Image-to-Image
Transformers
Diffusers
Safetensors
unidflow
multimodal
image-generation
masked-diffusion
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-to-image", model="onkarsus13/unidflow-base-7B")# Load model directly
from transformers import UniDFlowForMultiModalGeneration
model = UniDFlowForMultiModalGeneration.from_pretrained("onkarsus13/unidflow-base-7B", device_map="auto")Quick Links
Refined UniDFlow
This repository contains the released UniDFlow weights, tokenizer, and VQ-VAE
prepared for the modular unidflow Python package.
from unidflow import UniDFlowPipeline
pipe = UniDFlowPipeline.from_pretrained("onkarsus13/unidflow-base-7B")
result = pipe(
"image-to-image",
image="input.jpg",
prompt="Turn this photograph into a watercolor painting",
edit_type="edit",
seed=42,
)
result.save("edited.png")
UniDFlow is a bidirectional masked-diffusion model. Use UniDFlowPipeline rather
than the autoregressive transformers.generate() method. Preserve the upstream
UniDFlow citation and notices when redistributing this checkpoint.
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