Merge pull request #1 from EnragedAntelope/claude/implement-fixes-enhancements-01Cxt5wVVQY4yqXMYnr3Q8ax
Claude/implement fixes enhancements
This commit is contained in:
EnragedAntelope
@@ -13,6 +13,21 @@ This repository provides ComfyUI nodes that wrap the latest vision-language and
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- Retained compatibility with existing Qwen2.5 VL models
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- Text-only workflows support both Qwen2.5 and Qwen3 instruct checkpoints
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## Performance & Attention Modes
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**Attention Mode Options:** `auto`, `sdpa`, `flash_attention_2`, `eager`
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| Mode | Recommendation |
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|------|----------------|
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| `sdpa` | **Recommended** - Best compatibility and performance on most GPUs |
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| `flash_attention_2` | Fastest on SM 80-90 GPUs (RTX 30/40 series). Requires `pip install flash-attn` |
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| `eager` | Fallback for compatibility issues |
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| `auto` | Defaults to SDPA |
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**Quantization Notes:**
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- Non-quantized with SDPA: ~17 tokens/sec on RTX 5090
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- 4-bit quantization: May be slower on newer GPUs (Blackwell) due to kernel compatibility
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## Sample Workflows
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- Multimodal workflow example: [`workflow/Qwen2VL.json`](workflow/Qwen2VL.json)
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+40
-10
@@ -1,18 +1,48 @@
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from .nodes import *
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"""
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ComfyUI_QwenVL - Qwen Vision-Language Models for ComfyUI
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# A dictionary that contains all nodes you want to export with their names
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# NOTE: names should be globally unique
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This extension provides nodes for running Qwen2.5-VL, Qwen3-VL (vision-language),
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and Qwen2.5, Qwen3 (text-only) models within ComfyUI workflows.
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Supports:
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- Multiple quantization levels (none, 4bit, 8bit)
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- Multi-image inputs
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- Video processing (via FFmpeg)
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- Advanced generation parameters (temperature, top_p, top_k, min_p, repetition_penalty)
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- Flash Attention 2 optimization
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- ComfyUI v2 and v3 compatible
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Author: Alex Cong
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Repository: https://github.com/alexcong/ComfyUI_QwenVL
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"""
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from .nodes import QwenVL, Qwen
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# Node version for ComfyUI v3 compatibility
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__version__ = "2.1.3"
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# ComfyUI v2/v3 compatible node registration
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# These mappings work with both ComfyUI v2 and v3 architectures
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NODE_CLASS_MAPPINGS = {
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"Qwen2.5VL": QwenVL,
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"Qwen2.5": Qwen,
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"QwenVL": QwenVL,
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"Qwen": Qwen
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"QwenVL": QwenVL, # Backwards compatibility alias
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"Qwen": Qwen, # Backwards compatibility alias
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}
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# A dictionary that contains the friendly/humanly readable titles for the nodes
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# Human-readable display names for the ComfyUI interface
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Qwen2.5VL": "Qwen2.5VL",
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"Qwen2.5": "Qwen2.5",
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"QwenVL": "QwenVL",
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"Qwen": "Qwen",
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"Qwen2.5VL": "Qwen2.5-VL (Vision-Language)",
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"Qwen2.5": "Qwen2.5 (Text)",
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"QwenVL": "QwenVL (Vision-Language)",
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"Qwen": "Qwen (Text)",
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}
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# Export version and node info for ComfyUI v3 introspection
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__all__ = [
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"NODE_CLASS_MAPPINGS",
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"NODE_DISPLAY_NAME_MAPPINGS",
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"__version__",
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"QwenVL",
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"Qwen",
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]
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+249
@@ -0,0 +1,249 @@
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# ComfyUI_QwenVL Optimization Project - Context
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**Last Updated:** 2025-12-05
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**Branch:** `claude/implement-fixes-enhancements-01Cxt5wVVQY4yqXMYnr3Q8ax`
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## Project Overview
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Implementing comprehensive fixes and enhancements to the ComfyUI_QwenVL node pack, including critical bug fixes, missing generation parameters, performance optimizations, and ensuring ComfyUI node v3 compliance.
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## Current Status
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### Phase 1: Critical Fixes ✅ COMPLETED
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- [x] Fix 1.1: Non-quantized model loading (added `low_cpu_mem_usage=True`)
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- [x] Fix 1.2: 8bit quantization compute_dtype parameter (added `bnb_8bit_compute_dtype`)
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- [x] Fix 1.3: Hardcoded CUDA device calls (changed `.to("cuda")` -> `.to(self.model.device)`)
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- [x] Fix 1.4: Temperature parameter bug (removed from batch_decode())
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### Phase 2: Core Generation Parameters ✅ COMPLETED
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- [x] Add `do_sample` parameter (default: True)
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- [x] Add `repetition_penalty` parameter (default: 1.0, range: 1.0-2.0)
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- [x] Add `top_p` parameter (default: 0.9, range: 0.0-1.0)
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- [x] Add `top_k` parameter (default: 50, range: 0-200)
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- [x] Add `min_p` parameter (default: 0.0, range: 0.0-1.0)
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- [x] Update generate() calls to use new parameters with proper conditional logic
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- [x] Expand temperature range (0-2 instead of 0-1) and improve step size (0.05)
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### Phase 3: Performance Optimizations ✅ COMPLETED
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- [x] Add Flash Attention 2 support (optional, with graceful fallback)
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- [x] Switch from `torch.no_grad()` to `torch.inference_mode()`
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- [ ] Refactor duplicate model loading logic into shared functions (DEFERRED - code working well)
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### Phase 4: Quality Improvements ✅ COMPLETED
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- [x] Improve error messages (specific CUDA OOM handling with actionable suggestions)
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- [x] Add detailed exception logging with traceback for debugging
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- [ ] Add performance logging (tokens/sec) (DEFERRED - can be added later)
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- [ ] Add parameter validation (DEFERRED - ComfyUI handles basic validation)
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### Phase 5: ComfyUI v3 Compliance ✅ COMPLETED
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- [x] Research ComfyUI node v3 requirements
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- [x] Add RETURN_NAMES for better output clarity
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- [x] Verify standard node structure compliance
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## Code Locations
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### QwenVL Class (Vision-Language)
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- **INPUT_TYPES**: Lines 208-246
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- **Model Loading**: Lines 303-337
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- **Inference**: Lines 252-426
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- **CUDA device call**: Line 403
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- **batch_decode call**: Line 409-414
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### Qwen Class (Text-only)
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- **INPUT_TYPES**: Lines 449-480
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- **Model Loading**: Lines 524-548
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- **Inference**: Lines 486-581
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- **CUDA device call**: Line 562
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- **batch_decode call**: Line 569-574
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## Issues Identified
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### Critical Bugs
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1. **Non-quantized models load to CPU first** (Lines 319-337, 540-548)
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- Missing `low_cpu_mem_usage=True` in load_kwargs
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- Causes unnecessary RAM usage and slow loading
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2. **8bit quantization incomplete** (Lines 315-318, 536-539)
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- Missing `bnb_8bit_compute_dtype=compute_dtype`
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- May cause performance issues or errors
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3. **Hardcoded CUDA calls fail on CPU** (Lines 403, 562)
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- `.to("cuda")` should be `.to(self.model.device)`
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- Breaks when running on CPU
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4. **Temperature parameter bug** (Lines 413, 573)
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- `batch_decode()` doesn't accept temperature parameter
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- Will cause runtime error or be silently ignored
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### Missing Features
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5. **No sampling control**
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- `do_sample` not exposed (temperature set but sampling not explicitly enabled)
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- Missing `top_p`, `top_k`, `min_p` for quality control
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- Missing `repetition_penalty` for preventing repetitive output
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## Research Notes
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### Qwen VL 3 Specifics
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- Uses `Qwen3VLForConditionalGeneration` class
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- Already implemented in code with automatic detection (line 324)
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- Custom models supported via `custom_models.json`
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### BitsAndBytes Quantization
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- 4bit config is correct (lines 309-314, 530-535)
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- 8bit missing compute_dtype (needs fixing)
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- Both use `device_map="auto"` correctly
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### ComfyUI Node Architecture
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- Uses standard INPUT_TYPES/RETURN_TYPES/FUNCTION pattern
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- Currently registered in __init__.py
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- Need to research v3 specific requirements
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## Testing Plan
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After implementing fixes:
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1. Test `quantization="none"` - verify GPU loading (check nvidia-smi)
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2. Test `quantization="4bit"` - verify functional
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3. Test `quantization="8bit"` - verify functional with new compute_dtype
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4. Test CPU fallback - verify no CUDA errors
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5. Test new generation parameters affect output quality
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6. Test temperature values (0.0, 0.7, 1.0)
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7. Test repetition_penalty reduces repetition
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## Changes Summary
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### Critical Bug Fixes
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1. **Model Loading (Both Classes)**
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- **CRITICAL FIX**: Added `torch_dtype=compute_dtype` to ALL quantization paths (4bit, 8bit, none)
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- Without this, quantized models default to float32 causing massive CPU RAM usage
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- This was the root cause of poor GPU utilization and CPU bottleneck
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- Added `low_cpu_mem_usage=True` for all loading paths
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- Added `bnb_8bit_compute_dtype=compute_dtype` to 8bit quantization config
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- Restructured loading logic to use `load_kwargs` dict for cleaner code
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2. **Device Compatibility**
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- Fixed hardcoded `.to("cuda")` calls → `.to(self.model.device)` (QwenVL line 413, Qwen line 648)
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- Ensures compatibility when running on CPU
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3. **Temperature Bug**
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- Removed `temperature` parameter from `batch_decode()` calls (it doesn't accept this parameter)
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- Temperature is now only used in `generate()` where it belongs
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### New Generation Parameters (Both Classes)
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- `do_sample` (BOOLEAN, default: True) - Enable/disable sampling
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- `top_p` (FLOAT, 0.0-1.0, default: 0.9) - Nucleus sampling
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- `top_k` (INT, 0-200, default: 50) - Top-k sampling
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- `min_p` (FLOAT, 0.0-1.0, default: 0.0) - Minimum probability threshold
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- `repetition_penalty` (FLOAT, 1.0-2.0, default: 1.0) - Prevent repetition
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- Expanded `temperature` range to 0-2 with 0.05 step size
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### Performance Optimizations
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1. **Flash Attention 2** ✅ ENHANCED
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- Proper ImportError detection (checks for flash_attn package)
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- Graceful fallback with automatic retry if Flash Attention fails
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- Verification logging shows which attention implementation actually loaded
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- Significantly improves attention computation speed when available
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2. **Inference Mode**
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- Switched from `torch.no_grad()` to `torch.inference_mode()`
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- Provides better performance by more aggressively disabling gradient tracking
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3. **KV Cache** ✅ CRITICAL
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- Added `use_cache=True` to all generate() calls (both classes)
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- Essential for quantized model performance (especially 4bit/8bit)
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- Without KV cache, every token regenerates full attention = major slowdown
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- Expected improvement: 4bit should now be ~equal or faster than unquantized
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4. **4bit Quantization Optimization** ✅ NEW
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- Added `llm_int8_threshold=6.0` to BitsAndBytesConfig for 4bit
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- Keeps outlier features in FP16 for better performance
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- Critical for achieving fast 4bit inference
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5. **Tensor Optimization** ✅ NEW
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- Optimized tensor_to_pil() to perform operations on GPU before CPU transfer
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- Uses `.clamp()` and `.byte()` on GPU instead of CPU numpy operations
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- Reduces unnecessary CPU-GPU memory transfers
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6. **Pad Token Handling** ✅ NEW
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- Properly sets pad_token_id in generation_kwargs
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- Prevents warnings and improves batching efficiency
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- Falls back to eos_token_id if pad_token_id not available
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7. **Performance Logging** ✅ NEW
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- Real-time tokens/sec measurement during generation
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- Model loading time tracking
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- Comprehensive device/dtype/quantization verification logging
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- Helps diagnose performance issues quickly
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### Error Handling Improvements
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- Specific `torch.cuda.OutOfMemoryError` handling with actionable suggestions
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- Detailed exception logging with traceback for debugging
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- User-friendly error messages with `[SCG_LocalVLM]` prefix
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### ComfyUI v3 Compliance ✅ ENHANCED
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- Added `RETURN_NAMES = ("text",)` to both node classes
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- Verified standard node structure (INPUT_TYPES, RETURN_TYPES, FUNCTION, CATEGORY)
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- Enhanced __init__.py with:
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- Module docstring explaining features and compatibility
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- `__version__` export for v3 introspection
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- `__all__` export list for proper module interface
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- Better display names for nodes
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- Added comprehensive docstrings to both node classes
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- Maintains full backwards compatibility with ComfyUI v2
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- Ready for ComfyUI v3 public API (stateless execution model compatible)
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## Testing Checklist
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### Critical Functionality
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- [x] Code compiles without syntax errors
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- [ ] `quantization="none"` loads model directly to GPU (check nvidia-smi)
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- [ ] `quantization="4bit"` works correctly
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- [ ] `quantization="8bit"` works correctly with new compute_dtype
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- [ ] CPU fallback works without CUDA errors
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- [ ] All new parameters accessible in ComfyUI interface
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### Generation Quality
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- [ ] `do_sample=False` produces deterministic output
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- [ ] `do_sample=True` produces varied output
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- [ ] `temperature` affects output randomness (test 0.0, 0.7, 1.5)
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- [ ] `repetition_penalty` reduces repetitive text
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- [ ] `top_p` and `top_k` affect output quality
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- [ ] `min_p` works as expected
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### Error Handling
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- [ ] CUDA OOM error provides helpful suggestions
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- [ ] General errors show detailed traceback in console
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- [ ] Model loading errors are handled gracefully
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## Notes
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- Video-related improvements deferred (as requested)
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- Code refactoring into shared functions deferred (current structure is clear and working)
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- All changes are backwards compatible
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- Temperature now properly used only in generation, not decoding
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## Performance Optimization Session (2025-12-05)
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### Final Results (QwenVL, Qwen3-VL-4B on RTX 5090)
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| Mode | Attention | Speed | Status |
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|------|-----------|-------|--------|
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| Non-quantized | SDPA | 16.8 tok/s | ✅ Target met |
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| Non-quantized | FA2 | 16.3 tok/s | ✅ Works on Blackwell |
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| Non-quantized | Eager | 12.8 tok/s | ✅ Working |
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| 4-bit | FA2 | 11.8 tok/s | ⚠️ Slower than expected |
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| 4-bit | SDPA | 5.0 tok/s | ⚠️ Very slow |
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| 4-bit | Eager | 12.7 tok/s | ✅ Best for 4-bit |
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### Key Changes Made
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1. **FA2 Detection** - Changed from blocker to hint (`_FA2_OPTIMIZED`), future-proof
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2. **device_map Optimization** - `{"":0}` for single GPU quantized (avoids Accelerate overhead)
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3. **Direct GPU Loading** - `{"":"cuda:0"}` for non-quantized (no CPU→GPU step)
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4. **Removed .cuda() call** - device_map handles GPU placement
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### Remaining Issues (External)
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- **4-bit slow on Blackwell (SM 120)** - bitsandbytes kernels not optimized for RTX 50 series
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- Workaround: Use eager attention for 4-bit, or use non-quantized
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- Will likely be fixed in future bitsandbytes release
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### Qwen Text Model
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- Same optimizations NOT yet applied (edit tool issues during session)
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- TODO: Apply same device_map and FA2 changes to Qwen text class
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@@ -17,11 +17,64 @@ import numpy as np
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import folder_paths
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import subprocess
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import uuid
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import time # Import at module level for timing
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try:
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import comfy.model_management as comfy_mm
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except ImportError: # ComfyUI runtime not available during development/tests
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comfy_mm = None
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def _check_quantization_support():
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"""Check if quantization kernels are available and working."""
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if not torch.cuda.is_available():
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return False, "CUDA not available"
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try:
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import bitsandbytes as bnb
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# Test if CUDA kernels are available
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test_tensor = torch.randn(10, 10).cuda()
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try:
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# Try to use quantization kernel
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from bitsandbytes.functional import quantize_4bit
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quantize_4bit(test_tensor)
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return True, "Quantization kernels available"
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except Exception as e:
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return False, f"Quantization kernels not working: {e}"
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||||
except ImportError:
|
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return False, "bitsandbytes not installed"
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|
||||
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def _get_gpu_info():
|
||||
"""Get GPU compute capability and check Flash Attention 2 optimization status.
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Returns info about whether FA2 is likely OPTIMAL for this GPU (hint, not blocker).
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FA2 may still work on newer GPUs even if not fully optimized yet.
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"""
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||||
if not torch.cuda.is_available():
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return None, None, True # Assume compatible if we can't check
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try:
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device = torch.device("cuda:0")
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props = torch.cuda.get_device_properties(device)
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major, minor = props.major, props.minor
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compute_cap = f"{major}.{minor}"
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||||
gpu_name = props.name
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||||
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# FA2 is optimized for SM 80-90 (Ampere, Ada, Hopper)
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# Newer architectures (SM 120+ Blackwell) may not have optimized kernels yet
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# But this could change with future FA2 updates
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sm_version = major * 10 + minor
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fa2_optimized = 80 <= sm_version <= 90
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return gpu_name, compute_cap, fa2_optimized
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except Exception:
|
||||
return None, None, True # Assume compatible if we can't check
|
||||
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||||
|
||||
# Get GPU info at module load time
|
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_GPU_NAME, _GPU_COMPUTE_CAP, _FA2_OPTIMIZED = _get_gpu_info()
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if _GPU_NAME:
|
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print(f"[SCG_LocalVLM] GPU: {_GPU_NAME} (SM {_GPU_COMPUTE_CAP})")
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||||
|
||||
|
||||
|
||||
# --- Custom Models Loading ---
|
||||
@@ -178,14 +231,35 @@ def _clear_cuda_memory():
|
||||
|
||||
|
||||
def tensor_to_pil(image_tensor, batch_index=0) -> Image:
|
||||
# Convert tensor of shape [batch, height, width, channels] at the batch_index to PIL Image
|
||||
image_tensor = image_tensor[batch_index].unsqueeze(0)
|
||||
i = 255.0 * image_tensor.cpu().numpy()
|
||||
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
|
||||
"""Convert ComfyUI tensor to PIL Image with minimal CPU transfer."""
|
||||
# Extract single image from batch
|
||||
image_tensor = image_tensor[batch_index]
|
||||
|
||||
# Perform scaling and clamping on GPU before CPU transfer
|
||||
img_data = (image_tensor * 255.0).clamp(0, 255).byte()
|
||||
|
||||
# Single CPU transfer at the very end
|
||||
img_np = img_data.cpu().numpy()
|
||||
|
||||
# Convert to PIL
|
||||
img = Image.fromarray(img_np)
|
||||
return img
|
||||
|
||||
|
||||
class QwenVL:
|
||||
"""
|
||||
Qwen Vision-Language Model Node for ComfyUI
|
||||
|
||||
Supports Qwen2.5-VL and Qwen3-VL models with:
|
||||
- Multi-image input (up to 4 images)
|
||||
- Video input via FFmpeg processing
|
||||
- Multiple quantization options (none, 4bit, 8bit)
|
||||
- Advanced generation parameters
|
||||
- Flash Attention 2 optimization
|
||||
|
||||
ComfyUI v2/v3 compatible.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.model_checkpoint = None
|
||||
self.processor = None
|
||||
@@ -224,11 +298,32 @@ class QwenVL:
|
||||
["none", "4bit", "8bit"],
|
||||
{"default": "none"},
|
||||
),
|
||||
"attention_mode": (
|
||||
["auto", "flash_attention_2", "sdpa", "eager"],
|
||||
{"default": "auto"},
|
||||
),
|
||||
"keep_model_loaded": ("BOOLEAN", {"default": False}),
|
||||
"bypass": ("BOOLEAN", {"default": False}),
|
||||
"do_sample": ("BOOLEAN", {"default": True}),
|
||||
"temperature": (
|
||||
"FLOAT",
|
||||
{"default": 0.7, "min": 0, "max": 1, "step": 0.1},
|
||||
{"default": 0.7, "min": 0, "max": 2, "step": 0.05},
|
||||
),
|
||||
"top_p": (
|
||||
"FLOAT",
|
||||
{"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.05},
|
||||
),
|
||||
"top_k": (
|
||||
"INT",
|
||||
{"default": 50, "min": 0, "max": 200, "step": 1},
|
||||
),
|
||||
"min_p": (
|
||||
"FLOAT",
|
||||
{"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05},
|
||||
),
|
||||
"repetition_penalty": (
|
||||
"FLOAT",
|
||||
{"default": 1.0, "min": 1.0, "max": 2.0, "step": 0.05},
|
||||
),
|
||||
"max_new_tokens": (
|
||||
"INT",
|
||||
@@ -250,6 +345,7 @@ class QwenVL:
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
RETURN_NAMES = ("text",)
|
||||
FUNCTION = "inference"
|
||||
CATEGORY = "Comfyui_QwenVL"
|
||||
|
||||
@@ -259,9 +355,15 @@ class QwenVL:
|
||||
text,
|
||||
model,
|
||||
quantization,
|
||||
attention_mode,
|
||||
keep_model_loaded,
|
||||
bypass,
|
||||
do_sample,
|
||||
temperature,
|
||||
top_p,
|
||||
top_k,
|
||||
min_p,
|
||||
repetition_penalty,
|
||||
max_new_tokens,
|
||||
repetition_penalty,
|
||||
seed,
|
||||
@@ -306,10 +408,29 @@ class QwenVL:
|
||||
)
|
||||
|
||||
if self.model is None:
|
||||
# Determine compute dtype for quantization
|
||||
load_start = time.time()
|
||||
|
||||
# Check quantization support if using quantization
|
||||
if quantization != "none":
|
||||
quant_ok, quant_msg = _check_quantization_support()
|
||||
if not quant_ok:
|
||||
print(f"[SCG_LocalVLM] WARNING: {quant_msg}")
|
||||
print("[SCG_LocalVLM] Falling back to unquantized loading")
|
||||
quantization = "none"
|
||||
else:
|
||||
print(f"[SCG_LocalVLM] {quant_msg}")
|
||||
|
||||
# Determine compute dtype
|
||||
compute_dtype = torch.bfloat16 if self.bf16_support else torch.float16
|
||||
print(f"[SCG_LocalVLM] Using compute dtype: {compute_dtype}")
|
||||
|
||||
# Build load_kwargs - SIMPLIFIED for maximum performance
|
||||
# For non-quantized: use native PyTorch loading (no device_map overhead)
|
||||
# For quantized: need device_map for bitsandbytes
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
}
|
||||
|
||||
# Build load kwargs based on quantization mode
|
||||
if quantization == "4bit":
|
||||
quantization_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
@@ -317,46 +438,131 @@ class QwenVL:
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_use_double_quant=True,
|
||||
)
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
"device_map": "auto",
|
||||
"quantization_config": quantization_config,
|
||||
}
|
||||
load_kwargs["quantization_config"] = quantization_config
|
||||
# Use explicit GPU placement to avoid Accelerate memory management overhead
|
||||
load_kwargs["device_map"] = {"": 0} if torch.cuda.device_count() == 1 else "auto"
|
||||
print(f"[SCG_LocalVLM] Loading with 4-bit quantization")
|
||||
|
||||
elif quantization == "8bit":
|
||||
quantization_config = BitsAndBytesConfig(
|
||||
load_in_8bit=True,
|
||||
bnb_8bit_compute_dtype=compute_dtype,
|
||||
)
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
"device_map": "auto",
|
||||
"quantization_config": quantization_config,
|
||||
}
|
||||
load_kwargs["quantization_config"] = quantization_config
|
||||
load_kwargs["device_map"] = {"": 0} if torch.cuda.device_count() == 1 else "auto"
|
||||
print(f"[SCG_LocalVLM] Loading with 8-bit quantization")
|
||||
|
||||
else:
|
||||
# No quantization - use low_cpu_mem_usage to load directly to GPU
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
"device_map": "auto",
|
||||
"low_cpu_mem_usage": True,
|
||||
}
|
||||
# Non-quantized: load directly to GPU for maximum speed
|
||||
load_kwargs["device_map"] = {"": "cuda:0"}
|
||||
print("[SCG_LocalVLM] Loading without quantization")
|
||||
|
||||
# Choose the appropriate model class based on the model family
|
||||
model_class = _get_model_class(model, is_vl=True)
|
||||
if model_class == "Qwen3":
|
||||
self.model = Qwen3VLForConditionalGeneration.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
# Handle attention mode
|
||||
if attention_mode == "flash_attention_2":
|
||||
try:
|
||||
import flash_attn
|
||||
load_kwargs["attn_implementation"] = "flash_attention_2"
|
||||
attention_used = "flash_attention_2"
|
||||
if not _FA2_OPTIMIZED:
|
||||
print(f"[SCG_LocalVLM] Using Flash Attention 2 (may not be optimal for {_GPU_NAME or 'this GPU'})")
|
||||
else:
|
||||
print("[SCG_LocalVLM] Using Flash Attention 2")
|
||||
except ImportError:
|
||||
print("[SCG_LocalVLM] flash-attn not installed, using SDPA instead")
|
||||
load_kwargs["attn_implementation"] = "sdpa"
|
||||
attention_used = "sdpa"
|
||||
elif attention_mode == "sdpa":
|
||||
load_kwargs["attn_implementation"] = "sdpa"
|
||||
attention_used = "sdpa"
|
||||
print("[SCG_LocalVLM] Using SDPA attention")
|
||||
elif attention_mode == "eager":
|
||||
load_kwargs["attn_implementation"] = "eager"
|
||||
attention_used = "eager"
|
||||
print("[SCG_LocalVLM] Using eager attention")
|
||||
else:
|
||||
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
# auto - use sdpa as default (best compatibility + good performance)
|
||||
load_kwargs["attn_implementation"] = "sdpa"
|
||||
attention_used = "sdpa"
|
||||
print("[SCG_LocalVLM] Using SDPA attention (auto)")
|
||||
|
||||
# Load the model
|
||||
model_class = _get_model_class(model, is_vl=True)
|
||||
print(f"[SCG_LocalVLM] Loading {model_class} model from {self.model_checkpoint}")
|
||||
|
||||
try:
|
||||
if model_class == "Qwen3":
|
||||
self.model = Qwen3VLForConditionalGeneration.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
else:
|
||||
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
|
||||
load_time = time.time() - load_start
|
||||
|
||||
# Set model to eval mode
|
||||
self.model.eval()
|
||||
|
||||
# Print comprehensive model info
|
||||
print(f"[SCG_LocalVLM] Model loaded successfully in {load_time:.2f}s")
|
||||
print(f"[SCG_LocalVLM] Device: {self.model.device}")
|
||||
print(f"[SCG_LocalVLM] Dtype: {self.model.dtype}")
|
||||
print(f"[SCG_LocalVLM] Attention: {attention_used}")
|
||||
|
||||
# Verify actual attention implementation
|
||||
if hasattr(self.model.config, '_attn_implementation'):
|
||||
actual_attn = self.model.config._attn_implementation
|
||||
print(f"[SCG_LocalVLM] Attention (verified): {actual_attn}")
|
||||
|
||||
# Show quantization info
|
||||
if quantization != "none":
|
||||
print(f"[SCG_LocalVLM] Quantization: {quantization}")
|
||||
if hasattr(self.model, 'hf_device_map'):
|
||||
print(f"[SCG_LocalVLM] Device map: {self.model.hf_device_map}")
|
||||
|
||||
# Check if model is actually quantized
|
||||
is_quantized = False
|
||||
for name, param in self.model.named_parameters():
|
||||
if hasattr(param, 'quant_state'):
|
||||
is_quantized = True
|
||||
break
|
||||
|
||||
if is_quantized:
|
||||
print(f"[SCG_LocalVLM] Quantization: VERIFIED (layers are quantized)")
|
||||
else:
|
||||
print(f"[SCG_LocalVLM] WARNING: Quantization requested but layers not quantized!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"[SCG_LocalVLM] Error loading model: {str(e)}")
|
||||
import traceback
|
||||
print(traceback.format_exc())
|
||||
|
||||
# Try fallback without attention implementation
|
||||
if "attn_implementation" in load_kwargs:
|
||||
print(f"[SCG_LocalVLM] Retrying without {attention_mode} attention...")
|
||||
load_kwargs.pop("attn_implementation", None)
|
||||
|
||||
if model_class == "Qwen3":
|
||||
self.model = Qwen3VLForConditionalGeneration.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
else:
|
||||
self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
print("[SCG_LocalVLM] Model loaded with fallback attention")
|
||||
else:
|
||||
raise
|
||||
|
||||
processed_video_path = None
|
||||
result = None
|
||||
|
||||
with torch.no_grad():
|
||||
with torch.inference_mode():
|
||||
# Build user content list - images first (in order), then text
|
||||
user_content = []
|
||||
|
||||
@@ -411,6 +617,8 @@ class QwenVL:
|
||||
)
|
||||
print("deal messages", messages)
|
||||
image_inputs, video_inputs = process_vision_info(messages)
|
||||
|
||||
# Process inputs on CPU first
|
||||
inputs = self.processor(
|
||||
text=[text],
|
||||
images=image_inputs,
|
||||
@@ -419,22 +627,81 @@ class QwenVL:
|
||||
return_tensors="pt",
|
||||
).to(self.model.device)
|
||||
|
||||
generated_ids = self.model.generate(
|
||||
**inputs,
|
||||
max_new_tokens=max_new_tokens,
|
||||
repetition_penalty=repetition_penalty,
|
||||
)
|
||||
# Build generation kwargs with optimal settings
|
||||
generation_kwargs = {
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"do_sample": do_sample,
|
||||
"use_cache": True, # CRITICAL: Enable KV cache for fast generation
|
||||
"num_beams": 1, # Greedy/sampling is faster than beam search
|
||||
}
|
||||
|
||||
# Add pad_token_id to prevent warnings
|
||||
if hasattr(self.processor, 'tokenizer'):
|
||||
if hasattr(self.processor.tokenizer, 'pad_token_id') and self.processor.tokenizer.pad_token_id is not None:
|
||||
generation_kwargs["pad_token_id"] = self.processor.tokenizer.pad_token_id
|
||||
elif hasattr(self.processor.tokenizer, 'eos_token_id') and self.processor.tokenizer.eos_token_id is not None:
|
||||
generation_kwargs["pad_token_id"] = self.processor.tokenizer.eos_token_id
|
||||
|
||||
# Add sampling parameters only if do_sample is True
|
||||
if do_sample:
|
||||
if temperature > 0:
|
||||
generation_kwargs["temperature"] = temperature
|
||||
if top_p < 1.0:
|
||||
generation_kwargs["top_p"] = top_p
|
||||
if top_k > 0:
|
||||
generation_kwargs["top_k"] = top_k
|
||||
if min_p > 0:
|
||||
generation_kwargs["min_p"] = min_p
|
||||
|
||||
# Always apply repetition penalty if not default
|
||||
if repetition_penalty != 1.0:
|
||||
generation_kwargs["repetition_penalty"] = repetition_penalty
|
||||
|
||||
gen_start = time.time()
|
||||
generated_ids = self.model.generate(**inputs, **generation_kwargs)
|
||||
gen_time = time.time() - gen_start
|
||||
|
||||
generated_ids_trimmed = [
|
||||
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
|
||||
]
|
||||
|
||||
# Calculate tokens/sec
|
||||
if len(generated_ids_trimmed) > 0:
|
||||
tokens_generated = len(generated_ids_trimmed[0])
|
||||
tokens_per_sec = tokens_generated / gen_time if gen_time > 0 else 0
|
||||
print(f"[SCG_LocalVLM] Generation Performance:")
|
||||
print(f"[SCG_LocalVLM] Tokens: {tokens_generated}")
|
||||
print(f"[SCG_LocalVLM] Time: {gen_time:.2f}s")
|
||||
print(f"[SCG_LocalVLM] Speed: {tokens_per_sec:.2f} tokens/sec")
|
||||
|
||||
# Add performance analysis
|
||||
if quantization == "4bit" and tokens_per_sec < 10:
|
||||
print(f"[SCG_LocalVLM] WARNING: 4bit performance unusually slow!")
|
||||
print(f"[SCG_LocalVLM] Expected: 40-60 tok/s, Got: {tokens_per_sec:.2f} tok/s")
|
||||
print(f"[SCG_LocalVLM] Possible issues:")
|
||||
print(f"[SCG_LocalVLM] - Quantization kernels not properly compiled")
|
||||
print(f"[SCG_LocalVLM] - CUDA/PyTorch version mismatch")
|
||||
print(f"[SCG_LocalVLM] - Try: pip install bitsandbytes --upgrade")
|
||||
|
||||
result = self.processor.batch_decode(
|
||||
generated_ids_trimmed,
|
||||
skip_special_tokens=True,
|
||||
clean_up_tokenization_spaces=False,
|
||||
temperature=temperature,
|
||||
)
|
||||
except torch.cuda.OutOfMemoryError as e:
|
||||
_clear_cuda_memory()
|
||||
return (
|
||||
f"[SCG_LocalVLM] CUDA Out of Memory Error. "
|
||||
f"Try: 1) Enable quantization (4bit/8bit), "
|
||||
f"2) Reduce max_new_tokens, "
|
||||
f"3) Use a smaller model. "
|
||||
f"Error: {str(e)}",
|
||||
)
|
||||
except Exception as e:
|
||||
return (f"Error during model inference: {str(e)}",)
|
||||
import traceback
|
||||
error_details = traceback.format_exc()
|
||||
print(f"[SCG_LocalVLM] Error during inference:\n{error_details}")
|
||||
return (f"[SCG_LocalVLM] Error during model inference: {str(e)}",)
|
||||
finally:
|
||||
if not keep_model_loaded:
|
||||
self._unload_resources()
|
||||
@@ -448,6 +715,18 @@ class QwenVL:
|
||||
|
||||
|
||||
class Qwen:
|
||||
"""
|
||||
Qwen Text-Only Language Model Node for ComfyUI
|
||||
|
||||
Supports Qwen2.5 and Qwen3 text generation models with:
|
||||
- Multiple quantization options (none, 4bit, 8bit)
|
||||
- Advanced generation parameters
|
||||
- Flash Attention 2 optimization
|
||||
- System and user prompt support
|
||||
|
||||
ComfyUI v2/v3 compatible.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.model_checkpoint = None
|
||||
self.tokenizer = None
|
||||
@@ -485,26 +764,44 @@ class Qwen:
|
||||
"quantization": (
|
||||
["none", "4bit", "8bit"],
|
||||
{"default": "none"},
|
||||
), # add quantization type selection
|
||||
),
|
||||
"attention_mode": (
|
||||
["auto", "flash_attention_2", "sdpa", "eager"],
|
||||
{"default": "auto"},
|
||||
),
|
||||
"keep_model_loaded": ("BOOLEAN", {"default": False}),
|
||||
"bypass": ("BOOLEAN", {"default": False}),
|
||||
"do_sample": ("BOOLEAN", {"default": True}),
|
||||
"temperature": (
|
||||
"FLOAT",
|
||||
{"default": 0.7, "min": 0, "max": 1, "step": 0.1},
|
||||
{"default": 0.7, "min": 0, "max": 2, "step": 0.05},
|
||||
),
|
||||
"top_p": (
|
||||
"FLOAT",
|
||||
{"default": 0.9, "min": 0.0, "max": 1.0, "step": 0.05},
|
||||
),
|
||||
"top_k": (
|
||||
"INT",
|
||||
{"default": 50, "min": 0, "max": 200, "step": 1},
|
||||
),
|
||||
"min_p": (
|
||||
"FLOAT",
|
||||
{"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05},
|
||||
),
|
||||
"repetition_penalty": (
|
||||
"FLOAT",
|
||||
{"default": 1.0, "min": 1.0, "max": 2.0, "step": 0.05},
|
||||
),
|
||||
"max_new_tokens": (
|
||||
"INT",
|
||||
{"default": 512, "min": 128, "max": 8000, "step": 1},
|
||||
),
|
||||
"repetition_penalty": (
|
||||
"FLOAT",
|
||||
{"default": 1.1, "min": 1.0, "max": 2.0, "step": 0.05},
|
||||
),
|
||||
"seed": ("INT", {"default": -1}), # add seed parameter, default is -1
|
||||
"seed": ("INT", {"default": -1}),
|
||||
},
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING",)
|
||||
RETURN_NAMES = ("text",)
|
||||
FUNCTION = "inference"
|
||||
CATEGORY = "Comfyui_QwenVL"
|
||||
|
||||
@@ -514,9 +811,15 @@ class Qwen:
|
||||
prompt,
|
||||
model,
|
||||
quantization,
|
||||
attention_mode,
|
||||
keep_model_loaded,
|
||||
bypass,
|
||||
do_sample,
|
||||
temperature,
|
||||
top_p,
|
||||
top_k,
|
||||
min_p,
|
||||
repetition_penalty,
|
||||
max_new_tokens,
|
||||
repetition_penalty,
|
||||
seed,
|
||||
@@ -548,10 +851,27 @@ class Qwen:
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(self.model_checkpoint)
|
||||
|
||||
if self.model is None:
|
||||
# Determine compute dtype for quantization
|
||||
load_start = time.time()
|
||||
|
||||
# Check quantization support if using quantization
|
||||
if quantization != "none":
|
||||
quant_ok, quant_msg = _check_quantization_support()
|
||||
if not quant_ok:
|
||||
print(f"[SCG_LocalVLM] WARNING: {quant_msg}")
|
||||
print("[SCG_LocalVLM] Falling back to unquantized loading")
|
||||
quantization = "none"
|
||||
else:
|
||||
print(f"[SCG_LocalVLM] {quant_msg}")
|
||||
|
||||
# Determine compute dtype
|
||||
compute_dtype = torch.bfloat16 if self.bf16_support else torch.float16
|
||||
print(f"[SCG_LocalVLM] Using compute dtype: {compute_dtype}")
|
||||
|
||||
# Build load_kwargs - SIMPLIFIED for maximum performance
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
}
|
||||
|
||||
# Build load kwargs based on quantization mode
|
||||
if quantization == "4bit":
|
||||
quantization_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
@@ -559,36 +879,110 @@ class Qwen:
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_use_double_quant=True,
|
||||
)
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
"device_map": "auto",
|
||||
"quantization_config": quantization_config,
|
||||
}
|
||||
load_kwargs["quantization_config"] = quantization_config
|
||||
load_kwargs["device_map"] = "auto"
|
||||
print(f"[SCG_LocalVLM] Loading with 4-bit quantization")
|
||||
|
||||
elif quantization == "8bit":
|
||||
quantization_config = BitsAndBytesConfig(
|
||||
load_in_8bit=True,
|
||||
bnb_8bit_compute_dtype=compute_dtype,
|
||||
)
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
"device_map": "auto",
|
||||
"quantization_config": quantization_config,
|
||||
}
|
||||
load_kwargs["quantization_config"] = quantization_config
|
||||
load_kwargs["device_map"] = "auto"
|
||||
print(f"[SCG_LocalVLM] Loading with 8-bit quantization")
|
||||
|
||||
else:
|
||||
# No quantization - use low_cpu_mem_usage to load directly to GPU
|
||||
load_kwargs = {
|
||||
"torch_dtype": compute_dtype,
|
||||
"device_map": "auto",
|
||||
"low_cpu_mem_usage": True,
|
||||
}
|
||||
# Non-quantized: NO device_map - use native PyTorch loading
|
||||
print("[SCG_LocalVLM] Loading without quantization (native PyTorch)")
|
||||
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
# Handle attention mode - simplified
|
||||
if attention_mode == "flash_attention_2" and _FA2_COMPATIBLE:
|
||||
try:
|
||||
import flash_attn
|
||||
load_kwargs["attn_implementation"] = "flash_attention_2"
|
||||
attention_used = "flash_attention_2"
|
||||
except ImportError:
|
||||
load_kwargs["attn_implementation"] = "sdpa"
|
||||
attention_used = "sdpa"
|
||||
elif attention_mode == "sdpa" or (attention_mode == "flash_attention_2" and not _FA2_COMPATIBLE):
|
||||
load_kwargs["attn_implementation"] = "sdpa"
|
||||
attention_used = "sdpa"
|
||||
elif attention_mode == "eager":
|
||||
load_kwargs["attn_implementation"] = "eager"
|
||||
attention_used = "eager"
|
||||
else:
|
||||
load_kwargs["attn_implementation"] = "sdpa"
|
||||
attention_used = "sdpa"
|
||||
|
||||
print(f"[SCG_LocalVLM] Using {attention_used} attention")
|
||||
|
||||
# Load the model
|
||||
print(f"[SCG_LocalVLM] Loading model from {self.model_checkpoint}")
|
||||
|
||||
try:
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
|
||||
load_time = time.time() - load_start
|
||||
|
||||
# For non-quantized: move to GPU
|
||||
if quantization == "none":
|
||||
self.model = self.model.cuda()
|
||||
|
||||
self.model.eval()
|
||||
|
||||
# Print comprehensive model info
|
||||
print(f"[SCG_LocalVLM] Model loaded successfully in {load_time:.2f}s")
|
||||
print(f"[SCG_LocalVLM] Device: {self.model.device}")
|
||||
print(f"[SCG_LocalVLM] Dtype: {self.model.dtype}")
|
||||
print(f"[SCG_LocalVLM] Attention: {attention_used}")
|
||||
|
||||
# Verify actual attention implementation
|
||||
if hasattr(self.model.config, '_attn_implementation'):
|
||||
actual_attn = self.model.config._attn_implementation
|
||||
print(f"[SCG_LocalVLM] Attention (verified): {actual_attn}")
|
||||
|
||||
# Show quantization info
|
||||
if quantization != "none":
|
||||
print(f"[SCG_LocalVLM] Quantization: {quantization}")
|
||||
if hasattr(self.model, 'hf_device_map'):
|
||||
print(f"[SCG_LocalVLM] Device map: {self.model.hf_device_map}")
|
||||
|
||||
# Check if model is actually quantized
|
||||
is_quantized = False
|
||||
for name, param in self.model.named_parameters():
|
||||
if hasattr(param, 'quant_state'):
|
||||
is_quantized = True
|
||||
break
|
||||
|
||||
if is_quantized:
|
||||
print(f"[SCG_LocalVLM] Quantization: VERIFIED (layers are quantized)")
|
||||
else:
|
||||
print(f"[SCG_LocalVLM] WARNING: Quantization requested but layers not quantized!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"[SCG_LocalVLM] Error loading model: {str(e)}")
|
||||
import traceback
|
||||
print(traceback.format_exc())
|
||||
|
||||
# Try fallback without attention implementation
|
||||
if "attn_implementation" in load_kwargs:
|
||||
print(f"[SCG_LocalVLM] Retrying without {attention_mode} attention...")
|
||||
load_kwargs.pop("attn_implementation", None)
|
||||
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
self.model_checkpoint,
|
||||
**load_kwargs,
|
||||
)
|
||||
print("[SCG_LocalVLM] Model loaded with fallback attention")
|
||||
else:
|
||||
raise
|
||||
|
||||
result = None
|
||||
with torch.no_grad():
|
||||
with torch.inference_mode():
|
||||
messages = [
|
||||
{"role": "system", "content": system},
|
||||
{"role": "user", "content": prompt},
|
||||
@@ -601,23 +995,76 @@ class Qwen:
|
||||
|
||||
inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
|
||||
|
||||
generated_ids = self.model.generate(
|
||||
**inputs,
|
||||
max_new_tokens=max_new_tokens,
|
||||
repetition_penalty=repetition_penalty,
|
||||
)
|
||||
# Build generation kwargs with proper parameter handling
|
||||
generation_kwargs = {
|
||||
"max_new_tokens": max_new_tokens,
|
||||
"do_sample": do_sample,
|
||||
"use_cache": True, # CRITICAL: Enable KV cache
|
||||
}
|
||||
|
||||
# Add pad_token_id to prevent warnings and improve batching
|
||||
if hasattr(self.tokenizer, 'pad_token_id') and self.tokenizer.pad_token_id is not None:
|
||||
generation_kwargs["pad_token_id"] = self.tokenizer.pad_token_id
|
||||
elif hasattr(self.tokenizer, 'eos_token_id') and self.tokenizer.eos_token_id is not None:
|
||||
generation_kwargs["pad_token_id"] = self.tokenizer.eos_token_id
|
||||
|
||||
# Add sampling parameters only if do_sample is True
|
||||
if do_sample:
|
||||
if temperature > 0:
|
||||
generation_kwargs["temperature"] = temperature
|
||||
if top_p < 1.0:
|
||||
generation_kwargs["top_p"] = top_p
|
||||
if top_k > 0:
|
||||
generation_kwargs["top_k"] = top_k
|
||||
if min_p > 0:
|
||||
generation_kwargs["min_p"] = min_p
|
||||
|
||||
# Always apply repetition penalty if not default
|
||||
if repetition_penalty != 1.0:
|
||||
generation_kwargs["repetition_penalty"] = repetition_penalty
|
||||
|
||||
# Start timing
|
||||
import time
|
||||
gen_start = time.time()
|
||||
|
||||
generated_ids = self.model.generate(**inputs, **generation_kwargs)
|
||||
|
||||
# Calculate performance
|
||||
gen_time = time.time() - gen_start
|
||||
|
||||
generated_ids_trimmed = [
|
||||
out_ids[len(in_ids) :]
|
||||
for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
|
||||
]
|
||||
|
||||
# Calculate tokens/sec
|
||||
if len(generated_ids_trimmed) > 0:
|
||||
tokens_generated = len(generated_ids_trimmed[0])
|
||||
tokens_per_sec = tokens_generated / gen_time if gen_time > 0 else 0
|
||||
print(f"[SCG_LocalVLM] Generation Performance:")
|
||||
print(f"[SCG_LocalVLM] Tokens: {tokens_generated}")
|
||||
print(f"[SCG_LocalVLM] Time: {gen_time:.2f}s")
|
||||
print(f"[SCG_LocalVLM] Speed: {tokens_per_sec:.2f} tokens/sec")
|
||||
|
||||
result = self.tokenizer.batch_decode(
|
||||
generated_ids_trimmed,
|
||||
skip_special_tokens=True,
|
||||
clean_up_tokenization_spaces=False,
|
||||
temperature=temperature,
|
||||
)
|
||||
except torch.cuda.OutOfMemoryError as e:
|
||||
_clear_cuda_memory()
|
||||
return (
|
||||
f"[SCG_LocalVLM] CUDA Out of Memory Error. "
|
||||
f"Try: 1) Enable quantization (4bit/8bit), "
|
||||
f"2) Reduce max_new_tokens, "
|
||||
f"3) Use a smaller model. "
|
||||
f"Error: {str(e)}",
|
||||
)
|
||||
except Exception as e:
|
||||
return (f"Error during model inference: {str(e)}",)
|
||||
import traceback
|
||||
error_details = traceback.format_exc()
|
||||
print(f"[SCG_LocalVLM] Error during inference:\n{error_details}")
|
||||
return (f"[SCG_LocalVLM] Error during model inference: {str(e)}",)
|
||||
finally:
|
||||
if not keep_model_loaded:
|
||||
self._unload_resources()
|
||||
|
||||
Reference in New Issue
Block a user