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gpustack/FLUX.1-dev-GGUF
FLUX.1-dev-GGUF is a text-to-image model from gpustack. Use it when you need an image from a text prompt. The card lists the license as other.
!!! Experimental supported by gpustack/llama-box v0.0.77+ only !!!
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From the Hugging Face model README
!!! Experimental supported by gpustack/llama-box v0.0.77+ only !!!
Model creator: Black Forest Labs<br/> Original model: FLUX.1-dev<br/> GGUF quantization: based on stable-diffusion.cpp ac54e that patched by llama-box.
| Quantization | OpenAI CLIP ViT-L/14 Quantization | Google T5-xxl Quantization | VAE Quantization |
|---|---|---|---|
| FP16 | FP16 | FP16 | FP16 |
| Q8_0 | FP16 | Q8_0 | FP16 |
| (pure) Q8_0 | Q8_0 | Q8_0 | FP16 |
| Q4_1 | FP16 | Q8_0 | FP16 |
| Q4_0 | FP16 | Q8_0 | FP16 |
| (pure) Q4_0 | Q4_0 | Q4_0 | FP16 |
![FLUX.1 [dev] Grid](https://huggingface.co/gpustack/FLUX.1-dev-GGUF/resolve/main/dev_grid.jpg)
FLUX.1 [dev] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions.
For more information, please read our blog post.
FLUX.1 [pro].FLUX.1 [dev] more efficient.FLUX.1 [dev] Non-Commercial License.We provide a reference implementation of FLUX.1 [dev], as well as sampling code, in a dedicated github repository.
Developers and creatives looking to build on top of FLUX.1 [dev] are encouraged to use this as a starting point.
The FLUX.1 models are also available via API from the following sources
FLUX.1 [pro])FLUX.1 [dev] is also available in Comfy UI for local inference with a node-based workflow.
To use FLUX.1 [dev] with the 🧨 diffusers python library, first install or upgrade diffusers
pip install -U diffusers
Then you can use FluxPipeline to run the model
import torch
from diffusers import FluxPipeline
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=3.5,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-dev.png")
To learn more check out the diffusers documentation
The model and its derivatives may not be used
This model falls under the FLUX.1 [dev] Non-Commercial License.