Update README for flux1-schnell-fp8-blackwell
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README.md
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TensorRT-RTX
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- **Base Model**: black-forest-labs/FLUX.1-schnell
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- **Architecture**: Blackwell
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- **Quantization**: FP8
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- **Batch Size**: 1 (optimized)
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- **Resolution**: 1024x1024 (optimized)
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- **Estimated Size**: 6.0 GB
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- **
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##
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- `config.json`: Configuration metadata
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- `README.md`: This file
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```python
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from imageai_server.tensorrt.nvidia_sdxl_pipeline import NVIDIASDXLPipeline
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pipeline = NVIDIASDXLPipeline()
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pipeline.load_engines(
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engine_dir="
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framework_model_dir="
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onnx_dir="
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)
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pipeline.activate_engines()
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images, time_ms = pipeline.infer(
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prompt="a
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height=1024,
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width=1024
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)
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```
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##
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- **VRAM**: 6.0GB+
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- **TensorRT-RTX**: 1.0.0.21+
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- **CUDA**: 12.0+
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---
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library_name: tensorrt-rtx
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license: apache-2.0
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base_model: black-forest-labs/FLUX.1-schnell
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tags:
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- tensorrt-rtx
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- flux1
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- fp8
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- schnell
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- optimized
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inference: false
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---
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# FLUX1 TensorRT-RTX: SCHNELL-Fp8 π¨ Building
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Optimized TensorRT-RTX engines for **FLUX1** on **Fp8** architecture with **SCHNELL** quantization.
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## π― This Repository
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**One variant, one download** - only get exactly what you need!
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- **Model**: FLUX1
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- **Architecture**: Fp8 (Compute Capability 8.0+)
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- **Quantization**: SCHNELL
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- **Memory**: TBD
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- **Speed**: TBD for 1024x1024 generation
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## π Quick Start
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### Automatic (Recommended)
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```bash
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# ImageAI server downloads automatically
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curl -X POST "http://localhost:8001/generate" \
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-H "Content-Type: application/json" \
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-d '{
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"prompt": "a beautiful landscape",
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"model": "flux1-tensorrt_rtx:schnell",
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"width": 1024,
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"height": 1024
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}'
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```
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### Manual Download
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```python
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from huggingface_hub import snapshot_download
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# Download this specific variant only
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engines_path = snapshot_download(
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repo_id="imgailab/flux1-trtx-schnell-fp8-blackwell"
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)
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# Engines are in: engines_path/engines/*.plan
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```
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### Direct Integration
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```python
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from imageai_server.tensorrt.nvidia_sdxl_pipeline import NVIDIASDXLPipeline
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pipeline = NVIDIASDXLPipeline()
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pipeline.load_engines(
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engine_dir=f"{engines_path}/engines",
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framework_model_dir=f"{engines_path}/framework",
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onnx_dir=f"{engines_path}/onnx"
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)
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pipeline.activate_engines()
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images, time_ms = pipeline.infer(
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prompt="a serene mountain landscape",
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height=1024,
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width=1024
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)
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```
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## π Performance
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| Metric | Value |
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|--------|-------|
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| **Memory Usage** | TBD |
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| **Inference Speed** | TBD |
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| **Resolution** | 1024x1024 (optimized) |
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| **Batch Size** | 1 (optimized) |
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| **Precision** | SCHNELL |
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## π§ Requirements
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### Hardware
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- **GPU**: Fp8 architecture
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- Ampere: RTX 3090, A100, etc.
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- Ada Lovelace: RTX 4090, etc.
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- Blackwell: H200, etc.
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- **VRAM**: TBD minimum
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- **Compute Capability**: 8.0+
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### Software
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- **TensorRT-RTX**: 1.0.0.21+
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- **CUDA**: 12.0+
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- **Python**: 3.8+
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## π Repository Structure
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```
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flux1-trtx-schnell-fp8-blackwell/
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βββ engines/ # TensorRT engine files
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β βββ *.plan # Optimized engines
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βββ config.json # Configuration metadata
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βββ README.md # This file
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```
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## π Related Repositories
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Other variants for FLUX1:
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- [Ampere BF16](https://huggingface.co/imgailab/flux1-trtx-bf16-ampere)\n- [Ada FP8](https://huggingface.co/imgailab/flux1-trtx-fp8-ada)\n- [Ada BF16](https://huggingface.co/imgailab/flux1-trtx-bf16-ada)\n- [Blackwell FP4](https://huggingface.co/imgailab/flux1-trtx-fp4-blackwell)\n- [Blackwell FP8](https://huggingface.co/imgailab/flux1-trtx-fp8-blackwell)\n- [Blackwell BF16](https://huggingface.co/imgailab/flux1-trtx-bf16-blackwell)\n
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## π License
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Inherits license from base model: [black-forest-labs/FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
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## π Updates
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- **2025-08-12**: Initial release
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- Optimized for single-variant downloads
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---
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*Part of the ImageAI TensorRT-RTX engine collection*
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