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yiyixuxu merged 8 commits into from
Jan 7, 2026
Merged

Add transformer cache context for SkyReels-V2 pipelines & Update docs #12837

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46c5e58
feat: Add transformer cache context for conditional and unconditional…
tolgacangoz Dec 14, 2025
17072dd
docs: Remove SkyReels-V2 FLF2V model link and add contributor attribu…
tolgacangoz Dec 14, 2025
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Merge branch 'main' into add-caching-to-skyreels-v2-pipelines
tolgacangoz Dec 16, 2025
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Merge branch 'main' into add-caching-to-skyreels-v2-pipelines
tolgacangoz Dec 19, 2025
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Merge branch 'main' into add-caching-to-skyreels-v2-pipelines
tolgacangoz Dec 22, 2025
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Merge branch 'main' into add-caching-to-skyreels-v2-pipelines
tolgacangoz Dec 25, 2025
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Merge branch 'main' into add-caching-to-skyreels-v2-pipelines
tolgacangoz Jan 1, 2026
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Merge branch 'main' into add-caching-to-skyreels-v2-pipelines
tolgacangoz Jan 6, 2026
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3 changes: 2 additions & 1 deletion docs/source/en/api/pipelines/skyreels_v2.md
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Original file line numberDiff line numberDiff line change
Expand Up@@ -37,7 +37,8 @@ The following SkyReels-V2 models are supported in Diffusers:
- [SkyReels-V2 I2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers)
- [SkyReels-V2 I2V 14B - 540P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-540P-Diffusers)
- [SkyReels-V2 I2V 14B - 720P](https://huggingface.co/Skywork/SkyReels-V2-I2V-14B-720P-Diffusers)
- [SkyReels-V2 FLF2V 1.3B - 540P](https://huggingface.co/Skywork/SkyReels-V2-FLF2V-1.3B-540P-Diffusers)
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This model was contributed by [M. Tolga Cangöz](https://github.com/tolgacangoz).
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> [!TIP]
> Click on the SkyReels-V2 models in the right sidebar for more examples of video generation.
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24 changes: 13 additions & 11 deletions src/diffusers/pipelines/skyreels_v2/pipeline_skyreels_v2.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -545,22 +545,24 @@ def __call__(
latent_model_input = latents.to(transformer_dtype)
timestep = t.expand(latents.shape[0])

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]

if self.do_classifier_free_guidance:
noise_uncond = self.transformer(
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
encoder_hidden_states=prompt_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]

if self.do_classifier_free_guidance:
with self.transformer.cache_context("uncond"):
noise_uncond = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)

# compute the previous noisy sample x_t -> x_t-1
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27 changes: 15 additions & 12 deletions src/diffusers/pipelines/skyreels_v2/pipeline_skyreels_v2_diffusion_forcing.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -887,25 +887,28 @@ def __call__(
)
timestep[:, valid_interval_start:prefix_video_latents_frames] = addnoise_condition

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
if self.do_classifier_free_guidance:
noise_uncond = self.transformer(
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
encoder_hidden_states=prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]

if self.do_classifier_free_guidance:
with self.transformer.cache_context("uncond"):
noise_uncond = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)

update_mask_i = step_update_mask[i]
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27 changes: 15 additions & 12 deletions src/diffusers/pipelines/skyreels_v2/pipeline_skyreels_v2_diffusion_forcing_i2v.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -966,25 +966,28 @@ def __call__(
)
timestep[:, valid_interval_start:prefix_video_latents_frames] = addnoise_condition

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
if self.do_classifier_free_guidance:
noise_uncond = self.transformer(
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
encoder_hidden_states=prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]

if self.do_classifier_free_guidance:
with self.transformer.cache_context("uncond"):
noise_uncond = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)

update_mask_i = step_update_mask[i]
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27 changes: 15 additions & 12 deletions src/diffusers/pipelines/skyreels_v2/pipeline_skyreels_v2_diffusion_forcing_v2v.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -974,25 +974,28 @@ def __call__(
)
timestep[:, valid_interval_start:prefix_video_latents_frames] = addnoise_condition

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
if self.do_classifier_free_guidance:
noise_uncond = self.transformer(
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
encoder_hidden_states=prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]

if self.do_classifier_free_guidance:
with self.transformer.cache_context("uncond"):
noise_uncond = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
enable_diffusion_forcing=True,
fps=fps_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)

update_mask_i = step_update_mask[i]
Expand Down
26 changes: 14 additions & 12 deletions src/diffusers/pipelines/skyreels_v2/pipeline_skyreels_v2_i2v.py
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Original file line numberDiff line numberDiff line change
Expand Up@@ -678,24 +678,26 @@ def __call__(
latent_model_input = torch.cat([latents, condition], dim=1).to(transformer_dtype)
timestep = t.expand(latents.shape[0])

noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=prompt_embeds,
encoder_hidden_states_image=image_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]

if self.do_classifier_free_guidance:
noise_uncond = self.transformer(
with self.transformer.cache_context("cond"):
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
encoder_hidden_states=prompt_embeds,
encoder_hidden_states_image=image_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]

if self.do_classifier_free_guidance:
with self.transformer.cache_context("uncond"):
noise_uncond = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
encoder_hidden_states_image=image_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)

# compute the previous noisy sample x_t -> x_t-1
Expand Down
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