Native LongCat-Image implementation (#12597)

This commit is contained in:
Talmaj
2026-02-28 05:04:34 +01:00
committed by GitHub
parent 94f1a1cc9d
commit ac4412d0fa
7 changed files with 356 additions and 2 deletions
+19
View File
@@ -925,6 +925,25 @@ class Flux(BaseModel):
out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
return out
class LongCatImage(Flux):
def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
transformer_options = transformer_options.copy()
rope_opts = transformer_options.get("rope_options", {})
rope_opts = dict(rope_opts)
rope_opts.setdefault("shift_t", 1.0)
rope_opts.setdefault("shift_y", 512.0)
rope_opts.setdefault("shift_x", 512.0)
transformer_options["rope_options"] = rope_opts
return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
def encode_adm(self, **kwargs):
return None
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
out.pop('guidance', None)
return out
class Flux2(Flux):
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)