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Bugfix for flux2 img2img2 prediction #12855

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Merged
sayakpaul merged 7 commits into from
Jan 12, 2026
Merged

Bugfix for flux2 img2img2 prediction #12855

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e1cf6a3
Bugfix for dreambooth flux2 img2img2
leisuzz Dec 18, 2025
e3556db
Bugfix for dreambooth flux2 img2img2
leisuzz Dec 18, 2025
79f704c
Bugfix for dreambooth flux2 img2img2
leisuzz Dec 18, 2025
e5229da
Bugfix for dreambooth flux2 img2img2
leisuzz Dec 18, 2025
86da067
Bugfix for dreambooth flux2 img2img2
leisuzz Dec 18, 2025
be5985b
Bugfix for dreambooth flux2 img2img2
leisuzz Jan 12, 2026
abee6cf
Merge branch 'main' into m2m
leisuzz Jan 12, 2026
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12 changes: 10 additions & 2 deletions examples/dreambooth/train_dreambooth_lora_flux2_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1695,9 +1695,13 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
cond_model_input = (cond_model_input - latents_bn_mean) / latents_bn_std

model_input_ids = Flux2Pipeline._prepare_latent_ids(model_input).to(device=model_input.device)
cond_model_input_ids = Flux2Pipeline._prepare_image_ids(cond_model_input).to(
cond_model_input_list = [cond_model_input[i].unsqueeze(0) for i in range(cond_model_input.shape[0])]
cond_model_input_ids = Flux2Pipeline._prepare_image_ids(cond_model_input_list).to(
device=cond_model_input.device
)
cond_model_input_ids = cond_model_input_ids.view(
cond_model_input.shape[0], -1, model_input_ids.shape[-1]
)

# Sample noise that we'll add to the latents
noise = torch.randn_like(model_input)
Expand All @@ -1724,6 +1728,9 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
packed_noisy_model_input = Flux2Pipeline._pack_latents(noisy_model_input)
packed_cond_model_input = Flux2Pipeline._pack_latents(cond_model_input)

orig_input_shape = packed_noisy_model_input.shape
orig_input_ids_shape = model_input_ids.shape

# concatenate the model inputs with the cond inputs
packed_noisy_model_input = torch.cat([packed_noisy_model_input, packed_cond_model_input], dim=1)
model_input_ids = torch.cat([model_input_ids, cond_model_input_ids], dim=1)
Expand All @@ -1742,7 +1749,8 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
img_ids=model_input_ids, # B, image_seq_len, 4
return_dict=False,
)[0]
model_pred = model_pred[:, : packed_noisy_model_input.size(1) :]
model_pred = model_pred[:, : orig_input_shape[1], :]
model_input_ids = model_input_ids[:, : orig_input_ids_shape[1], :]

model_pred = Flux2Pipeline._unpack_latents_with_ids(model_pred, model_input_ids)

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