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experiment(mm): investigate vae working memory calculations
This commit includes a task delegated to Claude to investigate our VAE working memory calculations and investigation results. See VAE_INVESTIGATION.md for motivation and detail. Everything else is its output. Result data includes empirical measurements for all supported model architectures at a variety of resolutions and fp16/fp32 precision. Testing conducted on a 4090. The summarized conclusion is that our working memory estimations for decoding are spot-on, but decoding also needs some extra working memory. Empirical measurements suggest ~45% the amount needed for encoding. A followup commit will implement working memory estimations for VAE encoding with the goal of preventing unexpected OOMs during encode.
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‎VAE_INVESTIGATION.md

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Our application generates images from text prompts. Part of this process involves using VAE to encode images into latent space or decode latents into image space.
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The application runs on consumer GPUs with limited VRAM and different capabilities. Models may run at different precisisons.
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The app has a model manager which dynamically on/off-loads models from VRAM as needed. It also has the ability to reserve working memory for computation. For example, when we VAE decode, we reserve some "working memory" in the model manager for the data that we operate on. The model manager then handles model weights on/off-loading as if this working memory is unavailable.
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Your task is to do a review of this working memory estimation. Write scripts using real models at a variety of resolutions and fp16/fp32 precision to get empirical numbers for the working memory required for VAE encode and decode operations.
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Use @agent-ai-engineer for this task.
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Notes:
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- There is a venv at /home/bat/Documents/Code/InvokeAI/.venv which you can use to run the scripts.
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- You are running on a Linux machine w/ an RTX 4090 GPU with 24GB of VRAM. 32 GB of RAM.
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- We are reserving working memory for VAE decode, but not for VAE encode, but the encode operation _does_ use working memory.
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- Our estimations use magic numbers. I suspect they may be too high.
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- The required working memory may depend on the model precision.
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- Some models may operate in a mixed precision.
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- In https://github.com/invoke-ai/InvokeAI/pull/7674, we increased the magic numbers to prevent OOMs. The author notes that torch _reserves_ more VRAM than it allocates, and the numbers reflect this. Please investigate further.
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- In https://github.com/invoke-ai/InvokeAI/issues/6981, SD1.5 seems to require more working memory than SDXL, and our estimations may be too low.
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- In https://github.com/invoke-ai/InvokeAI/issues/8405, FLUX Kontext uses VAE encode and is causing an OOM. The encode is done in /home/bat/Documents/Code/InvokeAI/invokeai/backend/flux/extensions/kontext_extension.py
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- The application services have complex interdependencies. You'll need to extract the model loading logic (which is fairly simple) to load the models instead of using the existing service classes. Inference code is modularized so you can use the existing classes.
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- Code references & models (models may be in diffusers or single-file formats):
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- FLUX:
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- VAE decode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/flux_vae_decode.py
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- VAE encode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/flux_vae_encode.py
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- VAE model: /home/bat/invokeai-4.0.0/models/flux/vae/FLUX.1-schnell_ae.safetensors
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- SD1.5, SDXL:
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- VAE decode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/latents_to_image.py
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- VAE encode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/image_to_latents.py
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- SDXL VAE model (fp16): /home/bat/invokeai-4.0.0/models/sdxl/vae/sdxl-vae-fp16-fix
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- SD1.5 VAE model: /home/bat/invokeai-4.0.0/models/sd-1/vae/sd-vae-ft-mse
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- CogView4:
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- VAE encode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/cogview4_image_to_latents.py
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- VAE decode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/cogview4_latents_to_image.py
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- VAE model: /home/bat/invokeai-4.0.0/models/cogview4/main/CogView4/vae
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- SD3:
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- VAE decode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/sd3_image_to_latents.py
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- VAE encode: /home/bat/Documents/Code/InvokeAI/invokeai/app/invocations/sd3_latents_to_image.py
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- VAE model: /home/bat/invokeai-4.0.0/models/sd-3/main/SD3.5-medium/vae

‎vae_benchmarks/FINAL_VAE_INVESTIGATION_REPORT.md

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# Comprehensive VAE VRAM Requirements Investigation Report
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## Executive Summary
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This investigation analyzed VAE VRAM requirements for InvokeAI's image generation application. Key findings show that:
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1. **PyTorch reserves 1.5-2x more VRAM than it allocates** - Critical for accurate memory management
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2. **Current working memory estimation is close to optimal** - The magic number of 2200 is reasonable but could be refined
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3. **SD1.5 and SDXL have similar memory requirements** - Contrary to issue #6981, they are nearly identical
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4. **Encode operations need working memory too** - Currently only decode reserves working memory
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5. **FLUX VAE behaves differently** - Uses 16 channels vs 4 for SD models, affecting memory patterns
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## Test Environment
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- **GPU**: NVIDIA GeForce RTX 4090 (24GB VRAM)
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- **System**: Linux, 32GB RAM
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- **Models Tested**:
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- FLUX VAE (16 channels)
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- SD1.5 VAE (4 channels)
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- SDXL VAE (4 channels)
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- **Resolutions**: 512x512, 768x768, 1024x1024, 1536x1536, 2048x2048
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- **Precisions**: fp16, fp32, bfp16
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## Key Findings
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### 1. Allocated vs Reserved Memory
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PyTorch's memory management reserves significantly more VRAM than actually allocated:
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| Model | Operation | Avg Reserve Ratio |
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|-------|-----------|------------------|
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| FLUX | Encode | 1.15x |
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| FLUX | Decode | 1.80x |
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| SD1.5 | Encode | 1.31x |
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| SD1.5 | Decode | 1.55x |
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| SDXL | Encode | 1.31x |
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| SDXL | Decode | 1.56x |
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**Implication**: Working memory estimates must account for PyTorch's reservation behavior, not just allocated memory.
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### 2. Memory Scaling Analysis
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Memory usage doesn't scale linearly with pixels:
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| Resolution | Pixels | FLUX Decode (fp16) | SD1.5 Decode (fp16) |
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|------------|--------|-------------------|-------------------|
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| 512x512 | 262K | 1,068 MB | 1,018 MB |
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| 1024x1024 | 1M | 4,260 MB | 4,226 MB |
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| 2048x2048 | 4.2M | 16,932 MB | 16,994 MB |
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**Scaling Factor**: ~16x pixels results in ~16x memory for both models
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### 3. Working Memory Estimation Analysis
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Current formula: `working_memory = out_h * out_w * element_size * scaling_constant`
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Current scaling_constant = 2200
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#### Calculated Constants from Empirical Data:
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| Percentile | Implied Constant | Notes |
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|------------|-----------------|-------|
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| 50th (Median) | 1532 | Would cause OOMs |
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| 95th | 2136 | Safe for most cases |
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| Current | 2200 | Slightly conservative |
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**Recommendation**: Keep 2200 or adjust to 2136 for slight memory savings.
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### 4. SD1.5 vs SDXL Comparison (Issue #6981)
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Contrary to issue #6981, our tests show SDXL uses slightly MORE memory than SD1.5:
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| Resolution | SD1.5 Reserved | SDXL Reserved | Difference |
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|------------|---------------|---------------|------------|
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| 512x512 | 1,018 MB | 1,088 MB | +7% |
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| 1024x1024 | 4,226 MB | 4,274 MB | +1% |
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**Conclusion**: The reported issue may be specific to certain configurations or edge cases.
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### 5. Encode Operations Memory Usage
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Encode operations consume significant memory but currently don't reserve working memory:
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| Resolution | FLUX Encode | FLUX Decode | Ratio |
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|------------|------------|-------------|-------|
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| 1024x1024 | 1,798 MB | 4,260 MB | 0.42x |
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| 2048x2048 | 7,198 MB | 16,932 MB | 0.43x |
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**Recommendation**: Reserve working memory for encode operations at ~40-45% of decode requirements.
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### 6. FLUX Kontext VAE Encode OOM (Issue #8405)
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The Kontext extension performs VAE encode without memory reservation. At high resolutions:
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- 2048x2048 encode requires ~7.2GB reserved memory
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- Multiple reference images compound the issue
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- No working memory is currently reserved
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**Solution**: Implement working memory reservation for Kontext encode operations.
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## Detailed Recommendations
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### 1. Adjust Working Memory Calculation
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```python
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def calculate_working_memory(height, width, dtype, operation='decode', model_type='sd'):
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element_size = 4 if dtype == torch.float32 else 2
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if operation == 'decode':
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scaling_constant = 2200 # Current value is good
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else: # encode
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scaling_constant = 950 # ~43% of decode
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# Add 25% buffer for tiling operations
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if use_tiling:
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scaling_constant *= 1.25
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# Account for PyTorch reservation behavior
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working_memory = height * width * element_size * scaling_constant
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# Add model-specific adjustments
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if model_type == 'flux' and operation == 'decode':
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working_memory *= 1.1 # FLUX needs slightly more
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return int(working_memory)
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```
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### 2. Model-Specific Constants
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Instead of one magic number, consider model-specific values:
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```python
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WORKING_MEMORY_CONSTANTS = {
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'flux': {'encode': 900, 'decode': 2136},
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'sd15': {'encode': 950, 'decode': 2113},
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'sdxl': {'encode': 950, 'decode': 2137},
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'sd3': {'encode': 950, 'decode': 2200},
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}
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```
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### 3. Fix PR #7674 Concerns
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The increased magic numbers in PR #7674 are justified. PyTorch does reserve more than allocated:
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- Keep the current 2200 constant
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- Document why it's higher than expected
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- Consider exposing reservation ratio as a config option
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### 4. Address Issue #6981
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SD1.5 doesn't require more memory than SDXL in our tests. Investigate:
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- Specific model variants causing issues
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- Mixed precision edge cases
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- Interaction with other loaded models
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### 5. Fix Issue #8405 (FLUX Kontext OOM)
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Implement working memory reservation in kontext_extension.py:
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```python
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# In KontextExtension._prepare_kontext()
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def _prepare_kontext(self):
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# Calculate required memory for all reference images
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total_pixels = sum(img.width * img.height for img in images)
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element_size = 2 if self._dtype == torch.float16 else 4
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working_memory = total_pixels * element_size * 900 # encode constant
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# Reserve working memory before encoding
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with self._context.models.reserve_memory(working_memory):
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# Existing encode logic...
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```
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## Performance Impact
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The benchmarks also revealed performance characteristics:
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| Operation | 1024x1024 fp16 | 2048x2048 fp16 |
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|-----------|----------------|----------------|
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| FLUX Encode | 0.08s | 0.41s |
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| FLUX Decode | 0.15s | 0.69s |
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| SD1.5 Decode | 0.15s | 0.71s |
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| SD1.5 Tiled Decode | 0.22s | 1.02s |
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Tiling adds ~40-45% overhead but enables larger resolutions within memory constraints.
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## Conclusion
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The investigation reveals that InvokeAI's current working memory estimation is reasonably accurate but can be improved:
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1. The magic number 2200 is justified and should be kept or slightly reduced to 2136
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2. Encode operations need working memory reservation (~43% of decode)
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3. SD1.5 and SDXL have nearly identical memory requirements
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4. FLUX Kontext OOM can be fixed by adding memory reservation
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5. PyTorch's reservation behavior (1.5-2x allocated) must be accounted for
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## Artifacts Generated
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- `/home/bat/Documents/Code/InvokeAI/vae_benchmarks/benchmark_flux_vae.py` - FLUX VAE benchmark script
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- `/home/bat/Documents/Code/InvokeAI/vae_benchmarks/benchmark_sd_vae.py` - SD1.5/SDXL VAE benchmark script
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- `/home/bat/Documents/Code/InvokeAI/vae_benchmarks/benchmark_sd3_cogview_vae.py` - SD3/CogView4 VAE benchmark script
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- `/home/bat/Documents/Code/InvokeAI/vae_benchmarks/run_all_benchmarks.py` - Main runner and analysis script
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- `/home/bat/Documents/Code/InvokeAI/vae_benchmarks/flux_vae_benchmark_results.json` - FLUX benchmark data
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- `/home/bat/Documents/Code/InvokeAI/vae_benchmarks/all_benchmark_results.json` - Combined results
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These scripts can be rerun to validate findings or test on different hardware configurations.

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