import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("valentinospariza/ovb", dtype=torch.bfloat16, device_map="cuda")
prompt = "Turn this cat into a dog"
input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
image = pipe(image=input_image, prompt=prompt).images[0]OVB FLUX.2-dev checkpoint 500
This repository contains the transformer weights from optimizer step 500 of
the OVB FLUX.2-dev fine-tuning run
flux2_dev_v2_no_masks_32node_global512_6250_s42.
This is an early intermediate research checkpoint, not the final trained model. The run used 32 H200 nodes, effective global batch 512, seed 42, and disabled training validity masks. Only the fine-tuned transformer is included; the VAE, text encoders, and other base-pipeline components are not duplicated.
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Access to the gated black-forest-labs/FLUX.2-dev base model is required.
import torch
from diffusers import Flux2Pipeline, Flux2Transformer2DModel
transformer = Flux2Transformer2DModel.from_pretrained(
"valentinospariza/ovb",
torch_dtype=torch.bfloat16,
)
pipe = Flux2Pipeline.from_pretrained(
"black-forest-labs/FLUX.2-dev",
transformer=transformer,
torch_dtype=torch.bfloat16,
)
License
These weights are a derivative of black-forest-labs/FLUX.2-dev and are
provided under the
FLUX Non-Commercial License.
Users are responsible for complying with that license and the base model's
acceptable-use requirements.
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