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Add missing context settings #12833

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185ed9a
add context for src pipelines
omkar-334 Dec 12, 2025
b67ba2c
add context for examples
omkar-334 Dec 12, 2025
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17 changes: 9 additions & 8 deletions examples/community/cogvideox_ddim_inversion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -522,14 +522,15 @@ def sample(
timestep = t.expand(latent_model_input.shape[0])

# predict noise model_output
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
image_rotary_emb=image_rotary_emb,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
image_rotary_emb=image_rotary_emb,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_pred.float()

if reference_latents is not None: # Recover the original batch size
Expand Down
23 changes: 12 additions & 11 deletions examples/community/pipeline_flux_differential_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -954,17 +954,18 @@ def __call__(

# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]

# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
Expand Down
42 changes: 22 additions & 20 deletions examples/community/pipeline_flux_kontext_multiple_images.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1150,33 +1150,35 @@ def __call__(
latent_model_input = torch.cat([latents, image_latents], dim=1)
timestep = t.expand(latents.shape[0]).to(latents.dtype)

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_pred[:, : latents.size(1)]

if do_true_cfg:
if negative_image_embeds is not None:
self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
neg_noise_pred = self.transformer(
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=negative_pooled_prompt_embeds,
encoder_hidden_states=negative_prompt_embeds,
txt_ids=negative_text_ids,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_pred[:, : latents.size(1)]

if do_true_cfg:
if negative_image_embeds is not None:
self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
with self.transformer.cache_context("uncond"):
neg_noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=negative_pooled_prompt_embeds,
encoder_hidden_states=negative_prompt_embeds,
txt_ids=negative_text_ids,
img_ids=latent_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
neg_noise_pred = neg_noise_pred[:, : latents.size(1)]
noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)

Expand Down
46 changes: 24 additions & 22 deletions examples/community/pipeline_flux_rf_inversion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -888,17 +888,18 @@ def __call__(
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)

noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]

latents_dtype = latents.dtype
if do_rf_inversion:
Expand Down Expand Up @@ -1058,17 +1059,18 @@ def invert(
timestep = torch.tensor(t_i, dtype=Y_t.dtype, device=device).repeat(batch_size)

# get the unconditional vector field
u_t_i = self.transformer(
hidden_states=Y_t,
timestep=timestep,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("uncond"):
u_t_i = self.transformer(
hidden_states=Y_t,
timestep=timestep,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]

# get the conditional vector field
u_t_i_cond = (y_1 - Y_t) / (1 - t_i)
Expand Down
47 changes: 25 additions & 22 deletions examples/community/pipeline_flux_semantic_guidance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1135,23 +1135,25 @@ def __call__(
else:
guidance = None

noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]

if enable_edit_guidance and max_edit_cooldown_steps >= i >= min_edit_warmup_steps:
noise_pred_edit_concepts = []
for e_embed, pooled_e_embed, e_text_id in zip(
editing_prompts_embeds, pooled_editing_prompt_embeds, edit_text_ids
):
# TODO-context
noise_pred_edit = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
Expand All @@ -1168,17 +1170,18 @@ def __call__(
if do_true_cfg:
if negative_image_embeds is not None:
self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
noise_pred_uncond = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=negative_pooled_prompt_embeds,
encoder_hidden_states=negative_prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("uncond"):
noise_pred_uncond = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=negative_pooled_prompt_embeds,
encoder_hidden_states=negative_prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
noise_guidance = true_cfg_scale * (noise_pred - noise_pred_uncond)
else:
noise_pred_uncond = noise_pred
Expand Down
23 changes: 12 additions & 11 deletions examples/community/pipeline_flux_with_cfg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -815,17 +815,18 @@ def __call__(
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latent_model_input.shape[0]).to(latent_model_input.dtype)

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]

if do_true_cfg:
neg_noise_pred, noise_pred = noise_pred.chunk(2)
Expand Down
25 changes: 13 additions & 12 deletions examples/community/pipeline_hunyuandit_differential_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1074,18 +1074,19 @@ def __call__(
)

# predict the noise residual
noise_pred = self.transformer(
latent_model_input,
t_expand,
encoder_hidden_states=prompt_embeds,
text_embedding_mask=prompt_attention_mask,
encoder_hidden_states_t5=prompt_embeds_2,
text_embedding_mask_t5=prompt_attention_mask_2,
image_meta_size=add_time_ids,
style=style,
image_rotary_emb=image_rotary_emb,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
latent_model_input,
t_expand,
encoder_hidden_states=prompt_embeds,
text_embedding_mask=prompt_attention_mask,
encoder_hidden_states_t5=prompt_embeds_2,
text_embedding_mask_t5=prompt_attention_mask_2,
image_meta_size=add_time_ids,
style=style,
image_rotary_emb=image_rotary_emb,
return_dict=False,
)[0]

noise_pred, _ = noise_pred.chunk(2, dim=1)

Expand Down
15 changes: 8 additions & 7 deletions examples/community/pipeline_stable_diffusion_3_differential_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -918,13 +918,14 @@ def __call__(
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latent_model_input.shape[0])

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_prompt_embeds,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_prompt_embeds,
return_dict=False,
)[0]

# perform guidance
if self.do_classifier_free_guidance:
Expand Down
18 changes: 10 additions & 8 deletions examples/community/pipeline_stable_diffusion_3_instruct_pix2pix.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1178,14 +1178,15 @@ def __call__(
timestep = t.expand(latent_model_input.shape[0])
scaled_latent_model_input = torch.cat([latent_model_input, image_latents], dim=1)

noise_pred = self.transformer(
hidden_states=scaled_latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_prompt_embeds,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=scaled_latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
pooled_projections=pooled_prompt_embeds,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)[0]

# perform guidance
if self.do_classifier_free_guidance:
Expand All @@ -1204,6 +1205,7 @@ def __call__(
if skip_guidance_layers is not None and should_skip_layers:
timestep = t.expand(latents.shape[0])
latent_model_input = latents
# TODO-context
noise_pred_skip_layers = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
Expand Down
17 changes: 9 additions & 8 deletions examples/community/pipeline_stg_cogvideox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -793,14 +793,15 @@ def __call__(
timestep = t.expand(latent_model_input.shape[0])

# predict noise model_output
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
image_rotary_emb=image_rotary_emb,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
encoder_hidden_states=prompt_embeds,
timestep=timestep,
image_rotary_emb=image_rotary_emb,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_pred.float()

# perform guidance
Expand Down
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