app_ALT.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()

# =================================================================================
# ROBUST RESHARDING LOGIC (Plan -> Execute)
# =================================================================================

def download_lora_smart(input_str, token):
    """Robust LoRA downloader that handles Direct URLs and Repo IDs."""
    local_path = TempDir / "adapter.safetensors"
    if local_path.exists(): os.remove(local_path)

    # 1. Try as Direct URL
    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=60)
            response.raise_for_status()
            with open(local_path, 'wb') as f:
                for chunk in response.iter_content(chunk_size=8192):
                    f.write(chunk)
            if verify_safetensors(local_path): return local_path
        except Exception as e:
            print(f"URL download failed: {e}. Trying as Repo ID...")

    # 2. Try as Repo ID
    print(f"Attempting download from Hub Repo: {input_str}")
    try:
        # Check if user provided a filename in the repo string (e.g. user/repo/file.safetensors)
        if ".safetensors" in input_str and "/" in input_str:
            # splitting repo_id and filename might be needed, but hf_hub_download expects valid repo_id
            pass 

        # Try to find the adapter file automatically
        files = list_repo_files(repo_id=input_str, token=token)
        candidates = ["adapter_model.safetensors", "model.safetensors"]
        target = next((f for f in files if f in candidates), None)
        
        # If no standard name, take the first safetensors found
        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)
        
        # Move to standard location
        downloaded = TempDir / target
        if downloaded != local_path:
            shutil.move(downloaded, local_path)
            
        return local_path
    except Exception as e:
        raise ValueError(f"Could not download LoRA. Checked URL and Repo. Error: {e}")

def get_tensor_byte_size(shape, dtype_str):
    """Calculates byte size of a tensor based on shape and dtype."""
    # F32=4, F16/BF16=2, I8=1, etc.
    bytes_per = 4 if "F32" in dtype_str else 2 if "16" in dtype_str else 1
    numel = 1
    for d in shape: numel *= d
    return numel * bytes_per

def plan_resharding(input_shards, max_shard_size_gb, filename_prefix):
    """
    Pass 1: Reads headers ONLY. Groups tensors into virtual shards of max_shard_size_gb.
    Returns a Plan (List of ShardDefinitions).
    """
    print(f"Planning resharding (Max {max_shard_size_gb} GB)...")
    max_bytes = int(max_shard_size_gb * 1024**3)
    
    all_tensors = []
    
    # 1. Scan all inputs
    for p in input_shards:
        with MemoryEfficientSafeOpen(p) as f:
            for k in f.keys():
                shape = f.header[k]['shape']
                dtype = f.header[k]['dtype']
                size = get_tensor_byte_size(shape, dtype)
                all_tensors.append({
                    "key": k,
                    "shape": shape,
                    "dtype": dtype,
                    "size": size,
                    "source": p
                })
    
    # 2. Sort tensors (Crucial for deterministic output)
    all_tensors.sort(key=lambda x: x["key"])
    
    # 3. Bucket into Shards
    plan = []
    current_shard = []
    current_size = 0
    
    for t in all_tensors:
        # If adding this tensor exceeds limit AND we have stuff in the bucket, close bucket
        if current_size + t['size'] > max_bytes and current_shard:
            plan.append(current_shard)
            current_shard = []
            current_size = 0
        
        current_shard.append(t)
        current_size += t['size']
        
    if current_shard:
        plan.append(current_shard)
        
    total_shards = len(plan)
    total_model_size = sum(t['size'] for shard in plan for t in shard)
    
    print(f"Plan created: {total_shards} shards. Total size: {total_model_size / 1024**3:.2f} GB")
    
    # 4. Format Plan
    final_plan = []
    for i, shard_tensors in enumerate(plan):
        # Naming: prefix-00001-of-00005.safetensors
        name = f"{filename_prefix}-{i+1:05d}-of-{total_shards:05d}.safetensors"
        final_plan.append({
            "filename": name,
            "tensors": shard_tensors
        })
        
    return final_plan, total_model_size

def copy_auxiliary_configs(hf_token, base_repo, base_subfolder, output_repo, output_subfolder):
    """
    Downloads NON-WEIGHT files (json, txt, model) from Base Repo and uploads to Output.
    """
    print(f"Copying config files from {base_repo}...")
    try:
        files = list_repo_files(repo_id=base_repo, token=hf_token)
        
        # Extensions to KEEP (Configs, Tokenizers, etc.)
        allowed_ext = ['.json', '.txt', '.model', '.py', '.yml', '.yaml']
        # Extensions to SKIP (Weights, we are generating these)
        blocked_ext = ['.safetensors', '.bin', '.pt', '.pth', '.msgpack', '.h5']
        
        for f in files:
            # Filter by subfolder if needed
            if base_subfolder and not f.startswith(base_subfolder):
                continue
                
            ext = os.path.splitext(f)[1]
            if ext in blocked_ext: continue
            if ext not in allowed_ext: continue # Skip unknown types to be safe? Or allow?
            
            # Download
            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
            if base_subfolder:
                # Remove base_subfolder prefix for the rel path
                rel_name = f[len(base_subfolder):].lstrip('/')
            else:
                rel_name = f
                
            # Add output_subfolder prefix
            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 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: Token missing."
    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}"
    
    # Determine Folder Logic
    # If base_subfolder is "qint4", and we want output to be "transformer", user needs to specify that.
    # But usually, if base has a subfolder, we maintain a subfolder structure.
    # ADAPTIVE: If base_subfolder is "qint4", we treat it as the source of weights.
    # Since you merged into "transformer", I assume you want the output in "transformer".
    # For general LLMs (root), both are empty.
    
    # Heuristic: If base has subfolder, use "transformer" as target if it looks like a DiT, else keep original name.
    if base_subfolder:
        output_subfolder = "transformer" if "qint" in base_subfolder or "transformer" in base_subfolder else base_subfolder
    else:
        output_subfolder = ""

    # 2. Copy Configs (The missing step from previous run)
    copy_auxiliary_configs(hf_token, base_repo, base_subfolder, output_repo, output_subfolder)
    
    # 3. Structure Repo (Only needed if Base doesn't have everything, e.g. VAE)
    if structure_repo:
        print(f"Copying extras from {structure_repo}...")
        # We assume structure repo is a standard diffusers repo
        # We copy text_encoder, vae, scheduler, tokenizer, etc.
        # We SKIP 'transformer' or 'unet' because we are building that.
        streaming_copy_structure(hf_token, structure_repo, output_repo, ignore_prefix="transformer")

    # 4. Download ALL Input Shards (Needed for Planning)
    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 base_subfolder and not f.startswith(base_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)
            
            # Handle nesting
            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()

    # 5. Detect Naming Convention (Adaptive)
    sample_name = os.path.basename(input_shards[0])
    if "diffusion_pytorch_model" in sample_name or output_subfolder == "transformer":
        prefix = "diffusion_pytorch_model"
        index_file = "diffusion_pytorch_model.safetensors.index.json"
    else:
        prefix = "model"
        index_file = "model.safetensors.index.json"

    # 6. Create Plan (Pass 1)
    # This calculates total shards and size BEFORE processing
    progress(0.2, desc="Planning Shards...")
    plan, total_model_size = plan_resharding(input_shards, shard_size, prefix)
    
    # 7. Load LoRA
    dtype = torch.bfloat16 if precision == "bf16" else torch.float16 if precision == "fp16" else torch.float32
    try:
        progress(0.25, desc="Loading 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"LoRA Error: {e}"

    # 8. Execute Plan (Pass 2)
    index_map = {}
    
    for i, shard_plan in enumerate(plan):
        filename = shard_plan['filename']
        tensors_to_write = shard_plan['tensors']
        
        progress(0.3 + (0.7 * i / len(plan)), desc=f"Merging {filename}")
        print(f"Generating {filename} ({len(tensors_to_write)} tensors)...")
        
        # Prepare Header
        header = {"__metadata__": {"format": "pt"}}
        current_offset = 0
        for t in tensors_to_write:
            # Recalculate dtype string for header based on TARGET dtype
            tgt_dtype_str = "BF16" if dtype == torch.bfloat16 else "F16" if dtype == torch.float16 else "F32"
            
            # Calculate output size (might differ from input size if we change precision)
            # Input size in plan was source size. We need target size.
            out_size = get_tensor_byte_size(t['shape'], tgt_dtype_str)
            
            header[t['key']] = {
                "dtype": tgt_dtype_str,
                "shape": t['shape'],
                "data_offsets": [current_offset, current_offset + out_size]
            }
            current_offset += out_size
            index_map[t['key']] = filename
            
        header_json = json.dumps(header).encode('utf-8')
        
        out_path = TempDir / filename
        with open(out_path, 'wb') as f_out:
            f_out.write(struct.pack('<Q', len(header_json)))
            f_out.write(header_json)
            
            # Open source files as needed
            open_files = {}
            
            for t_plan in tqdm(tensors_to_write, leave=False):
                src = t_plan['source']
                if src not in open_files: open_files[src] = MemoryEfficientSafeOpen(src)
                
                # Load Tensor
                v = open_files[src].get_tensor(t_plan['key'])
                k = t_plan['key']
                
                # --- MERGE LOGIC ---
                base_stem = get_key_stem(k)
                match = None
                
                # Check match (Same logic as before)
                if base_stem in lora_pairs: match = lora_pairs[base_stem]
                # ... [QKV Logic omitted for brevity, same as previous] ...
                if not match:
                     if "to_q" in base_stem:
                        qkv = base_stem.replace("to_q", "qkv")
                        if qkv in lora_pairs: match = lora_pairs[qkv]
                     elif "to_k" in base_stem:
                        qkv = base_stem.replace("to_k", "qkv")
                        if qkv in lora_pairs: match = lora_pairs[qkv]
                     elif "to_v" in base_stem:
                        qkv = base_stem.replace("to_v", "qkv")
                        if qkv in lora_pairs: match = lora_pairs[qkv]

                if match:
                    down = match["down"]
                    up = match["up"]
                    # ... [Matmul Logic, same as previous] ...
                    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
                    
                    # Slicing
                    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
                # --- END MERGE ---
                
                # Write
                if v.dtype != dtype: v = v.to(dtype)
                if dtype == torch.bfloat16:
                    raw = v.view(torch.int16).numpy().tobytes()
                else:
                    raw = v.numpy().tobytes()
                f_out.write(raw)
                del v
            
            # Close handles
            for fh in open_files.values(): fh.file.close()
            
        # Upload Shard
        path_in_repo = f"{output_subfolder}/{filename}" if output_subfolder else filename
        api.upload_file(path_or_fileobj=out_path, path_in_repo=path_in_repo, repo_id=output_repo, token=hf_token)
        os.remove(out_path)
        gc.collect()

    # 9. Upload Index
    # Update total size to reflect the TARGET dtype size, not source
    # We recalculate total_size based on what we actually wrote
    final_total_size = 0
    for t_list in plan:
        for t in t_list['tensors']:
             tgt_dtype_str = "BF16" if dtype == torch.bfloat16 else "F16" if dtype == torch.float16 else "F32"
             final_total_size += get_tensor_byte_size(t['shape'], tgt_dtype_str)

    index_data = {"metadata": {"total_size": final_total_size}, "weight_map": index_map}
    with open(TempDir / index_file, "w") as f:
        json.dump(index_data, f, indent=4)
        
    path_in_repo = f"{output_subfolder}/{index_file}" if output_subfolder else index_file
    api.upload_file(path_or_fileobj=TempDir / index_file, path_in_repo=path_in_repo, repo_id=output_repo, token=hf_token)
    
    cleanup_temp()
    return f"Success! {len(plan)} shards created 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 Repo as (name/repo)", value="GuangyuanSD/Z-Image-Re-Turbo-LoRA")
            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)