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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ...utils import logging
from ..modular_pipeline import AutoPipelineBlocks, SequentialPipelineBlocks
from ..modular_pipeline_utils import OutputParam
from .before_denoise import (
LTXImage2VideoPrepareLatentsStep,
LTXPrepareLatentsStep,
LTXSetTimestepsStep,
LTXTextInputStep,
)
from .decoders import LTXVaeDecoderStep
from .denoise import LTXDenoiseStep, LTXImage2VideoDenoiseStep
from .encoders import LTXTextEncoderStep, LTXVaeEncoderStep
logger = logging.get_logger(__name__)
# auto_docstring
class LTXCoreDenoiseStep(SequentialPipelineBlocks):
"""
Denoise block that takes encoded conditions and runs the denoising process.
Components:
scheduler (`FlowMatchEulerDiscreteScheduler`) pachifier (`LTXVideoPachifier`) guider
(`ClassifierFreeGuidance`) transformer (`LTXVideoTransformer3DModel`)
Inputs:
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
prompt_embeds (`Tensor`):
text embeddings used to guide the image generation. Can be generated from text_encoder step.
prompt_attention_mask (`Tensor`):
mask for the text embeddings. Can be generated from text_encoder step.
negative_prompt_embeds (`Tensor`, *optional*):
negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
negative_prompt_attention_mask (`Tensor`, *optional*):
mask for the negative text embeddings. Can be generated from text_encoder step.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps.
timesteps (`Tensor`, *optional*):
Timesteps for the denoising process.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
num_frames (`int`, *optional*, defaults to 161):
TODO: Add description.
frame_rate (`int`, *optional*, defaults to 25):
TODO: Add description.
latents (`Tensor`, *optional*):
Pre-generated noisy latents for image generation.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
model_name = "ltx"
block_classes = [
LTXTextInputStep,
LTXSetTimestepsStep,
LTXPrepareLatentsStep,
LTXDenoiseStep,
]
block_names = ["input", "set_timesteps", "prepare_latents", "denoise"]
@property
def description(self):
return "Denoise block that takes encoded conditions and runs the denoising process."
@property
def outputs(self):
return [OutputParam.template("latents")]
# auto_docstring
class LTXImage2VideoCoreDenoiseStep(SequentialPipelineBlocks):
"""
Denoise block for image-to-video that takes encoded conditions and image latents, and runs the denoising process.
Components:
scheduler (`FlowMatchEulerDiscreteScheduler`) pachifier (`LTXVideoPachifier`) guider
(`ClassifierFreeGuidance`) transformer (`LTXVideoTransformer3DModel`)
Inputs:
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
prompt_embeds (`Tensor`):
text embeddings used to guide the image generation. Can be generated from text_encoder step.
prompt_attention_mask (`Tensor`):
mask for the text embeddings. Can be generated from text_encoder step.
negative_prompt_embeds (`Tensor`, *optional*):
negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
negative_prompt_attention_mask (`Tensor`, *optional*):
mask for the negative text embeddings. Can be generated from text_encoder step.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps.
timesteps (`Tensor`, *optional*):
Timesteps for the denoising process.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
num_frames (`int`, *optional*, defaults to 161):
TODO: Add description.
frame_rate (`int`, *optional*, defaults to 25):
TODO: Add description.
latents (`Tensor`, *optional*):
Pre-generated noisy latents for image generation.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
image_latents (`Tensor`):
TODO: Add description.
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
model_name = "ltx"
block_classes = [
LTXTextInputStep,
LTXSetTimestepsStep,
LTXPrepareLatentsStep,
LTXImage2VideoPrepareLatentsStep,
LTXImage2VideoDenoiseStep,
]
block_names = ["input", "set_timesteps", "prepare_latents", "prepare_i2v_latents", "denoise"]
@property
def description(self):
return "Denoise block for image-to-video that takes encoded conditions and image latents, and runs the denoising process."
@property
def outputs(self):
return [OutputParam.template("latents")]
# auto_docstring
class LTXBlocks(SequentialPipelineBlocks):
"""
Modular pipeline blocks for LTX Video text-to-video.
Components:
text_encoder (`T5EncoderModel`) tokenizer (`T5Tokenizer`) guider (`ClassifierFreeGuidance`) scheduler
(`FlowMatchEulerDiscreteScheduler`) pachifier (`LTXVideoPachifier`) transformer
(`LTXVideoTransformer3DModel`) vae (`AutoencoderKLLTXVideo`) video_processor (`VideoProcessor`)
Inputs:
prompt (`str`):
The prompt or prompts to guide image generation.
negative_prompt (`str`, *optional*):
The prompt or prompts not to guide the image generation.
max_sequence_length (`int`, *optional*, defaults to 128):
Maximum sequence length for prompt encoding.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps.
timesteps (`Tensor`, *optional*):
Timesteps for the denoising process.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
num_frames (`int`, *optional*, defaults to 161):
TODO: Add description.
frame_rate (`int`, *optional*, defaults to 25):
TODO: Add description.
latents (`Tensor`, *optional*):
Pre-generated noisy latents for image generation.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
output_type (`str`, *optional*, defaults to np):
Output format: 'pil', 'np', 'pt'.
decode_timestep (`None`, *optional*, defaults to 0.0):
TODO: Add description.
decode_noise_scale (`None`, *optional*):
TODO: Add description.
Outputs:
videos (`list`):
The generated videos.
"""
model_name = "ltx"
block_classes = [
LTXTextEncoderStep,
LTXCoreDenoiseStep,
LTXVaeDecoderStep,
]
block_names = ["text_encoder", "denoise", "decode"]
@property
def description(self):
return "Modular pipeline blocks for LTX Video text-to-video."
@property
def outputs(self):
return [OutputParam.template("videos")]
# auto_docstring
class LTXAutoVaeEncoderStep(AutoPipelineBlocks):
"""
VAE encoder step that encodes the image input into its latent representation.
This is an auto pipeline block that works for image-to-video tasks.
- `LTXVaeEncoderStep` is used when `image` is provided.
- If `image` is not provided, step will be skipped.
Components:
vae (`AutoencoderKLLTXVideo`) video_processor (`VideoProcessor`)
Inputs:
image (`Image | list`, *optional*):
Reference image(s) for denoising. Can be a single image or list of images.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
Outputs:
image_latents (`Tensor`):
Encoded image latents from the VAE encoder
"""
model_name = "ltx"
block_classes = [LTXVaeEncoderStep]
block_names = ["vae_encoder"]
block_trigger_inputs = ["image"]
@property
def description(self):
return (
"VAE encoder step that encodes the image input into its latent representation.\n"
"This is an auto pipeline block that works for image-to-video tasks.\n"
" - `LTXVaeEncoderStep` is used when `image` is provided.\n"
" - If `image` is not provided, step will be skipped."
)
# auto_docstring
class LTXAutoCoreDenoiseStep(AutoPipelineBlocks):
"""
Auto denoise block that selects the appropriate denoise pipeline based on inputs.
- `LTXImage2VideoCoreDenoiseStep` is used when `image_latents` is provided.
- `LTXCoreDenoiseStep` is used otherwise (text-to-video).
Components:
scheduler (`FlowMatchEulerDiscreteScheduler`) pachifier (`LTXVideoPachifier`) guider
(`ClassifierFreeGuidance`) transformer (`LTXVideoTransformer3DModel`)
Inputs:
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
prompt_embeds (`Tensor`):
text embeddings used to guide the image generation. Can be generated from text_encoder step.
prompt_attention_mask (`Tensor`):
mask for the text embeddings. Can be generated from text_encoder step.
negative_prompt_embeds (`Tensor`):
negative text embeddings used to guide the image generation. Can be generated from text_encoder step.
negative_prompt_attention_mask (`Tensor`):
mask for the negative text embeddings. Can be generated from text_encoder step.
num_inference_steps (`int`):
The number of denoising steps.
timesteps (`Tensor`):
Timesteps for the denoising process.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
num_frames (`int`, *optional*, defaults to 161):
TODO: Add description.
frame_rate (`int`, *optional*, defaults to 25):
TODO: Add description.
latents (`Tensor`):
Pre-generated noisy latents for image generation.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
image_latents (`Tensor`, *optional*):
TODO: Add description.
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
Outputs:
latents (`Tensor`):
Denoised latents.
"""
model_name = "ltx"
block_classes = [LTXImage2VideoCoreDenoiseStep, LTXCoreDenoiseStep]
block_names = ["image2video", "text2video"]
block_trigger_inputs = ["image_latents", None]
@property
def description(self):
return (
"Auto denoise block that selects the appropriate denoise pipeline based on inputs.\n"
" - `LTXImage2VideoCoreDenoiseStep` is used when `image_latents` is provided.\n"
" - `LTXCoreDenoiseStep` is used otherwise (text-to-video)."
)
# auto_docstring
class LTXAutoBlocks(SequentialPipelineBlocks):
"""
Auto blocks for LTX Video that support both text-to-video and image-to-video workflows.
Supported workflows:
- `text2video`: requires `prompt`
- `image2video`: requires `image`, `prompt`
Components:
text_encoder (`T5EncoderModel`) tokenizer (`T5Tokenizer`) guider (`ClassifierFreeGuidance`) vae
(`AutoencoderKLLTXVideo`) video_processor (`VideoProcessor`) scheduler (`FlowMatchEulerDiscreteScheduler`)
pachifier (`LTXVideoPachifier`) transformer (`LTXVideoTransformer3DModel`)
Inputs:
prompt (`str`):
The prompt or prompts to guide image generation.
negative_prompt (`str`, *optional*):
The prompt or prompts not to guide the image generation.
max_sequence_length (`int`, *optional*, defaults to 128):
Maximum sequence length for prompt encoding.
image (`Image | list`, *optional*):
Reference image(s) for denoising. Can be a single image or list of images.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`):
The number of denoising steps.
timesteps (`Tensor`):
Timesteps for the denoising process.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
num_frames (`int`, *optional*, defaults to 161):
TODO: Add description.
frame_rate (`int`, *optional*, defaults to 25):
TODO: Add description.
latents (`Tensor`):
Pre-generated noisy latents for image generation.
image_latents (`Tensor`, *optional*):
TODO: Add description.
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
output_type (`str`, *optional*, defaults to np):
Output format: 'pil', 'np', 'pt'.
decode_timestep (`None`, *optional*, defaults to 0.0):
TODO: Add description.
decode_noise_scale (`None`, *optional*):
TODO: Add description.
Outputs:
videos (`list`):
The generated videos.
"""
model_name = "ltx"
block_classes = [
LTXTextEncoderStep,
LTXAutoVaeEncoderStep,
LTXAutoCoreDenoiseStep,
LTXVaeDecoderStep,
]
block_names = ["text_encoder", "vae_encoder", "denoise", "decode"]
_workflow_map = {
"text2video": {"prompt": True},
"image2video": {"image": True, "prompt": True},
}
@property
def description(self):
return "Auto blocks for LTX Video that support both text-to-video and image-to-video workflows."
@property
def outputs(self):
return [OutputParam.template("videos")]
# auto_docstring
class LTXImage2VideoBlocks(SequentialPipelineBlocks):
"""
Modular pipeline blocks for LTX Video image-to-video.
Components:
text_encoder (`T5EncoderModel`) tokenizer (`T5Tokenizer`) guider (`ClassifierFreeGuidance`) vae
(`AutoencoderKLLTXVideo`) video_processor (`VideoProcessor`) scheduler (`FlowMatchEulerDiscreteScheduler`)
pachifier (`LTXVideoPachifier`) transformer (`LTXVideoTransformer3DModel`)
Inputs:
prompt (`str`):
The prompt or prompts to guide image generation.
negative_prompt (`str`, *optional*):
The prompt or prompts not to guide the image generation.
max_sequence_length (`int`, *optional*, defaults to 128):
Maximum sequence length for prompt encoding.
image (`Image | list`, *optional*):
Reference image(s) for denoising. Can be a single image or list of images.
height (`int`, *optional*, defaults to 512):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 704):
The width in pixels of the generated image.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps.
timesteps (`Tensor`, *optional*):
Timesteps for the denoising process.
sigmas (`list`, *optional*):
Custom sigmas for the denoising process.
num_frames (`int`, *optional*, defaults to 161):
TODO: Add description.
frame_rate (`int`, *optional*, defaults to 25):
TODO: Add description.
latents (`Tensor`, *optional*):
Pre-generated noisy latents for image generation.
image_latents (`Tensor`):
TODO: Add description.
attention_kwargs (`dict`, *optional*):
Additional kwargs for attention processors.
output_type (`str`, *optional*, defaults to np):
Output format: 'pil', 'np', 'pt'.
decode_timestep (`None`, *optional*, defaults to 0.0):
TODO: Add description.
decode_noise_scale (`None`, *optional*):
TODO: Add description.
Outputs:
videos (`list`):
The generated videos.
"""
model_name = "ltx"
block_classes = [
LTXTextEncoderStep,
LTXAutoVaeEncoderStep,
LTXImage2VideoCoreDenoiseStep,
LTXVaeDecoderStep,
]
block_names = ["text_encoder", "vae_encoder", "denoise", "decode"]
@property
def description(self):
return "Modular pipeline blocks for LTX Video image-to-video."
@property
def outputs(self):
return [OutputParam.template("videos")]
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