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salma-remyx

28 24 494

https://remyx.ai

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remyxai/stitch-flux-modular

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remyxai/story-diffusion-flux-modular

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remyxai/regional-prompting-flux-modular

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Post

2409

New diffusion methods can take months or years to reach diffusers.

Until then, trying them means working through fragmented research repos with different dependencies, APIs, and implementation choices. Maintainers also can’t review and maintain every promising method in the main repo.

We’re sharing flux-modular-diffusers as a shared test bed for new FLUX methods. https://github.com/remyxai/flux-modular-diffusers
Each one is adapted to the same ModularPipeline interface, validated with a GPU notebook, and published as its own Hub artifact.

@sayakpaul suggested this path on our HRDiT PR. We added Outrider, our open-source GitHub Action that discovers relevant methods and implements them as draft PRs.

Outrider drafted each pipeline against the repo’s shared conventions. We reviewed the implementations, ran the GPU validation, and published the ones that passed.

This helped us quickly grow the test bed to 10 pipelines covering:
* High-res + panoramic generation: HRDiT, DyPE, Panorama
* Identity + virtual try-on: PuLID, CatVTON
* Image editing + regional control: FlowEdit, KV-Edit, Regional Prompting
* Style + character consistency: StyleAligned, StoryDiffusion

Users get new methods sooner and a consistent way to test them. Maintainers can review the implementation and results without taking on every method in core.

Resources:
🧩 Modular Diffusers: https://huggingface.co/blog/modular-diffusers
🧪 FLUX test bed: https://github.com/remyxai/flux-modular-diffusers
📚 FLUX Modular Diffusers collection: https://huggingface.co/collections/remyxai/flux-modular-diffusers
🤖 Outrider: https://github.com/remyxai/outrider

Post

2337

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:

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]