Add after_loop callback to in context generation
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@ -114,7 +114,7 @@ class Flux1InContextLoRA(nn.Module):
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static_noise=static_noise,
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)
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# (Optional) Call subscribers at end of loop
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# (Optional) Call subscribers in-loop
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Callbacks.in_loop(
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t=t,
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seed=seed,
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@ -137,6 +137,14 @@ class Flux1InContextLoRA(nn.Module):
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time_steps=time_steps,
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)
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# (Optional) Call subscribers after loop
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Callbacks.after_loop(
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seed=seed,
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prompt=prompt,
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latents=latents,
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config=config,
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) # fmt: off
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# 6. Decode the latent array and return the image
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latents = ArrayUtil.unpack_latents(latents=latents, height=config.height, width=config.width)
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decoded = self.vae.decode(latents)
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@ -119,7 +119,7 @@ class Flux1Controlnet(nn.Module):
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dt = config.sigmas[t + 1] - config.sigmas[t]
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latents += noise * dt
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# (Optional) Call subscribers at end of loop
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# (Optional) Call subscribers in-loop
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Callbacks.in_loop(
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t=t,
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seed=seed,
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@ -142,7 +142,7 @@ class Flux1Controlnet(nn.Module):
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time_steps=time_steps,
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)
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# (Optional) Call subscribers at end of loop
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# (Optional) Call subscribers after loop
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Callbacks.after_loop(
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seed=seed,
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prompt=prompt,
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@ -99,7 +99,7 @@ class Flux1(nn.Module):
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dt = config.sigmas[t + 1] - config.sigmas[t]
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latents += noise * dt
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# (Optional) Call subscribers at end of loop
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# (Optional) Call subscribers in-loop
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Callbacks.in_loop(
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t=t,
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seed=seed,
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@ -122,7 +122,7 @@ class Flux1(nn.Module):
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time_steps=time_steps,
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)
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# (Optional) Call subscribers at end of loop
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# (Optional) Call subscribers after loop
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Callbacks.after_loop(
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seed=seed,
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prompt=prompt,
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