Update app.py

app.py

@@ -10,8 +10,6 @@ from huggingface_hub import HfApi, hf_hub_download
 from safetensors.torch import load_file, save_file
 import torch
 import torch.nn.functional as F
-import traceback
-import math
 try:
     from modelscope.hub.file_download import model_file_download as ms_file_download
     from modelscope.hub.api import HubApi as ModelScopeApi
@@ -20,123 +18,71 @@ except ImportError:
     MODELScope_AVAILABLE = False
 
 def low_rank_decomposition(weight, rank=64):
-    """Handle both 2D and 4D tensors for LoRA decomposition."""
+    """
+    Improved LoRA decomposition supporting 2D and 4D tensors with proper factorization.
+    Returns (lora_A, lora_B) such that weight ≈ lora_B @ lora_A (for 2D) or appropriate conv form.
+    """
     original_shape = weight.shape
     original_dtype = weight.dtype
-    
+
     try:
-        # Handle 2D tensors (linear layers, attention)
         if weight.ndim == 2:
+            actual_rank = min(rank, min(weight.shape) // 2)
+            if actual_rank < 4:
+                return None, None
+
             U, S, Vh = torch.linalg.svd(weight.float(), full_matrices=False)
-            if rank > len(S):
-                rank = len(S) // 2  # Use half the available rank if requested rank is too high
-            U = U[:, :rank] @ torch.diag(torch.sqrt(S[:rank]))
-            Vh = torch.diag(torch.sqrt(S[:rank])) @ Vh[:rank, :]
-            return U.contiguous(), Vh.contiguous()
-        
-        # Handle 4D tensors (convolutional layers)
+            S_sqrt = torch.sqrt(S[:actual_rank])
+
+            # Standard LoRA: W ≈ W_B @ W_A
+            W_A = (Vh[:actual_rank, :] * S_sqrt.unsqueeze(1)).contiguous()  # [rank, in_features]
+            W_B = (U[:, :actual_rank] * S_sqrt.unsqueeze(0)).contiguous()   # [out_features, rank]
+
+            return W_A.to(original_dtype), W_B.to(original_dtype)
+
         elif weight.ndim == 4:
-            # Strategy 1: Reshape to 2D and decompose
-            out_ch, in_ch, kH, kW = weight.shape
-            
-            # For small conv kernels, use spatial decomposition
-            if kH * kW <= 9:  # 3x3 kernel or smaller
-                weight_2d = weight.permute(0, 2, 3, 1).reshape(out_ch * kH * kW, in_ch)
-                U, S, Vh = torch.linalg.svd(weight_2d.float(), full_matrices=False)
-                
-                if rank > len(S):
-                    rank = max(8, len(S) // 2)
-                
-                U = U[:, :rank] @ torch.diag(torch.sqrt(S[:rank]))
-                Vh = torch.diag(torch.sqrt(S[:rank])) @ Vh[:rank, :]
-                
-                # Reshape back to convolutional format
-                U = U.view(out_ch, kH, kW, rank).permute(0, 3, 1, 2).contiguous()
-                Vh = Vh.view(rank, in_ch, 1, 1).contiguous()
-                return U, Vh
-            
-            # For larger kernels, use channel-wise decomposition
-            else:
-                weight_2d = weight.view(out_ch, -1)
+            out_ch, in_ch, k_h, k_w = weight.shape
+            if k_h * k_w <= 9:  # small kernel (e.g., 3x3)
+                weight_2d = weight.permute(0, 2, 3, 1).reshape(out_ch, -1)
+                actual_rank = min(rank, min(weight_2d.shape) // 2)
+                if actual_rank < 4:
+                    return None, None
+
                 U, S, Vh = torch.linalg.svd(weight_2d.float(), full_matrices=False)
-                
-                if rank > len(S):
-                    rank = max(8, len(S) // 2)
-                
-                U = U[:, :rank] @ torch.diag(torch.sqrt(S[:rank]))
-                Vh = torch.diag(torch.sqrt(S[:rank])) @ Vh[:rank, :]
-                U = U.view(out_ch, rank, 1, 1).contiguous()
-                Vh = Vh.view(rank, in_ch, kH, kW).contiguous()
-                return U, Vh
-        
-        # Handle 3D tensors (rare, but sometimes in attention mechanisms)
-        elif weight.ndim == 3:
-            out_ch, mid_ch, in_ch = weight.shape
-            weight_2d = weight.reshape(out_ch * mid_ch, in_ch)
-            U, S, Vh = torch.linalg.svd(weight_2d.float(), full_matrices=False)
-            
-            if rank > len(S):
-                rank = max(8, len(S) // 2)
-            
-            U = U[:, :rank] @ torch.diag(torch.sqrt(S[:rank]))
-            Vh = torch.diag(torch.sqrt(S[:rank])) @ Vh[:rank, :]
-            U = U.view(out_ch, mid_ch, rank).contiguous()
-            Vh = Vh.view(rank, in_ch).contiguous()
-            return U, Vh
-            
+                S_sqrt = torch.sqrt(S[:actual_rank])
+
+                W_A_2d = (Vh[:actual_rank, :] * S_sqrt.unsqueeze(1)).contiguous()
+                W_B_2d = (U[:, :actual_rank] * S_sqrt.unsqueeze(0)).contiguous()
+
+                W_A = W_A_2d.view(actual_rank, k_h, k_w, in_ch).permute(0, 3, 1, 2).contiguous()
+                W_B = W_B_2d.view(out_ch, actual_rank, 1, 1).contiguous()
+
+                return W_A.to(original_dtype), W_B.to(original_dtype)
+
+        return None, None
+
     except Exception as e:
-        print(f"Decomposition error for tensor with shape {original_shape}: {str(e)}")
-        traceback.print_exc()
-    
-    return None, None
-
-def should_apply_lora(key, weight, architecture, lora_rank):
-    """Determine if LoRA should be applied to a specific weight based on architecture selection."""
-    
-    # Skip bias terms, batchnorm, and very small tensors
-    if 'bias' in key or 'norm' in key.lower() or 'bn' in key.lower():
-        return False
-    
-    # Skip very small tensors
-    if weight.numel() < 100:
-        return False
-    
-    # Architecture-specific rules
+        print(f"Decomposition failed for {original_shape}: {e}")
+        return None, None
+
+def should_apply_lora(key, weight, architecture="auto"):
+    """Determine if LoRA should be applied based on architecture selection."""
     lower_key = key.lower()
-    
+
+    # Skip unimportant weights
+    if 'bias' in lower_key or 'norm' in lower_key or weight.numel() < 256:
+        return False
+
     if architecture == "text_encoder":
-        # Text encoder: focus on embeddings and attention layers
-        return ('emb' in lower_key or 'embed' in lower_key or 
-                'attn' in lower_key or 'qkv' in lower_key or 'mlp' in lower_key)
-    
+        return any(t in lower_key for t in ['emb', 'embed', 'attn'])
     elif architecture == "unet_transformer":
-        # UNet transformers: focus on attention blocks
-        return ('attn' in lower_key or 'transformer' in lower_key or 
-                'qkv' in lower_key or 'to_out' in lower_key)
-    
+        return any(t in lower_key for t in ['attn', 'transformer'])
     elif architecture == "unet_conv":
-        # UNet convolutional layers
-        return ('conv' in lower_key or 'resnet' in lower_key or 
-                'downsample' in lower_key or 'upsample' in lower_key)
-    
+        return any(t in lower_key for t in ['conv', 'resnet', 'down', 'up'])
     elif architecture == "vae":
-        # VAE components
-        return ('encoder' in lower_key or 'decoder' in lower_key or 
-                'conv' in lower_key or 'post_quant' in lower_key)
-    
-    elif architecture == "all":
-        # Apply to all eligible tensors
-        return True
-    
-    elif architecture == "auto":
-        # Auto-detect based on tensor properties
-        if weight.ndim == 2 and min(weight.shape) > lora_rank:
-            return True
-        if weight.ndim == 4 and (weight.shape[0] > lora_rank or weight.shape[1] > lora_rank):
-            return True
-        return False
-    
-    return False
+        return any(t in lower_key for t in ['encoder', 'decoder', 'quant'])
+    else:  # "auto" or "all"
+        return weight.ndim in [2, 4]
 
 def convert_safetensors_to_fp8_with_lora(safetensors_path, output_dir, fp8_format, lora_rank=64, architecture="auto", progress=gr.Progress()):
     progress(0.1, desc="Starting FP8 conversion with LoRA extraction...")
@@ -147,146 +93,94 @@ def convert_safetensors_to_fp8_with_lora(safetensors_path, output_dir, fp8_forma
                 header_json = f.read(header_size).decode('utf-8')
                 header = json.loads(header_json)
                 return header.get('__metadata__', {})
-        
+
         metadata = read_safetensors_metadata(safetensors_path)
         progress(0.2, desc="Loaded metadata.")
-        
+
         state_dict = load_file(safetensors_path)
         progress(0.4, desc="Loaded weights.")
-        
-        # Architecture analysis
-        architecture_stats = {
-            'text_encoder': 0,
-            'unet_transformer': 0,
-            'unet_conv': 0,
-            'vae': 0,
-            'other': 0
-        }
-        
-        for key in state_dict:
-            lower_key = key.lower()
-            if 'text' in lower_key or 'emb' in lower_key:
-                architecture_stats['text_encoder'] += 1
-            elif 'attn' in lower_key or 'transformer' in lower_key:
-                architecture_stats['unet_transformer'] += 1
-            elif 'conv' in lower_key or 'resnet' in lower_key:
-                architecture_stats['unet_conv'] += 1
-            elif 'vae' in lower_key or 'encoder' in lower_key or 'decoder' in lower_key:
-                architecture_stats['vae'] += 1
-            else:
-                architecture_stats['other'] += 1
-        
-        print("Architecture analysis:")
-        for arch, count in architecture_stats.items():
-            print(f"- {arch}: {count} layers")
-        
+
         if fp8_format == "e5m2":
             fp8_dtype = torch.float8_e5m2
         else:
             fp8_dtype = torch.float8_e4m3fn
-        
+
         sd_fp8 = {}
         lora_weights = {}
+        total = len(state_dict)
+        lora_keys = []
+
         lora_stats = {
-            'total_layers': len(state_dict),
+            'total_layers': total,
             'layers_analyzed': 0,
             'layers_eligible': 0,
             'layers_processed': 0,
             'layers_skipped': [],
-            'architecture_distro': architecture_stats
         }
-        
-        total = len(state_dict)
-        lora_keys = []
-        
+
         for i, key in enumerate(state_dict):
-            progress(0.4 + 0.4 * (i / total), desc=f"Processing {i+1}/{total}: {key.split('.')[-1]}")
+            progress(0.4 + 0.4 * (i / total), desc=f"Processing {i+1}/{total}...")
             weight = state_dict[key]
             lora_stats['layers_analyzed'] += 1
-            
+
             if weight.dtype in [torch.float16, torch.float32, torch.bfloat16]:
                 fp8_weight = weight.to(fp8_dtype)
                 sd_fp8[key] = fp8_weight
-                
-                # Determine if we should apply LoRA
-                eligible_for_lora = should_apply_lora(key, weight, architecture, lora_rank)
-                
-                if eligible_for_lora:
+
+                # Apply LoRA based on architecture selection
+                if should_apply_lora(key, weight, architecture):
                     lora_stats['layers_eligible'] += 1
-                    
+
                     try:
-                        # Adjust rank based on tensor size
-                        actual_rank = lora_rank
-                        if weight.ndim == 2:
-                            actual_rank = min(lora_rank, min(weight.shape) // 2)
-                        elif weight.ndim == 4:
-                            actual_rank = min(lora_rank, max(weight.shape[0], weight.shape[1]) // 4)
-                        
-                        if actual_rank < 4:  # Minimum rank threshold
-                            lora_stats['layers_skipped'].append(f"{key}: rank too small ({actual_rank})")
-                            continue
-                        
-                        U, V = low_rank_decomposition(weight, rank=actual_rank)
-                        if U is not None and V is not None:
-                            lora_weights[f"lora_A.{key}"] = U.to(torch.float16)
-                            lora_weights[f"lora_B.{key}"] = V.to(torch.float16)
+                        A, B = low_rank_decomposition(weight, rank=lora_rank)
+                        if A is not None and B is not None:
+                            lora_weights[f"lora_A.{key}"] = A.to(torch.float16)
+                            lora_weights[f"lora_B.{key}"] = B.to(torch.float16)
                             lora_keys.append(key)
                             lora_stats['layers_processed'] += 1
                         else:
                             lora_stats['layers_skipped'].append(f"{key}: decomposition returned None")
                     except Exception as e:
-                        error_msg = f"{key}: {str(e)}"
-                        lora_stats['layers_skipped'].append(error_msg)
-                        print(f"LoRA decomposition error: {error_msg}")
-                        traceback.print_exc()
+                        lora_stats['layers_skipped'].append(f"{key}: {str(e)}")
                 else:
-                    reason = "not eligible for selected architecture" if architecture != "auto" else f"ndim={weight.ndim}, min(shape)={min(weight.shape) if weight.ndim > 0 else 'N/A'}"
-                    lora_stats['layers_skipped'].append(f"{key}: {reason}")
+                    reason = "architecture filter" if architecture != "auto" else "not 2D/4D or too small"
+                    lora_stats['layers_skipped'].append(f"{key}: skipped ({reason})")
             else:
                 sd_fp8[key] = weight
-                lora_stats['layers_skipped'].append(f"{key}: unsupported dtype {weight.dtype}")
-        
+                lora_stats['layers_skipped'].append(f"{key}: non-float dtype")
+
         base_name = os.path.splitext(os.path.basename(safetensors_path))[0]
         fp8_path = os.path.join(output_dir, f"{base_name}-fp8-{fp8_format}.safetensors")
         lora_path = os.path.join(output_dir, f"{base_name}-lora-r{lora_rank}-{architecture}.safetensors")
-        
+
         save_file(sd_fp8, fp8_path, metadata={"format": "pt", "fp8_format": fp8_format, **metadata})
-        
-        # Always save LoRA file, even if empty
-        lora_metadata = {
-            "format": "pt", 
+
+        # Always save LoRA file (even if empty) with stats
+        save_file(lora_weights, lora_path, metadata={
+            "format": "pt",
             "lora_rank": str(lora_rank),
             "architecture": architecture,
             "stats": json.dumps(lora_stats)
-        }
-        
-        save_file(lora_weights, lora_path, metadata=lora_metadata)
-        
-        # Generate detailed statistics message
+        })
+
+        progress(0.9, desc="Saved FP8 and LoRA files.")
+        progress(1.0, desc="✅ FP8 + LoRA extraction complete!")
+
         stats_msg = f"""
-📊 LoRA Extraction Statistics:
-- Total layers analyzed: {lora_stats['layers_analyzed']}
-- Layers eligible for LoRA: {lora_stats['layers_eligible']}
+📊 LoRA Extraction Stats:
+- Total layers: {lora_stats['total_layers']}
+- Eligible for LoRA: {lora_stats['layers_eligible']}
 - Successfully processed: {lora_stats['layers_processed']}
 - Architecture: {architecture}
-- FP8 Format: {fp8_format.upper()}
-
-Top skipped layers:
-{chr(10).join(lora_stats['layers_skipped'][:10])}
 """
-        
-        progress(0.9, desc="Saved FP8 and LoRA files.")
-        progress(1.0, desc="✅ FP8 + LoRA extraction complete!")
-        
         if lora_stats['layers_processed'] == 0:
-            stats_msg += "\n\n⚠️ WARNING: No LoRA weights were generated. Try a different architecture selection or lower rank."
-        
+            stats_msg += "\n⚠️ No LoRA weights generated. Try lower rank or different architecture."
+
         return True, f"FP8 ({fp8_format}) and rank-{lora_rank} LoRA saved.\n{stats_msg}", lora_stats
 
     except Exception as e:
-        error_msg = f"Conversion error: {str(e)}\n{traceback.format_exc()}"
-        print(error_msg)
-        return False, error_msg, None
+        import traceback
+        return False, f"Error: {str(e)}\n{traceback.format_exc()}", None
 
 def parse_hf_url(url):
     url = url.strip().rstrip("/")
@@ -364,11 +258,9 @@ def process_and_upload_fp8(
         return None, "❌ Hugging Face token required for source.", ""
     if target_type == "huggingface" and not hf_token:
         return None, "❌ Hugging Face token required for target.", ""
-    
-    # Validate lora_rank
     if lora_rank < 4:
         return None, "❌ LoRA rank must be at least 4.", ""
-    
+
     temp_dir = None
     output_dir = tempfile.mkdtemp()
     try:
@@ -376,24 +268,24 @@ def process_and_upload_fp8(
         safetensors_path, temp_dir = download_safetensors_file(
             source_type, repo_url, safetensors_filename, hf_token, progress
         )
-        
+
         progress(0.25, desc=f"Converting to FP8 with LoRA ({architecture})...")
         success, msg, stats = convert_safetensors_to_fp8_with_lora(
             safetensors_path, output_dir, fp8_format, lora_rank, architecture, progress
         )
-        
+
         if not success:
             return None, f"❌ Conversion failed: {msg}", ""
-        
+
         progress(0.9, desc="Uploading...")
         repo_url_final = upload_to_target(
             target_type, new_repo_id, output_dir, fp8_format, architecture, hf_token, modelscope_token, private_repo
         )
-        
+
         base_name = os.path.splitext(safetensors_filename)[0]
         lora_filename = f"{base_name}-lora-r{lora_rank}-{architecture}.safetensors"
         fp8_filename = f"{base_name}-fp8-{fp8_format}.safetensors"
-        
+
         readme = f"""---
 library_name: diffusers
 tags:
@@ -403,7 +295,7 @@ tags:
 - low-rank
 - diffusion
 - architecture-{architecture}
-- converted-by-ai-toolkit
+- converted-by-gradio
 ---
 # FP8 Model with Low-Rank LoRA
 - **Source**: `{repo_url}`
@@ -414,16 +306,6 @@ tags:
 - **LoRA File**: `{lora_filename}`
 - **FP8 File**: `{fp8_filename}`
 
-## Architecture Distribution
-"""
-        
-        # Add architecture stats to README if available
-        if stats and 'architecture_distro' in stats:
-            readme += "\n| Component | Layer Count |\n|-----------|------------|\n"
-            for arch, count in stats['architecture_distro'].items():
-                readme += f"| {arch.replace('_', ' ').title()} | {count} |\n"
-        
-        readme += f"""
 ## Usage (Inference)
 ```python
 from safetensors.torch import load_file
@@ -436,38 +318,25 @@ lora_state = load_file("{lora_filename}")
 # Reconstruct approximate original weights
 reconstructed = {{}}
 for key in fp8_state:
-    lora_a_key = f"lora_A.{{key}}"
-    lora_b_key = f"lora_B.{{key}}"
-    
-    if lora_a_key in lora_state and lora_b_key in lora_state:
-        A = lora_state[lora_a_key].to(torch.float32)
-        B = lora_state[lora_b_key].to(torch.float32)
-        
-        # Handle different tensor dimensions
+    if f"lora_A.{{key}}" in lora_state and f"lora_B.{{key}}" in lora_state:
+        A = lora_state[f"lora_A.{{key}}"].to(torch.float32)
+        B = lora_state[f"lora_B.{{key}}"].to(torch.float32)
         if A.ndim == 2 and B.ndim == 2:
             lora_weight = B @ A
-        elif A.ndim == 4 and B.ndim == 4:
-            # For convolutional LoRA
-            lora_weight = F.conv2d(fp8_state[key].to(torch.float32), 
-                                  B, padding=1) + F.conv2d(fp8_state[key].to(torch.float32), 
-                                  A, padding=1)
         else:
-            # Fallback for mixed dimension cases
-            lora_weight = B @ A.view(B.shape[1], -1)
-            if lora_weight.shape != fp8_state[key].shape:
-                lora_weight = lora_weight.view_as(fp8_state[key])
-        
+            # Handle convolutional LoRA (simplified)
+            lora_weight = torch.zeros_like(fp8_state[key], dtype=torch.float32)
         reconstructed[key] = fp8_state[key].to(torch.float32) + lora_weight
     else:
         reconstructed[key] = fp8_state[key].to(torch.float32)
 ```
 
-> **Note**: Requires PyTorch ≥ 2.1 for FP8 support. For best results, use the same architecture selection ({architecture}) during inference as was used during extraction.
+> Requires PyTorch ≥ 2.1 for FP8 support. Use the same architecture selection during inference.
 """
-        
+
         with open(os.path.join(output_dir, "README.md"), "w") as f:
             f.write(readme)
-        
+
         if target_type == "huggingface":
             HfApi(token=hf_token).upload_file(
                 path_or_fileobj=os.path.join(output_dir, "README.md"),
@@ -476,24 +345,18 @@ for key in fp8_state:
                 repo_type="model",
                 token=hf_token
             )
-        
+
         progress(1.0, desc="✅ Done!")
         result_html = f"""
 ✅ Success!  
 Model uploaded to: <a href="{repo_url_final}" target="_blank">{new_repo_id}</a>  
-Includes: 
-- FP8 model: `{fp8_filename}`
-- LoRA weights: `{lora_filename}` (rank {lora_rank}, architecture: {architecture})
-
-📊 Stats: {stats['layers_processed']}/{stats['layers_eligible']} eligible layers processed
+Includes: FP8 + rank-{lora_rank} LoRA ({architecture}).
 """
         return gr.HTML(result_html), "✅ FP8 + LoRA upload successful!", msg
-    
+
     except Exception as e:
-        error_msg = f"❌ Error: {str(e)}\n{traceback.format_exc()}"
-        print(error_msg)
-        return None, error_msg, ""
-    
+        import traceback
+        return None, f"❌ Error: {str(e)}\n{traceback.format_exc()}", ""
     finally:
         if temp_dir:
             shutil.rmtree(temp_dir, ignore_errors=True)
@@ -501,18 +364,17 @@ Includes:
 
 with gr.Blocks(title="FP8 + LoRA Extractor (HF ↔ ModelScope)") as demo:
     gr.Markdown("# 🔄 Advanced FP8 Pruner with Architecture-Specific LoRA Extraction")
-    gr.Markdown("Convert `.safetensors` → **FP8** + **targeted LoRA** weights for precision recovery. Supports Hugging Face ↔ ModelScope.")
-    
+    gr.Markdown("Convert `.safetensors` → **FP8** + **targeted LoRA** for precision recovery. Supports Hugging Face ↔ ModelScope.")
+
     with gr.Row():
         with gr.Column():
             source_type = gr.Radio(["huggingface", "modelscope"], value="huggingface", label="Source")
             repo_url = gr.Textbox(label="Repo URL or ID", placeholder="https://huggingface.co/... or modelscope-id")
             safetensors_filename = gr.Textbox(label="Filename", placeholder="model.safetensors")
-            
+
             with gr.Accordion("Advanced LoRA Settings", open=True):
                 fp8_format = gr.Radio(["e4m3fn", "e5m2"], value="e5m2", label="FP8 Format")
                 lora_rank = gr.Slider(minimum=4, maximum=256, step=4, value=64, label="LoRA Rank")
-                
                 architecture = gr.Dropdown(
                     choices=[
                         ("Auto-detect components", "auto"),
@@ -523,25 +385,24 @@ with gr.Blocks(title="FP8 + LoRA Extractor (HF ↔ ModelScope)") as demo:
                         ("All components", "all")
                     ],
                     value="auto",
-                    label="Target Architecture",
-                    info="Select which model components to apply LoRA to"
+                    label="Target Architecture"
                 )
-            
+
             with gr.Accordion("Authentication", open=False):
                 hf_token = gr.Textbox(label="Hugging Face Token", type="password")
                 modelscope_token = gr.Textbox(label="ModelScope Token (optional)", type="password", visible=MODELScope_AVAILABLE)
-        
+
         with gr.Column():
             target_type = gr.Radio(["huggingface", "modelscope"], value="huggingface", label="Target")
             new_repo_id = gr.Textbox(label="New Repo ID", placeholder="user/model-fp8-lora")
             private_repo = gr.Checkbox(label="Private Repository (HF only)", value=False)
-            
+
             status_output = gr.Markdown()
             detailed_log = gr.Textbox(label="Processing Log", interactive=False, lines=10)
-    
+
     convert_btn = gr.Button("🚀 Convert & Upload", variant="primary")
     repo_link_output = gr.HTML()
-    
+
     convert_btn.click(
         fn=process_and_upload_fp8,
         inputs=[
@@ -560,7 +421,7 @@ with gr.Blocks(title="FP8 + LoRA Extractor (HF ↔ ModelScope)") as demo:
         outputs=[repo_link_output, status_output, detailed_log],
         show_progress=True
     )
-    
+
     gr.Examples(
         examples=[
             ["huggingface", "https://huggingface.co/Yabo/FramePainter/tree/main", "unet_diffusion_pytorch_model.safetensors", "e5m2", 64, "unet_transformer"],
@@ -570,17 +431,15 @@ with gr.Blocks(title="FP8 + LoRA Extractor (HF ↔ ModelScope)") as demo:
         inputs=[source_type, repo_url, safetensors_filename, fp8_format, lora_rank, architecture],
         label="Example Conversions"
     )
-    
+
     gr.Markdown("""
     ## 💡 Usage Tips
-    
-    - **For Text Encoders**: Use rank 32-64 with `text_encoder` architecture for optimal results.
-    - **For UNet Attention**: Use `unet_transformer` with rank 64-128 for best quality preservation.
-    - **For UNet Convolutions**: Use `unet_conv` with lower ranks (16-32) as convolutions compress better.
-    - **For VAE**: Use `vae` architecture with rank 16-32.
-    - **Auto Mode**: Let the tool analyze and target appropriate layers automatically.
-    
-    ⚠️ **Note**: Higher ranks produce better quality but larger LoRA files. Start with lower ranks and increase if needed.
+    - **Text Encoder**: Use rank 32–64 with `text_encoder` architecture  
+    - **UNet Attention**: Use `unet_transformer` with rank 64–128  
+    - **UNet Convolutions**: Use `unet_conv` with rank 16–32  
+    - **VAE**: Use `vae` with rank 16–32  
+    - **Auto Mode**: Let the tool analyze and select layers automatically  
+    - Higher ranks = better quality but larger LoRA files
     """)
 
 demo.launch()