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import change from Diffusers upstream for Chroma masking fixes #1844

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merged 1 commit into from
Oct 26, 2025
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import change from Diffusers upstream for Chroma masking fixes #1844

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28 changes: 20 additions & 8 deletions simpletuner/helpers/models/chroma/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -841,19 +841,23 @@ def _get_t5_prompt_embeds(
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
attention_mask = text_inputs.attention_mask.clone()

seq_lengths = attention_mask.sum(dim=1)
mask_indices = torch.arange(attention_mask.size(1)).unsqueeze(0).expand(batch_size, -1)
attention_mask = (mask_indices <= seq_lengths.unsqueeze(1)).bool()
tokenizer_mask = text_inputs.attention_mask
tokenizer_mask_device = tokenizer_mask.to(device)

# unlike Flux, Chroma uses the tokenizer's attention mask when generating the T5 embeddings
prompt_embeds = self.text_encoder(
text_input_ids.to(device), output_hidden_states=False, attention_mask=attention_mask.to(device)
text_input_ids.to(device),
output_hidden_states=False,
attention_mask=tokenizer_mask_device,
)[0]

dtype = self.text_encoder.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
attention_mask = attention_mask.to(device=device)

# for the text tokens, Chroma requires that all except the first padding token are masked out
seq_lengths = tokenizer_mask_device.sum(dim=1)
mask_indices = torch.arange(tokenizer_mask_device.size(1), device=device).unsqueeze(0).expand(batch_size, -1)
attention_mask = (mask_indices <= seq_lengths.unsqueeze(1)).to(dtype=dtype, device=device)

_, seq_len, _ = prompt_embeds.shape

Expand Down Expand Up @@ -1154,7 +1158,15 @@ def _prepare_attention_mask(
return attention_mask

attention_mask = torch.cat(
[attention_mask, torch.ones(batch_size, sequence_length, device=attention_mask.device, dtype=torch.bool)],
[
attention_mask,
torch.ones(
batch_size,
sequence_length,
device=attention_mask.device,
dtype=attention_mask.dtype,
),
],
dim=1,
)

Expand Down