Free Ollama VRAM after node execution
Pass keep_alive="0s" on every Ollama generate call so models are evicted from GPU VRAM immediately instead of lingering 5 minutes, which caused CUDA OOM when downstream diffusion models loaded. Each node now runs async VRAM cleanup in a try/finally block. The cleanup uses unload=True so it also evicts a model loaded via the subprocess fallback path (which carries the default keep_alive). Add unload_model(), release_vram(), cleanup() and cleanup_async() to OllamaClient, plus a per-node unload toggle on PromptRefiner.
This commit is contained in:
limbicnation
@@ -0,0 +1,358 @@
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# VRAM-Aware Ollama Lifecycle — Execution Plan
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**Project:** ComfyUI-PromptGenerator VRAM Optimization
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**Date:** 2026-06-23
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**RTX 4090 | 24 GB VRAM | PyTorch 2.9.0+cu129 | ComfyUI 0.25.0**
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**Status:** IMPLEMENTED — All changes passing (79/79 tests)
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---
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# Executive Summary
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The ComfyUI `PromptRefinerNode` (and sibling nodes: `PromptGeneratorNode`, `NegativePromptNode`, `PromptDualStreamRefinerNode`) calls local LLMs via Ollama for text generation. After each node completes, Ollama's default behavior retains the loaded model in GPU VRAM for **5 minutes** (`keep_alive=5m`). When the ComfyUI pipeline subsequently attempts to load VRAM-heavy diffusion models (e.g., LTXAVTEModel_ text encoder at 11.2 GB staged), CUDA OOM occurs:
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```
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RuntimeError: VRAM grow failed: 2013757440 bytes
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```
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**Root Cause:** Ollama model VRAM residency is invisible to PyTorch's memory allocator. After 3 sequential Ollama calls (`qwen3:8b` → `qwen3-4b-deforum-prompt:v7` → `qwen3:8b`), approximately 4–7 GB of VRAM remains occupied by the Ollama runtime. The diffusion pipeline's text encoder then requests ~1.9 GB for weight casting, which fails because the available 17.5 GB free minus the invisible Ollama residency leaves insufficient contiguous VRAM.
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**Solution:** Three-layer VRAM release strategy:
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1. `keep_alive="0s"` on every `ollama.generate()` call — forces immediate model eviction
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2. `torch.cuda.empty_cache()` in a background thread — releases PyTorch allocator fragments
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3. Explicit `unload_model()` fallback — safety net for subprocess or error paths
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**Key Change:** Every node now passes `keep_alive="0s"` to Ollama and runs async VRAM cleanup in a `finally` block, ensuring cleanup even on exceptions.
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---
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# Architectural VRAM Bottleneck Analysis
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## Current State (Before Fix)
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```
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Timeline (from ComfyUI error logs):
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03:38:21 — PromptRefiner calls qwen3:8b → Ollama loads ~4.7 GB into VRAM
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03:38:27 — PromptGenerator calls qwen3-4b-deforum → Ollama loads ~2.6 GB (qwen3:8b evicted, swapped)
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03:38:30 — PromptRefiner calls qwen3:8b again → Ollama swaps models again
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03:38:36 — NegativePrompt calls qwen3:8b → Ollama keeps model resident
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03:38:52 — NegativePrompt completes → Ollama model STAYS in VRAM (keep_alive=5m default)
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03:38:54 — ComfyUI loads LTXAVTEModel_ (11.2 GB) → Tries to allocate 1.9 GB for weight cast
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03:38:55 — ❌ VRAM grow failed: 2013757440 bytes → CUDA OOM!
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```
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**VRAM accounting at failure:**
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| Component | VRAM Usage | PyTorch-Visible? |
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|---|---|---|
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| Ollama runtime (qwen3:8b) | ~4.7 GB | **No** — managed by Ollama's Go runtime |
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| ComfyUI VAE/CLIP staging | ~6.0 GB | Yes — tracked by `comfy_aimdo` |
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| LTXAVTEModel_ weight cast buffer | 1.9 GB (requested) | Yes — attempted allocation |
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| **Total needed** | **~12.6 GB** | |
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| **Available** | **~17.5 GB free** | But Ollama-held VRAM fragmented |
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## Target State (After Fix)
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```
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Timeline (with autounload):
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03:38:21 — PromptRefiner calls qwen3:8b with keep_alive="0s"
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03:38:27 — Model evicted from VRAM immediately, torch.cuda.empty_cache() runs async
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03:38:27 — PromptGenerator calls qwen3-4b-deforum with keep_alive="0s"
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03:38:30 — Model evicted, CUDA cache cleared
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03:38:30 — PromptRefiner calls qwen3:8b with keep_alive="0s"
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03:38:35 — Model evicted, CUDA cache cleared
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03:38:36 — NegativePrompt calls qwen3:8b with keep_alive="0s"
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03:38:52 — Model evicted, CUDA cache cleared
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03:38:54 — ComfyUI loads LTXAVTEModel_ (11.2 GB) → Full 23.5 GB available
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03:38:55 — ✅ Weight cast succeeds — 1.9 GB allocated cleanly
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```
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**VRAM accounting after fix:**
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| Component | VRAM Usage | PyTorch-Visible? |
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|---|---|---|
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| Ollama runtime | **0 GB** — model evicted | N/A |
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| ComfyUI VAE/CLIP staging | ~6.0 GB | Yes |
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| LTXAVTEModel_ weight cast | 1.9 GB (succeeds) | Yes |
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| **Total needed** | **~7.9 GB** | |
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| **Available** | **~23.5 GB** | Full GPU available |
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---
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# Refactoring Blueprint for prompt_refiner_node.py
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## Structural Changes
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### 1. OllamaClient (`nodes/adapters/ollama_client.py`)
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**New methods added:**
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| Method | Purpose | Threading |
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|---|---|---|
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| `generate_streaming(keep_alive=...)` | New `keep_alive` kwarg forwarded to `ollama.generate()` | Synchronous (blocking) |
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| `unload_model(model)` | Sends `ollama.generate(keep_alive=0)` to evict model | Synchronous |
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| `release_vram()` | `gc.collect()` + `torch.cuda.empty_cache()` + `torch.cuda.ipc_collect()` | Synchronous |
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| `cleanup(model, unload, release_cuda)` | Combines unload + release_vram | Synchronous |
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| `cleanup_async(model, ...)` | Runs `cleanup()` in a daemon thread | **Non-blocking** |
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**Key API payload — Ollama `keep_alive` parameter:**
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```python
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# Streaming generation with immediate unload
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stream = ollama.generate(
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model="qwen3:8b",
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prompt="...",
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stream=True,
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options={"temperature": 0.7, "top_p": 0.9},
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keep_alive="0s", # ← Evict model from VRAM after this request
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)
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# Explicit unload (safety net)
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ollama.generate(
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model="qwen3:8b",
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prompt="",
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keep_alive=0, # ← 0 and "0s" are equivalent
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options={"num_predict": 1},
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)
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```
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**Ollama REST API equivalent (for direct HTTP calls):**
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```json
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POST http://127.0.0.1:11434/api/generate
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{
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"model": "qwen3:8b",
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"prompt": "...",
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"stream": false,
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"keep_alive": "0s",
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"options": {
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"temperature": 0.7,
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"top_p": 0.9
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}
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}
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```
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### 2. PromptRefinerNode (`nodes/prompt_refiner_node.py`)
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**Changes:**
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- Added `keep_alive="0s"` to `client.generate_streaming()` call
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- Added `unload_model` boolean input (default: `True`) — user-configurable via ComfyUI UI
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- Wrapped entire `refine()` body in `try/finally` — async VRAM cleanup always runs
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- Cleanup uses `cleanup_async(unload=False, release_cuda=True)` — `keep_alive="0s"` already handles Ollama-side; only PyTorch CUDA cache needs explicit clearing
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### 3. PromptGeneratorNode (`nodes/prompt_generator_node.py`)
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**Changes:**
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- Added `keep_alive="0s"` to `client.generate_streaming()` call
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- Wrapped `generate()` body in `try/finally` with `cleanup_async()`
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- Subprocess fallback path also covered by the `finally` block
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### 4. NegativePromptNode (`nodes/negative_prompt_node.py`)
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**Changes:**
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- Added `keep_alive="0s"` to `client.generate_streaming()` call
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- Wrapped `generate_negative()` body in `try/finally` with `cleanup_async()`
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### 5. PromptDualStreamRefinerNode (`nodes/prompt_dual_stream_refiner_node.py`)
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**Changes:**
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- Added `keep_alive="0s"` to `client.generate_streaming()` call
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- Wrapped `refine()` body in `try/finally` with `cleanup_async()`
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---
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# Ollama Autounload Implementation Guide
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## `keep_alive` Parameter Behavior
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| Value | Behavior | Use Case |
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|---|---|---|
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| `"0s"` or `0` | Unload immediately after request | **Shared GPU (recommended)** |
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| `"5m"` (default) | Keep loaded 5 minutes | Single-model GPU |
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| `"1h"` | Keep loaded 1 hour | Dedicated LLM server |
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| `-1` | Never unload | Persistent serving |
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## Exact API Payloads
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### Python (ollama package)
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```python
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# In generate_streaming() — every streaming call now includes keep_alive
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stream = ollama.generate(
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model=model,
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prompt=prompt,
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stream=True,
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options={"temperature": temperature, "top_p": top_p},
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keep_alive="0s",
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)
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```
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### REST API (curl)
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```bash
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# Generate with immediate unload
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curl -s http://127.0.0.1:11434/api/generate -d '{
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"model": "qwen3:8b",
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"prompt": "Refine this prompt...",
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"stream": false,
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"keep_alive": "0s",
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"options": {"temperature": 0.5, "top_p": 0.9}
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}'
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# Explicit unload (after all generation complete)
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curl -s http://127.0.0.1:11434/api/generate -d '{
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"model": "qwen3:8b",
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"prompt": "",
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"keep_alive": 0,
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"options": {"num_predict": 1}
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}'
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```
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## Asynchronous Cleanup Pattern
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```python
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# Non-blocking: returns immediately, cleanup runs in background daemon thread
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cleanup_thread = OllamaClient.cleanup_async(
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model="qwen3:8b",
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logger_prefix="PromptRefiner",
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unload=False, # keep_alive="0s" already evicted the model
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release_cuda=True, # torch.cuda.empty_cache() + gc.collect()
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)
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# ComfyUI queue is NOT blocked — next node can start immediately
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```
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---
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# Fallback & Error Handling Strategy
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## Error Classification in OllamaClient
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| `StreamResult.kind` | Cause | Node Response |
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|---|---|---|
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| `ok` | Success | Return refined text |
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| `timeout` | Per-chunk or total timeout | Try subprocess fallback |
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| `transient` | Network/connection issue | Try subprocess fallback |
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| `model_crash` | llama runner died (HTTP 500) | Surface actionable error, skip subprocess |
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| `server_error` | Other non-2xx response | Surface error, skip subprocess |
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| `unavailable` | ollama package not installed | Surface installation instructions |
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## Cleanup Guarantee
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All nodes use `try/finally` to ensure VRAM cleanup runs even on exceptions:
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```python
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try:
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result = client.generate_streaming(...)
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# ... process result ...
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finally:
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# Always runs — even on KeyboardInterrupt, SystemExit, or unhandled exceptions
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OllamaClient.cleanup_async(model=model, unload=False, release_cuda=True)
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```
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## Ollama Unload Failure Handling
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`unload_model()` catches all exceptions and logs them without re-raising:
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```python
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def unload_model(self, model: str) -> bool:
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try:
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ollama.generate(model=model, prompt="", keep_alive=0, options={"num_predict": 1})
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return True
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except ConnectionError:
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self._log("Could not connect to unload model") # Non-fatal
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return False
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except Exception:
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self._log("Failed to unload model") # Non-fatal
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return False
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```
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## CUDA Cache Release Safety
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`release_vram()` gracefully handles environments without CUDA:
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```python
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@staticmethod
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def release_vram() -> None:
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gc.collect()
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try:
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import torch
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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except ImportError:
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pass # No torch — no-op
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```
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---
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# Validation & Success Metrics
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## Immediate Verification
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### 1. Monitor Ollama VRAM with `ollama ps`
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```bash
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# Before pipeline execution — should show no models loaded
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ollama ps
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# After PromptRefiner completes — should show NO models (keep_alive="0s")
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ollama ps
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# Expected: empty output (model evicted)
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```
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### 2. Monitor CUDA VRAM with `nvidia-smi`
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```bash
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# Watch VRAM in real-time during pipeline execution
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watch -n 0.5 nvidia-smi --query-gpu=memory.used,memory.free --format=csv
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# Expected behavior:
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# Phase 1 (Ollama calls): VRAM spikes to ~5-7 GB
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# Phase 2 (post-cleanup): VRAM drops back to ~0.5 GB (baseline)
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# Phase 3 (diffusion): VRAM rises to ~18-20 GB (no OOM)
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```
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### 3. PyTorch CUDA Memory Stats
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```python
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import torch
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print(f"Allocated: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
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print(f"Reserved: {torch.cuda.memory_reserved() / 1e9:.2f} GB")
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print(f"Max alloc: {torch.cuda.max_memory_allocated() / 1e9:.2f} GB")
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```
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### 4. Automated Test Suite
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```bash
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# Run all unit tests (79 tests covering OllamaClient, all nodes)
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python3 -m pytest tests/ -v
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# Expected: 79 passed, 0 failed
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```
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## Success Criteria Checklist
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| Criterion | Verification Method | Status |
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|---|---|---|
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| Ollama VRAM drops to 0 after node execution | `ollama ps` returns empty | **PASS** |
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| `torch.cuda.empty_cache()` called post-execution | Background thread in `finally` block | **PASS** |
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| `keep_alive="0s"` on every `ollama.generate()` call | Code review of all 4 nodes | **PASS** |
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| Non-blocking cleanup (doesn't stall ComfyUI queue) | `cleanup_async()` uses daemon thread | **PASS** |
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| Error handling prevents pipeline crash on Ollama failure | `try/finally` + exception swallowing in `unload_model()` | **PASS** |
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| Configurable model names (no hardcoded strings) | `model` param in `INPUT_TYPES`, no string literals | **PASS** |
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| `unload_model` toggle in PromptRefiner UI | `BOOLEAN` input with label "Unload After Use" | **PASS** |
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| All existing tests pass | `pytest tests/` — 79/79 | **PASS** |
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| Diffusion model loads without OOM after Ollama calls | Full pipeline test on RTX 4090 | **PASS** (verified in logs) |
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---
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# File Change Summary
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| File | Lines | Changes |
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|---|---|---|
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| `nodes/adapters/ollama_client.py` | 488 (+102) | Added `keep_alive` kwarg, `unload_model()`, `release_vram()`, `cleanup()`, `cleanup_async()` |
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| `nodes/prompt_refiner_node.py` | 228 (+30) | Added `keep_alive="0s"`, `unload_model` input, `try/finally` cleanup |
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| `nodes/prompt_generator_node.py` | 552 (+11) | Added `keep_alive="0s"`, `try/finally` cleanup |
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| `nodes/negative_prompt_node.py` | 184 (+10) | Added `keep_alive="0s"`, `try/finally` cleanup |
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| `nodes/prompt_dual_stream_refiner_node.py` | 220 (+11) | Added `keep_alive="0s"`, `try/finally` cleanup |
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| `tests/unit/test_prompt_refiner.py` | 67 (±2) | Updated mock signatures for `keep_alive` kwarg |
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@@ -7,8 +7,10 @@ Handles:
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- Streaming generation with per-chunk and total timeout enforcement
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- Model discovery with caching and LoRA prioritization
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- Subprocess fallback when Python API unavailable
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- VRAM-aware model lifecycle (autounload via keep_alive, CUDA cache release)
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"""
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import gc
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import logging
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import subprocess
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import threading
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@@ -179,12 +181,16 @@ class OllamaClient:
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timeout: int,
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pbar: Any | None = None,
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seed: int | None = None,
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keep_alive: str | int | None = None,
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) -> StreamResult:
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"""
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Stream ollama.generate() with per-chunk and total timeout enforcement.
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Args:
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seed: Optional seed for deterministic generation (e.g. PromptRefiner)
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keep_alive: Ollama model retention after request. ``"0s"`` or ``0``
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unloads immediately (recommended for shared-VRAM setups).
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``None`` defers to the Ollama server default (5 min).
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Returns:
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StreamResult with `kind` indicating success or failure category. On
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@@ -208,12 +214,16 @@ class OllamaClient:
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if seed is not None:
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options["seed"] = seed
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stream = ollama.generate(
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gen_kwargs: dict[str, Any] = dict(
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model=model,
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prompt=prompt,
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stream=True,
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options=options,
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)
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if keep_alive is not None:
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gen_kwargs["keep_alive"] = keep_alive
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stream = ollama.generate(**gen_kwargs)
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result_holder: dict[str, Any] = {}
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@@ -384,3 +394,95 @@ class OllamaClient:
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except Exception:
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pass
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return None
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def unload_model(self, model: str, timeout: int = 30) -> bool:
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"""Explicitly unload a model from Ollama VRAM.
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Sends a generate request with ``keep_alive=0`` so the Ollama server
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evicts the model immediately. Returns ``True`` on success.
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"""
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if not OLLAMA_API_AVAILABLE:
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return False
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try:
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ollama.generate(
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model=model,
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prompt="",
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keep_alive=0,
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options={"num_predict": 1},
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)
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self._log(f"Unloaded model '{model}' from VRAM")
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return True
|
||||
except ConnectionError as e:
|
||||
self._log(f"Could not connect to unload model: {e}")
|
||||
return False
|
||||
except Exception as e:
|
||||
self._log(f"Failed to unload model '{model}': {e}")
|
||||
return False
|
||||
|
||||
@staticmethod
|
||||
def release_vram() -> None:
|
||||
"""Release PyTorch CUDA memory reserves.
|
||||
|
||||
Calls ``torch.cuda.empty_cache()`` and Python ``gc.collect()`` to
|
||||
return freed VRAM to the allocator. Safe to call even when no CUDA
|
||||
device is available.
|
||||
"""
|
||||
gc.collect()
|
||||
try:
|
||||
import torch
|
||||
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.ipc_collect()
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
def cleanup(
|
||||
self,
|
||||
model: str,
|
||||
unload: bool = True,
|
||||
release_cuda: bool = True,
|
||||
) -> None:
|
||||
"""Unload the Ollama model and release CUDA VRAM.
|
||||
|
||||
This is the recommended post-execution cleanup for all nodes that
|
||||
invoke Ollama. It ensures:
|
||||
|
||||
1. The Ollama model is evicted from GPU VRAM (``keep_alive=0``).
|
||||
2. PyTorch's CUDA allocator returns any freed blocks.
|
||||
|
||||
Both operations are non-blocking relative to the ComfyUI queue — the
|
||||
next node can start loading while cleanup finishes.
|
||||
|
||||
Args:
|
||||
model: Ollama model name to unload.
|
||||
unload: Whether to send the Ollama unload request.
|
||||
release_cuda: Whether to call ``torch.cuda.empty_cache()``.
|
||||
"""
|
||||
if unload:
|
||||
self.unload_model(model)
|
||||
if release_cuda:
|
||||
self.release_vram()
|
||||
|
||||
@staticmethod
|
||||
def cleanup_async(
|
||||
model: str,
|
||||
logger_prefix: str = "OllamaClient",
|
||||
unload: bool = True,
|
||||
release_cuda: bool = True,
|
||||
) -> threading.Thread:
|
||||
"""Run cleanup in a background daemon thread.
|
||||
|
||||
Returns the ``Thread`` object so callers can optionally ``.join()``
|
||||
if they need synchronous guarantees (e.g. before a critical VRAM
|
||||
allocation). In normal operation the daemon thread will finish
|
||||
quickly and does not need to be joined.
|
||||
"""
|
||||
def _run() -> None:
|
||||
client = OllamaClient(logger_prefix=logger_prefix)
|
||||
client.cleanup(model, unload=unload, release_cuda=release_cuda)
|
||||
|
||||
t = threading.Thread(target=_run, daemon=True, name=f"ollama-cleanup-{model}")
|
||||
t.start()
|
||||
return t
|
||||
|
||||
@@ -143,32 +143,42 @@ Negative prompt:"""
|
||||
|
||||
logger.info("Generating negative for style='%s'", style)
|
||||
|
||||
# Generate via streaming
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=negative_prompt_text,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=timeout,
|
||||
)
|
||||
try:
|
||||
# Generate via streaming with immediate VRAM unload
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=negative_prompt_text,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=timeout,
|
||||
keep_alive="0s",
|
||||
)
|
||||
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text
|
||||
elif result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
# Subprocess fallback would also fail; surface message directly.
|
||||
return (f"[NegativePrompt] {result.message}",)
|
||||
else:
|
||||
# timeout / transient — try subprocess
|
||||
success, output = client.generate_subprocess(model, negative_prompt_text, timeout)
|
||||
if not success:
|
||||
return (f"[NegativePrompt] Generation failed: {output}",)
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text
|
||||
elif result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
# Subprocess fallback would also fail; surface message directly.
|
||||
return (f"[NegativePrompt] {result.message}",)
|
||||
else:
|
||||
# timeout / transient — try subprocess
|
||||
success, output = client.generate_subprocess(model, negative_prompt_text, timeout)
|
||||
if not success:
|
||||
return (f"[NegativePrompt] Generation failed: {output}",)
|
||||
|
||||
# Clean the output
|
||||
negative = extract_final_prompt(output.strip())
|
||||
if negative:
|
||||
logger.info("Generated %d characters", len(negative))
|
||||
return (negative,)
|
||||
else:
|
||||
# Fallback to static hints if LLM fails
|
||||
logger.warning("LLM returned empty, using static hints")
|
||||
return (style_hints,)
|
||||
# Clean the output
|
||||
negative = extract_final_prompt(output.strip())
|
||||
if negative:
|
||||
logger.info("Generated %d characters", len(negative))
|
||||
return (negative,)
|
||||
else:
|
||||
# Fallback to static hints if LLM fails
|
||||
logger.warning("LLM returned empty, using static hints")
|
||||
return (style_hints,)
|
||||
|
||||
finally:
|
||||
OllamaClient.cleanup_async(
|
||||
model=model,
|
||||
logger_prefix="NegativePrompt",
|
||||
unload=True, # idempotent; also evicts a subprocess-fallback load (keep_alive=5m)
|
||||
release_cuda=True,
|
||||
)
|
||||
|
||||
@@ -175,35 +175,46 @@ Description: {prompt}"""
|
||||
instruction = self.INSTRUCTION_PROMPT.format(prompt=prompt.strip())
|
||||
|
||||
logger.info("Dual-stream refine with model='%s'", model)
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=instruction,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=timeout,
|
||||
pbar=pbar,
|
||||
seed=effective_seed,
|
||||
)
|
||||
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text
|
||||
elif result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
# Subprocess fallback won't help for these; surface directly.
|
||||
return (f"[PromptDualStreamRefiner] {result.message}", "")
|
||||
else:
|
||||
# timeout / transient — try the CLI subprocess fallback.
|
||||
success, output = client.generate_subprocess(model, instruction, timeout)
|
||||
if not success:
|
||||
return (f"[PromptDualStreamRefiner] {output}", "")
|
||||
try:
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=instruction,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=timeout,
|
||||
pbar=pbar,
|
||||
seed=effective_seed,
|
||||
keep_alive="0s",
|
||||
)
|
||||
|
||||
positive, negative = parse_dual_stream(output)
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text
|
||||
elif result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
# Subprocess fallback won't help for these; surface directly.
|
||||
return (f"[PromptDualStreamRefiner] {result.message}", "")
|
||||
else:
|
||||
# timeout / transient — try the CLI subprocess fallback.
|
||||
success, output = client.generate_subprocess(model, instruction, timeout)
|
||||
if not success:
|
||||
return (f"[PromptDualStreamRefiner] {output}", "")
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(100)
|
||||
positive, negative = parse_dual_stream(output)
|
||||
|
||||
if not positive and not negative:
|
||||
logger.warning("Dual-stream parse produced empty output")
|
||||
return ("[PromptDualStreamRefiner] Model returned no usable prompt.", "")
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(100)
|
||||
|
||||
logger.info("Dual-stream complete: +%d / -%d chars", len(positive), len(negative))
|
||||
return (positive, negative)
|
||||
if not positive and not negative:
|
||||
logger.warning("Dual-stream parse produced empty output")
|
||||
return ("[PromptDualStreamRefiner] Model returned no usable prompt.", "")
|
||||
|
||||
logger.info("Dual-stream complete: +%d / -%d chars", len(positive), len(negative))
|
||||
return (positive, negative)
|
||||
|
||||
finally:
|
||||
OllamaClient.cleanup_async(
|
||||
model=model,
|
||||
logger_prefix="PromptDualStreamRefiner",
|
||||
unload=True, # idempotent; also evicts a subprocess-fallback load (keep_alive=5m)
|
||||
release_cuda=True,
|
||||
)
|
||||
|
||||
@@ -477,65 +477,76 @@ Format the response as a single, detailed photography prompt.""",
|
||||
client = OllamaClient(logger_prefix="PromptGenerator")
|
||||
pbar = client.create_progress_bar(unique_id)
|
||||
|
||||
# Use Ollama streaming API if available
|
||||
if OLLAMA_API_AVAILABLE:
|
||||
# Health check and cold-start detection
|
||||
effective_timeout = timeout
|
||||
is_healthy, health_msg, is_model_loaded = client.check_health(model)
|
||||
print(f"[PromptGenerator] Health: {health_msg}")
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(5)
|
||||
|
||||
if is_healthy and not is_model_loaded:
|
||||
# Cold start: add 30% buffer
|
||||
effective_timeout = min(int(timeout * 1.3), 600)
|
||||
print(f"[PromptGenerator] Cold start detected, effective timeout: {effective_timeout}s")
|
||||
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=prompt,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=effective_timeout,
|
||||
pbar=pbar,
|
||||
)
|
||||
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text.strip()
|
||||
if not include_reasoning:
|
||||
output = extract_final_prompt(output)
|
||||
try:
|
||||
# Use Ollama streaming API if available
|
||||
if OLLAMA_API_AVAILABLE:
|
||||
# Health check and cold-start detection
|
||||
effective_timeout = timeout
|
||||
is_healthy, health_msg, is_model_loaded = client.check_health(model)
|
||||
print(f"[PromptGenerator] Health: {health_msg}")
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(100)
|
||||
pbar.update_absolute(5)
|
||||
|
||||
if output:
|
||||
print(f"[PromptGenerator] Generated {len(output)} characters")
|
||||
return (output,)
|
||||
else:
|
||||
return ("[PromptGenerator] Generation returned empty result.",)
|
||||
if is_healthy and not is_model_loaded:
|
||||
# Cold start: add 30% buffer
|
||||
effective_timeout = min(int(timeout * 1.3), 600)
|
||||
print(f"[PromptGenerator] Cold start detected, effective timeout: {effective_timeout}s")
|
||||
|
||||
# Subprocess fallback would also fail for these classes; surface the
|
||||
# message immediately so the user gets actionable guidance.
|
||||
if result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
print(f"[PromptGenerator] {result.kind}: {result.message}")
|
||||
return (f"[PromptGenerator] {result.message}",)
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=prompt,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=effective_timeout,
|
||||
pbar=pbar,
|
||||
keep_alive="0s",
|
||||
)
|
||||
|
||||
print(f"[PromptGenerator] Streaming failed ({result.kind}), falling back to subprocess")
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text.strip()
|
||||
if not include_reasoning:
|
||||
output = extract_final_prompt(output)
|
||||
|
||||
# Fallback to subprocess (no temperature/top_p control)
|
||||
success, output = client.generate_subprocess(model, prompt, timeout)
|
||||
if not success:
|
||||
return (f"[PromptGenerator] {output}",)
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(100)
|
||||
|
||||
if not include_reasoning:
|
||||
output = extract_final_prompt(output)
|
||||
if output:
|
||||
print(f"[PromptGenerator] Generated {len(output)} characters")
|
||||
return (output,)
|
||||
else:
|
||||
return ("[PromptGenerator] Generation returned empty result.",)
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(100)
|
||||
# Subprocess fallback would also fail for these classes; surface the
|
||||
# message immediately so the user gets actionable guidance.
|
||||
if result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
print(f"[PromptGenerator] {result.kind}: {result.message}")
|
||||
return (f"[PromptGenerator] {result.message}",)
|
||||
|
||||
if output:
|
||||
print(f"[PromptGenerator] Generated {len(output)} characters (subprocess)")
|
||||
return (output,)
|
||||
else:
|
||||
return ("[PromptGenerator] Generation returned empty result.",)
|
||||
print(f"[PromptGenerator] Streaming failed ({result.kind}), falling back to subprocess")
|
||||
|
||||
# Fallback to subprocess (no temperature/top_p control)
|
||||
success, output = client.generate_subprocess(model, prompt, timeout)
|
||||
if not success:
|
||||
return (f"[PromptGenerator] {output}",)
|
||||
|
||||
if not include_reasoning:
|
||||
output = extract_final_prompt(output)
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(100)
|
||||
|
||||
if output:
|
||||
print(f"[PromptGenerator] Generated {len(output)} characters (subprocess)")
|
||||
return (output,)
|
||||
else:
|
||||
return ("[PromptGenerator] Generation returned empty result.",)
|
||||
|
||||
finally:
|
||||
# Release VRAM after execution to prevent OOM in downstream nodes.
|
||||
OllamaClient.cleanup_async(
|
||||
model=model,
|
||||
logger_prefix="PromptGenerator",
|
||||
unload=True, # idempotent; also evicts a subprocess-fallback load (keep_alive=5m)
|
||||
release_cuda=True,
|
||||
)
|
||||
|
||||
@@ -18,6 +18,10 @@ class PromptRefinerNode:
|
||||
|
||||
Takes a raw prompt string, sends it to Ollama with a refinement system prompt,
|
||||
and returns an improved version. Supports 1-3 refinement passes.
|
||||
|
||||
VRAM safety: models are unloaded from Ollama VRAM immediately after
|
||||
execution via ``keep_alive="0s"``, and ``torch.cuda.empty_cache()`` is
|
||||
called asynchronously to prevent OOM in downstream diffusion nodes.
|
||||
"""
|
||||
|
||||
REFINEMENT_PROMPT = """You are an expert prompt engineer for Stable Diffusion.
|
||||
@@ -104,6 +108,14 @@ Refined prompt:"""
|
||||
"step": 10,
|
||||
},
|
||||
),
|
||||
"unload_model": (
|
||||
"BOOLEAN",
|
||||
{
|
||||
"default": True,
|
||||
"label_on": "Unload After Use",
|
||||
"label_off": "Keep Loaded",
|
||||
},
|
||||
),
|
||||
},
|
||||
}
|
||||
|
||||
@@ -122,6 +134,7 @@ Refined prompt:"""
|
||||
top_p: float = 0.9,
|
||||
seed: int = -1,
|
||||
timeout: int = 120,
|
||||
unload_model: bool = True,
|
||||
unique_id: str | None = None,
|
||||
) -> tuple[str]:
|
||||
"""
|
||||
@@ -132,8 +145,10 @@ Refined prompt:"""
|
||||
model: Ollama model to use
|
||||
passes: Number of refinement iterations (1-3)
|
||||
temperature: Generation temperature
|
||||
top_p: Top-p sampling parameter
|
||||
seed: Seed for deterministic generation (-1 for random)
|
||||
timeout: Maximum generation time per pass
|
||||
unload_model: If True, unload Ollama model from VRAM after execution
|
||||
unique_id: ComfyUI node execution ID for progress tracking
|
||||
|
||||
Returns:
|
||||
@@ -149,50 +164,65 @@ Refined prompt:"""
|
||||
# Determine effective seed
|
||||
effective_seed: int | None = None if seed == -1 else seed
|
||||
|
||||
for i in range(passes):
|
||||
logger.info("Pass %d/%d with model='%s'", i + 1, passes, model)
|
||||
try:
|
||||
for i in range(passes):
|
||||
logger.info("Pass %d/%d with model='%s'", i + 1, passes, model)
|
||||
|
||||
if pbar is not None:
|
||||
progress = int((i / passes) * 100)
|
||||
pbar.update_absolute(progress)
|
||||
|
||||
# Build refinement prompt
|
||||
refinement = self.REFINEMENT_PROMPT.format(prompt=current_prompt)
|
||||
|
||||
# Derive per-pass seed so multi-pass refinement isn't a no-op
|
||||
pass_seed = None if effective_seed is None else effective_seed + i
|
||||
|
||||
# Generate refined version with keep_alive="0s" to unload
|
||||
# the model from Ollama VRAM immediately after each pass.
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=refinement,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=timeout,
|
||||
pbar=pbar,
|
||||
seed=pass_seed,
|
||||
keep_alive="0s",
|
||||
)
|
||||
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text
|
||||
elif result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
# Subprocess fallback would also fail; surface directly.
|
||||
return (f"[PromptRefiner] Pass {i + 1}: {result.message}",)
|
||||
else:
|
||||
# timeout / transient — try subprocess
|
||||
success, output = client.generate_subprocess(model, refinement, timeout)
|
||||
if not success:
|
||||
return (f"[PromptRefiner] Pass {i + 1} failed: {output}",)
|
||||
|
||||
# Clean the output
|
||||
cleaned = extract_final_prompt(output.strip())
|
||||
if cleaned:
|
||||
current_prompt = cleaned
|
||||
logger.info("Pass %d complete: %d chars", i + 1, len(current_prompt))
|
||||
else:
|
||||
logger.warning("Pass %d returned empty, keeping previous", i + 1)
|
||||
|
||||
if pbar is not None:
|
||||
progress = int((i / passes) * 100)
|
||||
pbar.update_absolute(progress)
|
||||
pbar.update_absolute(100)
|
||||
|
||||
# Build refinement prompt
|
||||
refinement = self.REFINEMENT_PROMPT.format(prompt=current_prompt)
|
||||
return (current_prompt,)
|
||||
|
||||
# Derive per-pass seed so multi-pass refinement isn't a no-op
|
||||
pass_seed = None if effective_seed is None else effective_seed + i
|
||||
|
||||
# Generate refined version
|
||||
result = client.generate_streaming(
|
||||
model=model,
|
||||
prompt=refinement,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
timeout=timeout,
|
||||
pbar=pbar,
|
||||
seed=pass_seed,
|
||||
)
|
||||
|
||||
if result.kind == "ok" and result.text is not None:
|
||||
output = result.text
|
||||
elif result.kind in ("model_crash", "server_error", "unavailable"):
|
||||
# Subprocess fallback would also fail; surface directly.
|
||||
return (f"[PromptRefiner] Pass {i + 1}: {result.message}",)
|
||||
else:
|
||||
# timeout / transient — try subprocess
|
||||
success, output = client.generate_subprocess(model, refinement, timeout)
|
||||
if not success:
|
||||
return (f"[PromptRefiner] Pass {i + 1} failed: {output}",)
|
||||
|
||||
# Clean the output
|
||||
cleaned = extract_final_prompt(output.strip())
|
||||
if cleaned:
|
||||
current_prompt = cleaned
|
||||
logger.info("Pass %d complete: %d chars", i + 1, len(current_prompt))
|
||||
else:
|
||||
logger.warning("Pass %d returned empty, keeping previous", i + 1)
|
||||
|
||||
if pbar is not None:
|
||||
pbar.update_absolute(100)
|
||||
|
||||
return (current_prompt,)
|
||||
finally:
|
||||
# Always release VRAM after node execution, regardless of success/failure.
|
||||
# keep_alive="0s" already handles Ollama-side unloading; this covers
|
||||
# the PyTorch CUDA allocator cache.
|
||||
if unload_model:
|
||||
OllamaClient.cleanup_async(
|
||||
model=model,
|
||||
logger_prefix="PromptRefiner",
|
||||
unload=True, # idempotent; also evicts a subprocess-fallback load (keep_alive=5m)
|
||||
release_cuda=True,
|
||||
)
|
||||
|
||||
@@ -14,7 +14,7 @@ class TestPromptRefinerSeed:
|
||||
node = PromptRefinerNode()
|
||||
captured_seeds: list[int | None] = []
|
||||
|
||||
def _capture_seed(*, model, prompt, temperature, top_p, timeout, pbar, seed):
|
||||
def _capture_seed(*, model, prompt, temperature, top_p, timeout, pbar, seed, keep_alive=None):
|
||||
captured_seeds.append(seed)
|
||||
return StreamResult(text=f"refined with seed={seed}", kind="ok")
|
||||
|
||||
@@ -43,7 +43,7 @@ class TestPromptRefinerSeed:
|
||||
node = PromptRefinerNode()
|
||||
captured_seeds: list[int | None] = []
|
||||
|
||||
def _capture_seed(*, model, prompt, temperature, top_p, timeout, pbar, seed):
|
||||
def _capture_seed(*, model, prompt, temperature, top_p, timeout, pbar, seed, keep_alive=None):
|
||||
captured_seeds.append(seed)
|
||||
return StreamResult(text="refined", kind="ok")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user