digital-garbage
1e2ac5ca67
Learned tile-sparse attention (Veda, arXiv 2605.30325): tiles, tile plans, predictor scoring and selection in plain torch (tested on CPU, including a real tiny H3 with a stand-in kernel), an INT8 Triton kernel derived from SageAttention (untested on a GPU), installed through the same override and per-step wrapper as SLA. Runs at the predictor's trained budget; LoRA, MXFP8, Two stages, dense steps and the attention backend compose. Settings > Models offers the official Hugging Face download of the predictor on a click, with its licence beside it; taggers and this share one downloader (core/fetch.py).
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
"""Diffusion model loader. A node-only module."""
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from .nodes import FunUIDiffusionModelLoader
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ID = "loader_diffusion_model"
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TITLE = "Diffusion model loader"
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STAGE = "load"
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CATEGORY = "system"
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STATUS = "proven"
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NODES = [FunUIDiffusionModelLoader]
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def routes(table, base, web):
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@table.get(base + "/torch")
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async def _torch(_req):
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"""Why int8 would run slow on this torch build (None when it would not), for the chip beside Generate."""
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from ..common import slow_int8_build
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return web.json_response({"slow_int8": slow_int8_build()})
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@table.get(base + "/veda")
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async def _veda(_req):
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"""The Veda predictor files that are in models/veda, the two official downloads with their state, and the licence."""
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from .. import veda_download
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return web.json_response(veda_download.listing())
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@table.post(base + "/veda/download")
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async def _veda_download(req):
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"""Fetch one official predictor straight from Hugging Face: {"id": "t2va" | "r2va"}. Only ever started by a click."""
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from .. import veda_download
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try:
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body = await req.json()
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except ValueError:
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body = None
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started = veda_download.download(str(body.get("id") or "") if isinstance(body, dict) else "")
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return web.json_response({"started": started})
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PROVIDES = {"routes": routes}
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