Update app.py

app.py

@@ -10,7 +10,8 @@ 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
@@ -19,16 +20,126 @@ except ImportError:
     MODELScope_AVAILABLE = False
 
 def low_rank_decomposition(weight, rank=64):
-    if weight.ndim != 2:
-        return None
-    U, S, Vh = torch.linalg.svd(weight.float(), full_matrices=False)
-    U = U[:, :rank] @ torch.diag(torch.sqrt(S[:rank]))
-    Vh = torch.diag(torch.sqrt(S[:rank])) @ Vh[:rank, :]
-    return U.contiguous(), Vh.contiguous()
-
-def convert_safetensors_to_fp8_with_lora(safetensors_path, output_dir, fp8_format, lora_rank=64, progress=gr.Progress()):
+    """Handle both 2D and 4D tensors for LoRA decomposition."""
+    original_shape = weight.shape
+    original_dtype = weight.dtype
+    
+    try:
+        # Handle 2D tensors (linear layers, attention)
+        if weight.ndim == 2:
+            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)
+        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)
+                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
+            
+    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
+    lower_key = key.lower()
+    
+    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)
+    
+    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)
+    
+    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)
+    
+    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
+
+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...")
-
     try:
         def read_safetensors_metadata(path):
             with open(path, 'rb') as f:
@@ -36,57 +147,146 @@ 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 = {}
+        lora_stats = {
+            'total_layers': len(state_dict),
+            '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}...")
+            progress(0.4 + 0.4 * (i / total), desc=f"Processing {i+1}/{total}: {key.split('.')[-1]}")
             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
-
-                # Attempt LoRA decomposition only for 2D tensors
-                if weight.ndim == 2 and min(weight.shape) > lora_rank:
+                
+                # Determine if we should apply LoRA
+                eligible_for_lora = should_apply_lora(key, weight, architecture, lora_rank)
+                
+                if eligible_for_lora:
+                    lora_stats['layers_eligible'] += 1
+                    
                     try:
-                        U, V = low_rank_decomposition(weight, rank=lora_rank)
+                        # 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)
                             lora_keys.append(key)
-                    except Exception:
-                        pass
+                            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()
+                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}")
             else:
                 sd_fp8[key] = weight
-
+                lora_stats['layers_skipped'].append(f"{key}: unsupported dtype {weight.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}.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})
-        if lora_weights:
-            save_file(lora_weights, lora_path, metadata={"format": "pt", "lora_rank": str(lora_rank)})
-
+        
+        # Always save LoRA file, even if empty
+        lora_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
+        stats_msg = f"""
+📊 LoRA Extraction Statistics:
+- Total layers analyzed: {lora_stats['layers_analyzed']}
+- 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!")
-        return True, f"FP8 ({fp8_format}) and rank-{lora_rank} LoRA saved."
+        
+        if lora_stats['layers_processed'] == 0:
+            stats_msg += "\n\n⚠️ WARNING: No LoRA weights were generated. Try a different architecture selection or lower rank."
+        
+        return True, f"FP8 ({fp8_format}) and rank-{lora_rank} LoRA saved.\n{stats_msg}", lora_stats
 
     except Exception as e:
-        return False, str(e)
+        error_msg = f"Conversion error: {str(e)}\n{traceback.format_exc()}"
+        print(error_msg)
+        return False, error_msg, None
 
 def parse_hf_url(url):
     url = url.strip().rstrip("/")
@@ -129,7 +329,7 @@ def download_safetensors_file(source_type, repo_url, filename, hf_token=None, pr
         shutil.rmtree(temp_dir, ignore_errors=True)
         raise e
 
-def upload_to_target(target_type, new_repo_id, output_dir, fp8_format, hf_token=None, modelscope_token=None, private_repo=False):
+def upload_to_target(target_type, new_repo_id, output_dir, fp8_format, architecture, hf_token=None, modelscope_token=None, private_repo=False):
     if target_type == "huggingface":
         api = HfApi(token=hf_token)
         api.create_repo(repo_id=new_repo_id, private=private_repo, repo_type="model", exist_ok=True)
@@ -150,6 +350,7 @@ def process_and_upload_fp8(
     safetensors_filename,
     fp8_format,
     lora_rank,
+    architecture,
     target_type,
     new_repo_id,
     hf_token,
@@ -159,35 +360,40 @@ def process_and_upload_fp8(
 ):
     if not re.match(r"^[a-zA-Z0-9._-]+/[a-zA-Z0-9._-]+$", new_repo_id):
         return None, "❌ Invalid repo ID format. Use 'username/model-name'.", ""
-
     if source_type == "huggingface" and not hf_token:
         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:
         progress(0.05, desc="Downloading model...")
         safetensors_path, temp_dir = download_safetensors_file(
             source_type, repo_url, safetensors_filename, hf_token, progress
         )
-
-        progress(0.25, desc="Converting to FP8 with LoRA extraction...")
-        success, msg = convert_safetensors_to_fp8_with_lora(
-            safetensors_path, output_dir, fp8_format, lora_rank, 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, hf_token, modelscope_token, private_repo
+            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}.safetensors"
+        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:
@@ -196,45 +402,72 @@ tags:
 - lora
 - low-rank
 - diffusion
-- converted-by-gradio
+- architecture-{architecture}
+- converted-by-ai-toolkit
 ---
-
 # FP8 Model with Low-Rank LoRA
-
 - **Source**: `{repo_url}`
 - **File**: `{safetensors_filename}`
 - **FP8 Format**: `{fp8_format.upper()}`
 - **LoRA Rank**: {lora_rank}
+- **Architecture Target**: {architecture}
 - **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
 import torch
 
 # Load FP8 model
-fp8_state = load_file("{base_name}-fp8-{fp8_format}.safetensors")
+fp8_state = load_file("{fp8_filename}")
 lora_state = load_file("{lora_filename}")
 
 # Reconstruct approximate original weights
 reconstructed = {{}}
 for key in fp8_state:
-    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)
-        lora_weight = B @ A  # (rank, out) @ (in, rank) -> (out, in)
-        fp8_weight = fp8_state[key].to(torch.float32)
-        reconstructed[key] = fp8_weight + lora_weight
+    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 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])
+        
+        reconstructed[key] = fp8_state[key].to(torch.float32) + lora_weight
     else:
         reconstructed[key] = fp8_state[key].to(torch.float32)
 ```
 
-> Requires PyTorch ≥ 2.1 for FP8 support.
+> **Note**: Requires PyTorch ≥ 2.1 for FP8 support. For best results, use the same architecture selection ({architecture}) during inference as was used during extraction.
 """
+        
         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"),
@@ -243,44 +476,72 @@ 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 + rank-{lora_rank} LoRA.
-"""
-        return gr.HTML(result_html), "✅ FP8 + LoRA upload successful!", ""
+Includes: 
+- FP8 model: `{fp8_filename}`
+- LoRA weights: `{lora_filename}` (rank {lora_rank}, architecture: {architecture})
 
+📊 Stats: {stats['layers_processed']}/{stats['layers_eligible']} eligible layers processed
+"""
+        return gr.HTML(result_html), "✅ FP8 + LoRA upload successful!", msg
+    
     except Exception as e:
-        return None, f"�� Error: {str(e)}", ""
+        error_msg = f"❌ Error: {str(e)}\n{traceback.format_exc()}"
+        print(error_msg)
+        return None, error_msg, ""
+    
     finally:
         if temp_dir:
             shutil.rmtree(temp_dir, ignore_errors=True)
         shutil.rmtree(output_dir, ignore_errors=True)
 
 with gr.Blocks(title="FP8 + LoRA Extractor (HF ↔ ModelScope)") as demo:
-    gr.Markdown("# 🔄 FP8 Pruner with Low-Rank LoRA Extraction")
-    gr.Markdown("Convert `.safetensors` → **FP8** + **compact LoRA** for precision recovery. Supports Hugging Face ↔ ModelScope.")
-
+    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.")
+    
     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")
-            fp8_format = gr.Radio(["e4m3fn", "e5m2"], value="e5m2", label="FP8 Format")
-            lora_rank = gr.Slider(minimum=8, maximum=256, step=8, value=64, label="LoRA Rank")
-            hf_token = gr.Textbox(label="HF Token (only if using HF)", type="password")
-            modelscope_token = gr.Textbox(label="ModelScope Token (optional)", type="password", visible=MODELScope_AVAILABLE)
+            
+            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"),
+                        ("Text Encoder (embeddings, attention)", "text_encoder"),
+                        ("UNet Transformers (attention blocks)", "unet_transformer"),
+                        ("UNet Convolutions (resnets, downsampling)", "unet_conv"),
+                        ("VAE (encoder/decoder)", "vae"),
+                        ("All components", "all")
+                    ],
+                    value="auto",
+                    label="Target Architecture",
+                    info="Select which model components to apply LoRA to"
+                )
+            
+            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")
-            private_repo = gr.Checkbox(label="Private (HF only)", value=False)
-
+            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")
-    status_output = gr.Markdown()
     repo_link_output = gr.HTML()
-
+    
     convert_btn.click(
         fn=process_and_upload_fp8,
         inputs=[
@@ -289,21 +550,37 @@ with gr.Blocks(title="FP8 + LoRA Extractor (HF ↔ ModelScope)") as demo:
             safetensors_filename,
             fp8_format,
             lora_rank,
+            architecture,
             target_type,
             new_repo_id,
             hf_token,
             modelscope_token,
             private_repo
         ],
-        outputs=[repo_link_output, status_output],
+        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, "modelscope"]
+            ["huggingface", "https://huggingface.co/Yabo/FramePainter/tree/main", "unet_diffusion_pytorch_model.safetensors", "e5m2", 64, "unet_transformer"],
+            ["huggingface", "https://huggingface.co/stabilityai/sdxl-vae", "diffusion_pytorch_model.safetensors", "e4m3fn", 32, "vae"],
+            ["huggingface", "https://huggingface.co/runwayml/stable-diffusion-v1-5/tree/main/text_encoder", "model.safetensors", "e5m2", 48, "text_encoder"]
         ],
-        inputs=[source_type, repo_url, safetensors_filename, fp8_format, lora_rank, target_type]
+        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.
+    """)
 
 demo.launch()