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We just published two training-free 🧩 Modular Diffusers community pipelines that push off-the-shelf FLUX to 4096².
No fine-tuning, no extra weights, no upscaler.
The methods take different routes around positional extrapolation:
✅ **HRDiT:** resolution ladder + NTK RoPE + spatial-position alignment + structure guidance
✅ **DyPE:** single-pass, timestep-dynamic RoPE (κ=t²), plus an optional SEGA spectral mode that removes high-frequency speckle
Because both expose the same block interface, you can A/B the two methods on the same FLUX.1-Krea-dev weights by swapping the repo ID:
Both implementations address open
Check out the collection! https://huggingface.co/collections/remyxai/training-free-high-resolution-generation-with-flux
No fine-tuning, no extra weights, no upscaler.
The methods take different routes around positional extrapolation:
✅ **HRDiT:** resolution ladder + NTK RoPE + spatial-position alignment + structure guidance
✅ **DyPE:** single-pass, timestep-dynamic RoPE (κ=t²), plus an optional SEGA spectral mode that removes high-frequency speckle
Because both expose the same block interface, you can A/B the two methods on the same FLUX.1-Krea-dev weights by swapping the repo ID:
import torch
from diffusers import ModularPipeline
repo_id = "remyxai/dype-flux-modular"
# or "remyxai/hrdit-flux-modular"
pipe = ModularPipeline.from_pretrained(
repo_id,
trust_remote_code=True,
)
pipe.load_components(dtype=torch.bfloat16)
pipe.to("cuda")
img = pipe(
prompt="...",
height=4096,
width=4096,
).images[0]Both implementations address open
diffusers requests (#14480, #14520).Check out the collection! https://huggingface.co/collections/remyxai/training-free-high-resolution-generation-with-flux
