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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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11 changes: 9 additions & 2 deletions src/diffusers/models/transformers/transformer_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,14 +83,16 @@ 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 or {}
attn_output = self.attn(
hidden_states=norm_hidden_states,
image_rotary_emb=image_rotary_emb,
**joint_attention_kwargs
)

hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
Expand Down Expand Up @@ -161,18 +163,21 @@ def forward(
encoder_hidden_states: torch.FloatTensor,
temb: torch.FloatTensor,
image_rotary_emb=None,
joint_attention_kwargs=None,
):
norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)

norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
encoder_hidden_states, emb=temb
)

joint_attention_kwargs = joint_attention_kwargs or {}
# Attention.
attn_output, context_attn_output = self.attn(
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 +502,7 @@ 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 +539,7 @@ 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
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