Native LongCat-Image implementation (#12597)
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@@ -925,6 +925,25 @@ class Flux(BaseModel):
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out['ref_latents'] = list([1, 16, sum(map(lambda a: math.prod(a.size()[2:]), ref_latents))])
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return out
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class LongCatImage(Flux):
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def _apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
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transformer_options = transformer_options.copy()
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rope_opts = transformer_options.get("rope_options", {})
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rope_opts = dict(rope_opts)
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rope_opts.setdefault("shift_t", 1.0)
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rope_opts.setdefault("shift_y", 512.0)
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rope_opts.setdefault("shift_x", 512.0)
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transformer_options["rope_options"] = rope_opts
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return super()._apply_model(x, t, c_concat, c_crossattn, control, transformer_options, **kwargs)
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def encode_adm(self, **kwargs):
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return None
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def extra_conds(self, **kwargs):
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out = super().extra_conds(**kwargs)
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out.pop('guidance', None)
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return out
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class Flux2(Flux):
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def extra_conds(self, **kwargs):
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out = super().extra_conds(**kwargs)
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