---
license: apache-2.0
tags:
  - flux
  - flux1
  - flux2
  - diffusers
  - quanto
  - quantized
  - mps-compatible
  - apple-silicon
library_name: diffusers
pipeline_tag: text-to-image
---

# flux_qint_8bit

Pre-quantized **FLUX** models using [optimum-quanto](https://github.com/huggingface/optimum-quanto) for memory-efficient inference on consumer hardware.

## Summary

| Metric | Value |
|--------|-------|
| **Total Models** | 8 |
| **Total Size** | 146.3 GB |
| **Quantization** | qint8 (8-bit integer) |
| **Platforms** | MPS, CUDA, CPU |

## Available Quantizations

| Model | Transformer | Text Encoder | Path |
|-------|-------------|--------------|------|
| FLUX.1 Canny [dev] | ✅ 11.09 GB | ✅ 4.56 GB | `flux-1-canny-dev/` |
| FLUX.1 Depth [dev] | ✅ 11.09 GB | ✅ 4.56 GB | `flux-1-depth-dev/` |
| FLUX.1 Fill [dev] | ✅ 11.09 GB | ✅ 4.56 GB | `flux-1-fill-dev/` |
| FLUX.1 Kontext [dev] | ✅ 11.09 GB | ✅ 4.56 GB | `flux-1-kontext-dev/` |
| FLUX.1 [dev] | ✅ 11.09 GB | ✅ 4.56 GB | `flux-1-dev/` |
| FLUX.1 [schnell] | ✅ 11.08 GB | ✅ 4.56 GB | `flux-1-schnell/` |
| FLUX.2 [dev] | ✅ 30.02 GB | ✅ 22.37 GB | `flux-2-dev/` |

---

## Model Details

### FLUX.1 Canny [dev]

**Source:** [black-forest-labs/FLUX.1-Canny-dev](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev)

**Pipeline:** `FluxControlPipeline`

**Use case:** 12B canny edge-guided generation model

| Component | Params | Size | Path |
|-----------|--------|------|------|
| Transformer | 12.0B | 11.09 GB | `flux-1-canny-dev/transformer/qint8` |
| Text Encoder (T5-XXL) | 4.7B | 4.56 GB | `flux-1-canny-dev/text_encoder/qint8` |

### FLUX.1 Depth [dev]

**Source:** [black-forest-labs/FLUX.1-Depth-dev](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev)

**Pipeline:** `FluxControlPipeline`

**Use case:** 12B depth-guided generation model

| Component | Params | Size | Path |
|-----------|--------|------|------|
| Transformer | 12.0B | 11.09 GB | `flux-1-depth-dev/transformer/qint8` |
| Text Encoder (T5-XXL) | 4.7B | 4.56 GB | `flux-1-depth-dev/text_encoder/qint8` |

### FLUX.1 Fill [dev]

**Source:** [black-forest-labs/FLUX.1-Fill-dev](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev)

**Pipeline:** `FluxFillPipeline`

**Use case:** 12B inpainting/outpainting model

| Component | Params | Size | Path |
|-----------|--------|------|------|
| Transformer | 12.0B | 11.09 GB | `flux-1-fill-dev/transformer/qint8` |
| Text Encoder (T5-XXL) | 4.7B | 4.56 GB | `flux-1-fill-dev/text_encoder/qint8` |

### FLUX.1 Kontext [dev]

**Source:** [black-forest-labs/FLUX.1-Kontext-dev](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev)

**Pipeline:** `FluxKontextPipeline`

**Use case:** 12B image editing model (in-context generation)

| Component | Params | Size | Path |
|-----------|--------|------|------|
| Transformer | 12.0B | 11.09 GB | `flux-1-kontext-dev/transformer/qint8` |
| Text Encoder (T5-XXL) | 4.7B | 4.56 GB | `flux-1-kontext-dev/text_encoder/qint8` |

### FLUX.1 [dev]

**Source:** [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)

**Pipeline:** `FluxPipeline`

**Use case:** 12B high-quality generation model (guidance distilled)

| Component | Params | Size | Path |
|-----------|--------|------|------|
| Transformer | 12.0B | 11.09 GB | `flux-1-dev/transformer/qint8` |
| Text Encoder (T5-XXL) | 4.7B | 4.56 GB | `flux-1-dev/text_encoder/qint8` |

### FLUX.1 [schnell]

**Source:** [black-forest-labs/FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell)

**Pipeline:** `FluxPipeline`

**Use case:** 12B fast 4-step generation model (Apache 2.0 license)

| Component | Params | Size | Path |
|-----------|--------|------|------|
| Transformer | 12.0B | 11.08 GB | `flux-1-schnell/transformer/qint8` |
| Text Encoder (T5-XXL) | 4.7B | 4.56 GB | `flux-1-schnell/text_encoder/qint8` |

### FLUX.2 [dev]

**Source:** [black-forest-labs/FLUX.2-dev](https://huggingface.co/black-forest-labs/FLUX.2-dev)

**Pipeline:** `Flux2Pipeline`

**Use case:** 32B unified multi-modal model (text-to-image, inpainting, depth, canny, etc.)

| Component | Params | Size | Path |
|-----------|--------|------|------|
| Transformer | 32.0B | 30.02 GB | `flux-2-dev/transformer/qint8` |
| Text Encoder (Mistral) | 24.0B | 22.37 GB | `flux-2-dev/text_encoder/qint8` |


---

## Usage Examples

### FLUX.1 Models (text-to-image, inpainting, depth, canny, etc.)

```python
from diffusers import FluxPipeline  # or FluxFillPipeline, FluxControlPipeline, etc.
from diffusers.models import FluxTransformer2DModel
from transformers import T5EncoderModel
from optimum.quanto import QuantizedDiffusersModel, QuantizedTransformersModel
from huggingface_hub import snapshot_download
import torch

REPO_ID = "VincentGOURBIN/flux_qint_8bit"
quant_path = snapshot_download(REPO_ID)

# Quantized model classes for FLUX.1
class QuantizedFluxTransformer2DModel(QuantizedDiffusersModel):
    base_class = FluxTransformer2DModel

class QuantizedT5EncoderModel(QuantizedTransformersModel):
    auto_class = T5EncoderModel

# Example: Load FLUX.1-dev with quantized transformer
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    transformer=None,
    torch_dtype=torch.bfloat16
)

transformer = QuantizedFluxTransformer2DModel.from_pretrained(
    f"{quant_path}/flux-1-dev/transformer/qint8"
)
pipe.transformer = transformer.to("mps")  # or "cuda"

# Optional: Load quantized T5 text encoder (saves ~9GB)
# text_encoder_2 = QuantizedT5EncoderModel.from_pretrained(
#     f"{quant_path}/flux-1-dev/text_encoder/qint8"
# )
# pipe.text_encoder_2 = text_encoder_2.to("mps")

image = pipe("A majestic mountain at sunset", num_inference_steps=28).images[0]
image.save("output.png")
```
### FLUX.2 Models (unified multi-modal)

```python
from diffusers import Flux2Pipeline
from diffusers.models import Flux2Transformer2DModel
from transformers import AutoModel
from optimum.quanto import QuantizedDiffusersModel, QuantizedTransformersModel
from huggingface_hub import snapshot_download
import torch

REPO_ID = "VincentGOURBIN/flux_qint_8bit"
quant_path = snapshot_download(REPO_ID)

# Quantized model classes for FLUX.2
class QuantizedFlux2Transformer2DModel(QuantizedDiffusersModel):
    base_class = Flux2Transformer2DModel

class QuantizedFlux2TextEncoder(QuantizedTransformersModel):
    auto_class = AutoModel

# Load FLUX.2-dev with quantized transformer
pipe = Flux2Pipeline.from_pretrained(
    "black-forest-labs/FLUX.2-dev",
    transformer=None,
    torch_dtype=torch.bfloat16
)

transformer = QuantizedFlux2Transformer2DModel.from_pretrained(
    f"{quant_path}/flux-2-dev/transformer/qint8"
)
pipe.transformer = transformer.to("mps")  # or "cuda"

# Optional: Load quantized Mistral text encoder (saves ~36GB)
# text_encoder = QuantizedFlux2TextEncoder.from_pretrained(
#     f"{quant_path}/flux-2-dev/text_encoder/qint8"
# )
# pipe.text_encoder = text_encoder.to("mps")

image = pipe("A beautiful landscape", num_inference_steps=28, guidance_scale=4.0).images[0]
image.save("output.png")
```

---

## Memory Requirements

| Model Family | Transformer qint8 | Text Encoder qint8 | Total | RAM to Quantize |
|--------------|-------------------|--------------------| ------|-----------------|
| **FLUX.2** | ~30 GB | ~22 GB | ~52 GB | ~64 GB |
| **FLUX.1** | ~11 GB | ~4.4 GB | ~15 GB | ~24 GB |

## Compatibility

| Platform | Status | Notes |
|----------|--------|-------|
| MPS (Apple Silicon) | ✅ Fully supported | Best for M1/M2/M3 Macs |
| CUDA (NVIDIA) | ✅ Fully supported | RTX 3090+ recommended |
| CPU | ⚠️ Slow | Not recommended for production |

## Installation

```bash
pip install diffusers transformers accelerate safetensors
pip install optimum[quanto]
pip install huggingface_hub
```

## Important Notes

- **VAE is NOT quantized** - Quantizing VAE causes visual artifacts
- **LoRA compatible** - Quantized models work with LoRA adapters (unlike GGUF)
- **Text encoders are optional** - Transformer-only quantization saves significant memory while the text encoder runs in bfloat16

## File Structure

```
flux_qint_8bit/
├── flux-2-dev/           # FLUX.2 models (if present)
│   ├── transformer/
│   │   └── qint8/
│   └── text_encoder/
│       └── qint8/
├── flux-1-dev/           # FLUX.1 models
│   ├── transformer/
│   │   └── qint8/
│   └── text_encoder/
│       └── qint8/
├── flux-1-schnell/       # Fast model
│   └── ...
└── README.md
```

## Generated With

[flux-quantizer](https://github.com/...) - Gradio tool for batch quantizing and publishing FLUX models.

---
*Last updated: 2025-12-23 18:18 UTC*