Pinokio Update Breaks VRAM Validation for 4B DiT Models & Flash Attention (Python 3.12 Environment Mismatch)

NVIDIA GeForce RTX 3080 Ti Laptop GPU (16GB VRAM), 32GB RAM, Intel 12th Gen Core i9

A recent Pinokio update migrated the application's underlying environment to Python 3.12.8, breaking the memory validation logic and rendering the Flash Attention initialization toggle completely inactive/grayed out in the Gradio UI.
Consequently, the service initialization block now flags and hard-blocks flagship 4B models (acestep-v15-xl-sft and acestep-v15-xl-turbo-bf16) on 16GB VRAM GPUs (such as the RTX 3080 Ti Laptop GPU). It throws a false error stating the model is too heavy, forcing a switch to 2B variants. This occurs even though these identical 4B models previously generated full-length tracks on this exact hardware prior to the environment update.

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1. Python 3.12 Matrix & Flash Attention Mismatch

• Outdated Dependency Anchors: The repository's manifests are locked to older compiled wheel paths targeted strictly at Python 3.11 (-cp311-cp311-win_amd64.whl from the sdbds repository).
• Installer Rejection: Because Pinokio updated the isolated environment to Python 3.12.8, the native package managers (pip and uv) reject the hardcoded cp311 wheels as incompatible platform setups.
• Gradio UI Deactivation: Submodules like nano-vllm remain statically anchored to the older release configurations. When uv sync attempts to rebuild the environment, conflicts prevent proper integration of modern cp312 wheels. As a result, the Flash Attention engine cannot be initialized, leaving its control option completely unclickable in the UI.

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2. Broken Tier Validation Logic in gpu_config.py

• Previous Setup (Working Baseline): The system smoothly ran even the massive XL-SFT workflow on extreme configurations (up to 136 inference steps with Thinking/CoT mode active), shifting data across VRAM boundaries without OOM or crashes.
• Current Setup (Broken State): Selecting any 4B/XL variant triggers an immediate validation failure inside the Gradio UI. The generate action component is entirely locked out based on flawed, static pre-flight estimations, while the Flash Attention initialization switch remains permanently inactive.

Details (Suggested Reasons)

• Flawed OS Metrics: The internal profiling system flags the 16GB card under tier6a. However, the function get_effective_free_vram_gb() relies on calling torch.cuda.mem_get_info(), which returns raw, real-time operating system metrics.
• Static Calculations: Standard OS and background overhead inevitably reduce actual reported free VRAM to roughly 13.5–14.2GB. The weight calculator (MODEL_VRAM + DIT_INFERENCE_VRAM_PER_BATCH) benchmarks total allocation requirements based on unaccelerated layers without factoring in active memory-saving features—such as Offload to CPU, Offload DiT to CPU, and INT8 Quantization.
• Pre-Flight Roadblock: Instead of allowing the PyTorch allocator to handle memory dynamically, the hook encounters a fatal condition check and halts the process before the hardware can even begin the inference pass.

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Proposed Remediations

1. Relax Pre-Flight Memory Caps: Convert strict raise gr.Error validation gates within the service setup script into non-blocking warnings, or delay evaluation until runtime allocation boundaries are established after CPU offloading kicks in.
2. Decouple Flash Attention Wheel Mismatches: Remove rigid, fixed-URL dependency anchors targeted exclusively at cp311 assets in main/submodule manifests to allow clean resolution of Python 3.12 pre-compiled wheels.