]
+            if "lora_" in k: all_stems.add(stem)
+            
+    for stem in tqdm(all_stems, desc="SVD Merging Layers"):
+        total_delta = None
+        valid_layer = False
+        
+        for i, state in enumerate(adapter_states):
+            w = weights[i]
+            down_key = f"{stem}.lora_down.weight"
+            up_key = f"{stem}.lora_up.weight"
+            alpha_key = f"{stem}.alpha"
+            
+            if down_key in state and up_key in state:
+                down = state[down_key]
+                up = state[up_key]
+                alpha = state[alpha_key].item() if alpha_key in state else down.shape[0]
+                rank = down.shape[0]
+                
+                scale = (alpha / rank) * w
+                
+                # Reconstruct Delta
+                if len(down.shape) == 4: # Conv2d
+                    d_flat = down.flatten(start_dim=1)
+                    u_flat = up.flatten(start_dim=1)
+                    delta = (u_flat @ d_flat).reshape(up.shape[0], down.shape[1], down.shape[2], down.shape[3])
+                else:
+                    delta = up @ down
+                    
+                delta = delta * scale
+                
+                if total_delta is None:
+                    total_delta = delta
+                    valid_layer = True
+                else:
+                    if total_delta.shape == delta.shape:
+                        total_delta += delta
+                    else:
+                        print(f"Shape mismatch in {stem}, skipping.")
+        
+        if valid_layer and total_delta is not None:
+            out_dim = total_delta.shape[0]
+            in_dim = total_delta.shape[1]
+            is_conv = len(total_delta.shape) == 4
+            
+            if is_conv:
+                flat_delta = total_delta.flatten(start_dim=1)
+            else:
+                flat_delta = total_delta
+                
+            try:
+                U, S, V = torch.svd_lowrank(flat_delta, q=target_rank + 4, niter=4)
+                Vh = V.t()
+                
+                U = U[:, :target_rank]
+                S = S[:target_rank]
+                Vh = Vh[:target_rank, :]
+                
+                U = U @ torch.diag(S)
+                
+                if is_conv:
+                    U = U.reshape(out_dim, target_rank, 1, 1)
+                    Vh = Vh.reshape(target_rank, in_dim, total_delta.shape[2], total_delta.shape[3])
+                else:
+                    U = U.reshape(out_dim, target_rank)
+                    Vh = Vh.reshape(target_rank, in_dim)
+                    
+                merged_state[f"{stem}.lora_down.weight"] = Vh.contiguous()
+                merged_state[f"{stem}.lora_up.weight"] = U.contiguous()
+                merged_state[f"{stem}.alpha"] = torch.tensor(target_rank).float()
+            except Exception as e:
+                print(f"SVD Failed for {stem}: {e}")
+
+    return merged_state
+
+def task_merge_adapters_advanced(hf_token, inputs_text, method, weight_str, beta, sigma_rel, target_rank, out_repo, private):
+    cleanup_temp()
+    if hf_token: login(hf_token.strip())
+    
+    if not out_repo or not out_repo.strip():
+        return "Error: Output Repo cannot be empty."
+
+    # 1. Parse Inputs (Multi-line support)
+    raw_lines = inputs_text.replace(" ", "\n").split('\n')
+    urls = [line.strip() for line in raw_lines if line.strip()]
+    if len(urls) < 2: return "Error: Please provide at least 2 adapters."
+    
+    # 2. Parse Weights (for SVD/Concatenation)
+    try:
+        if not weight_str.strip():
+            weights = [1.0] * len(urls)
+        else:
+            weights = [float(w.strip()) for w in weight_str.split(',')]
+            # Broadcast or Truncate
+            if len(weights) < len(urls):
+                weights += [1.0] * (len(urls) - len(weights))
+            else:
+                weights = weights[:len(urls)]
+    except:
+        return "Error parsing weights. Use format: 1.0, 0.5, 0.8"
+
+    # 3. Download All
+    paths = []
+    try:
+        for url in tqdm(urls, desc="Downloading Adapters"):
+            paths.append(download_lora_smart(url, hf_token))
+    except Exception as e: return f"Download Error: {e}"
+
+    merged = None
+
+    # 4. Execute Selected Method
+    if "Iterative EMA" in method:
+        # Calls the original method logic exactly
+        merged = merge_lora_iterative_ema(paths, beta, sigma_rel)
+    
+    else:
+        # For new methods, we load everything upfront
+        states = [load_full_state_dict(p) for p in paths]
+        
+        if "Concatenation" in method:
+            merged = merge_lora_concatenation(states, weights)
+        elif "SVD" in method:
+            merged = merge_lora_svd(states, weights, int(target_rank))
+
+    if not merged: return "Merge failed (Result empty)."
+
+    # 5. Save & Upload
+    out = TempDir / "merged_adapters.safetensors"
+    save_file(merged, out)
+    
+    try:
+        api.create_repo(repo_id=out_repo, private=private, 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 f"Success! Merged to {out_repo}"
+    except Exception as e: return f"Upload Error: {e}"
+
+# =================================================================================
+# TAB 4: RESIZE (CPU Optimized)
+# =================================================================================
+
+def index_sv_cumulative(S, target):
+    """Cumulative sum retention."""
+    original_sum = float(torch.sum(S))
+    cumulative_sums = torch.cumsum(S, dim=0) / original_sum
+    index = int(torch.searchsorted(cumulative_sums, target)) + 1
+    index = max(1, min(index, len(S) - 1))
+    return index
+
+def index_sv_fro(S, target):
+    """Frobenius norm retention (squared sum)."""
+    S_squared = S.pow(2)
+    S_fro_sq = float(torch.sum(S_squared))
+    sum_S_squared = torch.cumsum(S_squared, dim=0) / S_fro_sq
+    index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
+    index = max(1, min(index, len(S) - 1))
+    return index
+
+def index_sv_ratio(S, target):
+    """Ratio between max and min singular value."""
+    max_sv = S[0]
+    min_sv = max_sv / target
+    index = int(torch.sum(S > min_sv).item())
+    index = max(1, min(index, len(S) - 1))
+    return index
+
+def task_resize(hf_token, lora_input, new_rank, dynamic_method, dynamic_param, out_repo):
+    cleanup_temp()
+    if not hf_token: return "Error: Token required"
+    login(hf_token.strip())
+    
+    try:
+        path = download_lora_smart(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]
+
+    print(f"Resizing {len(groups)} blocks...")
+    
+    # Pre-parse user settings
+    target_rank_limit = int(new_rank)
+    if dynamic_method == "None": dynamic_method = None
+    
+    for stem, g in tqdm(groups.items()):
+        if "down" in g and "up" in g:
+            down, up = g["down"].float(), g["up"].float()
+            
+            # 1. Merge Up/Down to get full weight delta
+            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
+            
+            # 2. FAST SVD (svd_lowrank)
+            # Use the "To Rank" input as a computational hard limit + buffer.
+            # This ensures we don't compute expensive full SVD for massive layers.
+            q_limit = target_rank_limit + 32 # Buffer to allow dynamic methods some wiggle room before truncation
+            q = min(q_limit, min(flat.shape))
+            
+            U, S, V = torch.svd_lowrank(flat, q=q)
+            Vh = V.t() 
+            
+            # 3. Dynamic Rank Selection
+            calculated_rank = target_rank_limit
+            
+            if dynamic_method == "sv_ratio":
+                calculated_rank = index_sv_ratio(S, dynamic_param)
+            elif dynamic_method == "sv_cumulative":
+                calculated_rank = index_sv_cumulative(S, dynamic_param)
+            elif dynamic_method == "sv_fro":
+                calculated_rank = index_sv_fro(S, dynamic_param)
+                
+            # Apply Hard Limit (User's "To Rank")
+            final_rank = min(calculated_rank, target_rank_limit, S.shape[0])
+            
+            # 4. Truncate
+            U = U[:, :final_rank]
+            S = S[:final_rank]
+            Vh = Vh[:final_rank, :]
+            
+            # 5. Reconstruct Up Matrix (Absorb S into U)
+            U = U @ torch.diag(S)
+            
+            if len(down.shape) == 4:
+                U = U.reshape(up.shape[0], final_rank, 1, 1)
+                Vh = Vh.reshape(final_rank, down.shape[1], down.shape[2], down.shape[3])
+                
+            # 6. Save (FIX: Enforce contiguous memory layout)
+            new_state[f"{stem}.lora_down.weight"] = Vh.contiguous()
+            new_state[f"{stem}.lora_up.weight"] = U.contiguous()
+            new_state[f"{stem}.alpha"] = torch.tensor(final_rank).float()
+
+    out = TempDir / "shrunken_.safetensors"
+    # safetensors requires contiguous tensors
+    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="shrunken.safetensors", repo_id=out_repo, token=hf_token)
+    return "Done"
+
+# =================================================================================
+# NEW TAB 5: FULL MODEL MERGER (MergeKit GUI Wrapper)
+# =================================================================================
+
+def task_full_model_merge(hf_token, model_a, model_b, method, base_model, weight_a, weight_b, density, out_repo, private):
+    cleanup_temp()
+    if hf_token: login(hf_token.strip())
+    
+    # Construct a valid MergeKit YAML Config dynamically
+    config = {
+        "merge_method": method.lower(),
+        "base_model": base_model if base_model else model_a,
+        "models": [
+            {"model": model_a, "parameters": {"weight": weight_a, "density": density}},
+            {"model": model_b, "parameters": {"weight": weight_b, "density": density}}
+        ],
+        "dtype": "float16",
+        "tokenizer_source": "base"
+    }
+    
+    out_path = TempDir / "merged_model"
+    try:
+        execute_mergekit(config, str(out_path), hf_token)
+        # Push to Hub logic (reuse your existing streaming_upload logic if sharding is needed)
+        api.create_repo(repo_id=out_repo, private=private, exist_ok=True, token=hf_token)
+        api.upload_folder(folder_path=str(out_path), repo_id=out_repo, token=hf_token)
+        return f"Full model merged successfully to {out_repo}"
+    except Exception as e:
+        return f"MergeKit Error: {e}"
+
+# =================================================================================
+# NEW TAB 6: MIXTURE OF EXPERTS (MoE Creator)
+# =================================================================================
+
+def task_create_moe(hf_token, base_model, experts_list, out_repo, private):
+    cleanup_temp()
+    experts = [e.strip() for e in experts_list.split(",") if e.strip()]
+    config = {
+        "method": "moe",
+        "base_model": base_model,
+        "experts": [{"source_model": exp} for exp in experts],
+        "gate_mode": "cheap_embed" # Memory efficient for CPU
+    }
+    # [Execution logic similar to Tab 5]
+    return "MoE Model Created (Placeholder for execution logic)"
+
+# =================================================================================
+# UI
+# =================================================================================
+
+css = ".container { max-width: 900px; margin: auto; }"
+
+with gr.Blocks() as demo:
+    title = gr.HTML(
+        """<h1><img src="https://huggingface.co/spaces/AlekseyCalvin/Soon_Merger/resolve/main/SMerger3.png" alt="SOONmerge®"> Transform Transformers for FREE!</h1>""",
+        elem_id="title",
+    )
+    gr.Markdown("# 🧰SOONmerge® LoRA Toolkit")
+    
+    with gr.Tabs():
+        with gr.Tab("Merge to Base Model + Reshard Output"):
+            t1_token = gr.Textbox(label="Token", type="password")
+            t1_base = gr.Textbox(label="Base Repo", value="name/repo")
+            t1_sub = gr.Textbox(label="Subfolder (Optional)", value="")
+            t1_lora = gr.Textbox(label="LoRA Direct Link or Repo", 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="Max 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="Extras Source (copies configs/components/etc)", value="name/repo")
+            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 or Homologous 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 Adapters/Weights"):
+            gr.Markdown("### Batch Adapter Merging")
+            t3_token = gr.Textbox(label="Token", type="password")
+            t3_urls = gr.TextArea(label="Adapter URLs/Repos (one per line, or space-separated)", placeholder="user/lora1\nhttps://hf.co/user/lora2.safetensors\n...")
+            
+            with gr.Row():
+                t3_method = gr.Dropdown(
+                    ["Iterative EMA (Linear w/ Beta/Sigma coefficient)", "Concatenation (MOE-like weights-stack)", "SVD Fusion (Task Arithmetic/Compressed)"], 
+                    value="Iterative EMA (Linear w/ Beta/Sigma coefficient)", 
+                    label="Merge Method"
+                )
+            
+            with gr.Row():
+                t3_weights = gr.Textbox(label="Weights (comma-separated) – for Concat/SVD", placeholder="1.0, 0.5, 0.8...")
+                t3_rank = gr.Number(label="Target Rank – For SVD only", value=128, minimum=4, maximum=1024)
+            
+            with gr.Row():
+                t3_beta = gr.Slider(label="Beta – for linear/post-hoc EMA", value=0.95, minimum=0.01, maximum=1.00, step=0.01)
+                t3_sigma = gr.Slider(label="Sigma Rel – for linear/post-hoc EMA", value=0.21, minimum=0.01, maximum=1.00, step=0.01)
+            
+            t3_out = gr.Textbox(label="Output Repo")
+            t3_priv = gr.Checkbox(label="Private Output", value=True)
+            t3_btn = gr.Button("Merge")
+            t3_res = gr.Textbox(label="Result")
+            
+            t3_btn.click(task_merge_adapters_advanced, [t3_token, t3_urls, t3_method, t3_weights, t3_beta, t3_sigma, t3_rank, t3_out, t3_priv], 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 (Safety Ceiling)", value=8, minimum=1, maximum=512, step=1)
+                t4_method = gr.Dropdown(["None", "sv_ratio", "sv_fro", "sv_cumulative"], value="None", label="Dynamic Method")
+                t4_param = gr.Number(label="Dynamic Param", value=0.9)
+                
+            gr.Markdown(
+                """
+                ### 📉 Dynamic Resizing Guide
+                These methods intelligently determine the best rank per layer. 
+                * **sv_ratio (Relative Strength):** Keeps features that are at least `1/Param` as strong as the main feature. **Param must be >= 2**. (e.g. 2 = keep features half as strong as top).
+                * **sv_fro (Visual Information Density):** Preserves `Param%` of the total information content (Frobenius Norm) of the layer. **Param between 0.0 and 1.0** (e.g. 0.9 = 90% info retention).
+                * **sv_cumulative (Cumulative Sum):** Preserves weights that sum up to `Param%` of the total strength. **Param between 0.0 and 1.0**.
+                * **⚠️ Safety Ceiling:** The **"To Rank"** slider acts as a hard limit. Even if a dynamic method wants a higher rank, it will be cut down to this number to keep file sizes small.
+                """
+            )
+            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)
+
+        with gr.Tab("Full Model Merge (MergeKit)"):
+            gr.Markdown("### 🧩 Advanced Model Fusion (MergeKit Engine)")
+            with gr.Row():
+                t5_token = gr.Textbox(label="HF Token", type="password")
+                t5_method = gr.Dropdown(["Linear", "SLERP", "TIES", "DARE_TIES", "DARE_LINEAR"], value="TIES", label="Merge Method")
+            with gr.Row():
+                t5_model_a = gr.Textbox(label="Model A (Repo ID)")
+                t5_model_b = gr.Textbox(label="Model B (Repo ID)")
+                t5_base = gr.Textbox(label="Base Model (Optional)", placeholder="Required for TIES/DARE")
+            with gr.Row():
+                t5_weight_a = gr.Slider(0, 1, 0.5, label="Weight A")
+                t5_weight_b = gr.Slider(0, 1, 0.5, label="Weight B")
+                t5_density = gr.Slider(0, 1, 0.5, label="Density (TIES/DARE)")
+            t5_out = gr.Textbox(label="Output Repo")
+            t5_priv = gr.Checkbox(label="Private", value=True)
+            t5_btn = gr.Button("Execute Full Merge")
+            t5_res = gr.Textbox(label="Result")
+            
+            t5_btn.click(task_full_model_merge, [t5_token, t5_model_a, t5_model_b, t5_method, t5_base, t5_weight_a, t5_weight_b, t5_density, t5_out, t5_priv], t5_res)
+
+        with gr.Tab("Create MoE"):
+            gr.Markdown("### 🤖 Mixture of Experts Upscaling")
+            t6_base = gr.Textbox(label="Base Architecture Model")
+            t6_experts = gr.TextArea(label="Expert Models (Comma separated Repo IDs)")
+            t6_btn = gr.Button("Build MoE")
+
+if __name__ == "__main__":
+    demo.queue().launch(css=css, ssr_mode=False)