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Fix the bug that joint_attention_kwargs is not passed to the FLUX's transformer attention processors #9517

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merged 9 commits into from
Oct 8, 2024
Merged

Fix the bug that joint_attention_kwargs is not passed to the FLUX's transformer attention processors #9517

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8a1d136
Update transformer_flux.py
HorizonWind2004 Sep 24, 2024
b12ee92
Update transformer_flux.py
HorizonWind2004 Sep 24, 2024
9509764
Update transformer_flux.py
HorizonWind2004 Sep 24, 2024
a89fd51
Merge branch 'main' into main
HorizonWind2004 Sep 25, 2024
5104098
Update transformer_flux.py
HorizonWind2004 Sep 26, 2024
8b0be8d
Merge branch 'main' into main
HorizonWind2004 Sep 26, 2024
9ed5284
Update transformer_flux.py
HorizonWind2004 Oct 8, 2024
c9d624b
Merge branch 'main' into main
HorizonWind2004 Oct 8, 2024
b6128a4
fix the quality
HorizonWind2004 Oct 8, 2024
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8 changes: 7 additions & 1 deletion src/diffusers/models/transformers/transformer_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,11 +83,12 @@ def forward(
hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
image_rotary_emb=None,
joint_attention_kwargs=None,
):
residual = hidden_states
norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))

joint_attention_kwargs = joint_attention_kwargs if joint_attention_kwargs is not None else {}
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should we pass this to attn too?

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Yes! I think it will be useful for other trial!

attn_output = self.attn(
hidden_states=norm_hidden_states,
image_rotary_emb=image_rotary_emb,
Expand Down Expand Up @@ -161,6 +162,7 @@ def forward(
encoder_hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
image_rotary_emb=None,
joint_attention_kwargs={},
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can you explain what additional argument you need to pass down to flux attention processor?

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Thank you for your recognition!

In our work, I am trying to integrate box and mask into the FLUX model and implement layout control (similar to what has been done in many works on SD1.4). This requires modifying the attention processor. I believe that the architecture of FLUX and other transformers can also be used to develop better layout control algorithms, so I believe these modifications will contribute to future training-free experiments on FLUX.

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Suggested change
joint_attention_kwargs={},
joint_attention_kwargs=None,

):
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)

Expand All @@ -173,6 +175,8 @@ def forward(
hidden_states=norm_hidden_states,
encoder_hidden_states=norm_encoder_hidden_states,
image_rotary_emb=image_rotary_emb,
**joint_attention_kwargs,

)

# Process attention outputs for the `hidden_states`.
Expand Down Expand Up @@ -497,6 +501,7 @@ def custom_forward(*inputs):
encoder_hidden_states=encoder_hidden_states,
temb=temb,
image_rotary_emb=image_rotary_emb,
joint_attention_kwargs=joint_attention_kwargs,
)

# controlnet residual
Expand Down Expand Up @@ -533,6 +538,7 @@ def custom_forward(*inputs):
hidden_states=hidden_states,
temb=temb,
image_rotary_emb=image_rotary_emb,
joint_attention_kwargs=joint_attention_kwargs,
)

# controlnet residual
Expand Down