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conditioning

Conditioning stage for diffusion pipelines.

Classes

fastvideo.pipelines.stages.conditioning.ConditioningStage

Bases: PipelineStage

Stage for applying conditioning to the diffusion process.

This stage handles the application of conditioning, such as classifier-free guidance, to the diffusion process.

Functions

fastvideo.pipelines.stages.conditioning.ConditioningStage.forward 
forward(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch

Apply conditioning to the diffusion process.

Parameters:

Name Type Description Default
batch ForwardBatch

The current batch information.

 required
fastvideo_args FastVideoArgs

The inference arguments.

 required

Returns:

Type Description
ForwardBatch

The batch with applied conditioning.
Source code in fastvideo/pipelines/stages/conditioning.py 
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@torch.no_grad()
def forward(
    self,
    batch: ForwardBatch,
    fastvideo_args: FastVideoArgs,
) -> ForwardBatch:
    """
    Apply conditioning to the diffusion process.

    Args:
        batch: The current batch information.
        fastvideo_args: The inference arguments.

    Returns:
        The batch with applied conditioning.
    """
    # TODO!!
    if not batch.do_classifier_free_guidance:
        return batch
    else:
        return batch

    logger.info("batch.negative_prompt_embeds: %s",
                batch.negative_prompt_embeds)
    logger.info("do_classifier_free_guidance: %s",
                batch.do_classifier_free_guidance)
    logger.info("cfg_scale: %s", batch.guidance_scale)

    # Ensure negative prompt embeddings are available
    assert batch.negative_prompt_embeds is not None, (
        "Negative prompt embeddings are required for classifier-free guidance"
    )

    # Concatenate primary embeddings and masks
    batch.prompt_embeds = torch.cat(
        [batch.negative_prompt_embeds, batch.prompt_embeds])
    if batch.attention_mask is not None:
        batch.attention_mask = torch.cat(
            [batch.negative_attention_mask, batch.attention_mask])

    # Concatenate secondary embeddings and masks if present
    if batch.prompt_embeds_2 is not None:
        batch.prompt_embeds_2 = torch.cat(
            [batch.negative_prompt_embeds_2, batch.prompt_embeds_2])
    if batch.attention_mask_2 is not None:
        batch.attention_mask_2 = torch.cat(
            [batch.negative_attention_mask_2, batch.attention_mask_2])

    return batch
fastvideo.pipelines.stages.conditioning.ConditioningStage.verify_input 
verify_input(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify conditioning stage inputs.

Source code in fastvideo/pipelines/stages/conditioning.py 
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def verify_input(self, batch: ForwardBatch,
                 fastvideo_args: FastVideoArgs) -> VerificationResult:
    """Verify conditioning stage inputs."""
    result = VerificationResult()
    if not batch.prompt_embeds:
        # No text encoder/prompt embeddings: skip checks and effectively disable CFG.
        batch.do_classifier_free_guidance = False
        return result
    result.add_check("do_classifier_free_guidance",
                     batch.do_classifier_free_guidance, V.bool_value)
    result.add_check("guidance_scale", batch.guidance_scale,
                     V.positive_float)
    # Matrix-Game allow empty prompt
    # embeddings when CFG isn't enabled.
    if batch.do_classifier_free_guidance or batch.prompt_embeds:
        result.add_check("prompt_embeds", batch.prompt_embeds,
                         V.list_not_empty)
        result.add_check(
            "negative_prompt_embeds", batch.negative_prompt_embeds, lambda
            x: not batch.do_classifier_free_guidance or V.list_not_empty(x))
    return result
fastvideo.pipelines.stages.conditioning.ConditioningStage.verify_output 
verify_output(batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult

Verify conditioning stage outputs.

Source code in fastvideo/pipelines/stages/conditioning.py 
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def verify_output(self, batch: ForwardBatch,
                  fastvideo_args: FastVideoArgs) -> VerificationResult:
    """Verify conditioning stage outputs."""
    result = VerificationResult()
    if batch.prompt_embeds is None or not batch.prompt_embeds:
        batch.do_classifier_free_guidance = False
        return result
    if batch.do_classifier_free_guidance or batch.prompt_embeds:
        result.add_check("prompt_embeds", batch.prompt_embeds,
                         V.list_not_empty)
    return result

Functions