---

license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-Krea-dev/blob/main/LICENSE.md language:

• en

library_name: diffusers base_model:

• black-forest-labs/FLUX.1-Krea-dev

pipeline_tag: text-to-image widget:

• text: a frog holding a sign that says hello world

output: url: output1.png

• text: a pig holding a sign that says hello world

output: url: output2.png

• text: a wolf holding a sign that says hello world

output: url: output3.png

• text: >-

cute anime girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron mouth open holding a fancy black forest cake with candles on top in the kitchen of an old dark Victorian mansion lit by candlelight with a bright window to the foggy forest and very expensive stuff everywhere output: url: workflow-embedded-demo1.png

• text: >-

on a rainy night, a girl holds an umbrella and looks at the camera. The rain keeps falling. output: url: workflow-embedded-demo2.png

• text: drone shot of a volcano erupting with a pig walking on it

output: url: workflow-embedded-demo3.png tags:

• gguf-node
• gguf-connector

---

gguf quantized version of krea

• run it straight with gguf-connector
• opt a gguf file in the current directory to interact with by:

ggc k

> >GGUF file(s) available. Select which one to use: > >1. flux-krea-lite-q2_k.gguf >2. flux-krea-lite-q4_0.gguf >3. flux-krea-lite-q8_0.gguf > >Enter your choice (1 to 3): _ > note: try experimental lite model with 8-step operation; save up to 70% loading time

!screenshot

• run it with diffusers (see example inference below)

import torch
from transformers import T5EncoderModel
from diffusers import FluxPipeline, GGUFQuantizationConfig, FluxTransformer2DModel

modelpath = "https://huggingface.co/calcuis/krea-gguf/blob/main/flux1-krea-dev-q2k.gguf"
transformer = FluxTransformer2DModel.fromsinglefile(
    model_path,
    quantizationconfig=GGUFQuantizationConfig(computedtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
    config="callgg/krea-decoder",
    subfolder="transformer"
)

textencoder = T5EncoderModel.frompretrained(
    "chatpig/t5-v1_1-xxl-encoder-fp32-gguf",
    gguffile="t5xxl-encoder-fp32-q2k.gguf",
    torch_dtype=torch.bfloat16
    )

pipe = FluxPipeline.from_pretrained(
    "callgg/krea-decoder",
    transformer=transformer,
    textencoder2=text_encoder,
    torch_dtype=torch.bfloat16
    )
pipe.enablemodelcpu_offload() # could change it to cuda if you have good gpu

prompt = "a pig holding a sign that says hello world"
image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=2.5,
).images[0]
image.save("output.png")

run it with gguf-node via comfyui

• drag krea to > ./ComfyUI/models/diffusionmodels
• drag clip-l-v2 [248MB], t5xxl [2.75GB] to > ./ComfyUI/models/textencoders
• drag pig [168MB] to > ./ComfyUI/models/vae

!screenshot

reference

• base model from black-forest-labs
• for model merge details, see sayakpaul
• diffusers from huggingface
• comfyui from comfyanonymous
• gguf-node (pypi|repo|pack)
• gguf-connector (pypi)