Mochi 1 Preview¶
Mochi 1 Preview from Genmo.
Mochi 1 preview is an open state-of-the-art video generation model with high-fidelity motion and strong prompt adherence in preliminary evaluation. This model dramatically closes the gap between closed and open video generation systems. The model is released under a permissive Apache 2.0 license.
Tip
Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.
Generating videos with Mochi-1 Preview¶
The following example will download the full precision mochi-1-preview
weights and produce the highest quality results but will require at least 42GB VRAM to run.
import mindspore as ms
from mindone.diffusers import MochiPipeline
from mindone.diffusers.utils import export_to_video
pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview", mindspore_dtype=ms.float16)
# Enable memory savings
pipe.enable_vae_tiling()
prompt = "Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k."
frames = pipe(prompt, num_inference_steps=28, guidance_scale=3.5)[0][0]
export_to_video(frames, "mochi.mp4", fps=30)
mindone.diffusers.MochiPipeline
¶
Bases: DiffusionPipeline
, Mochi1LoraLoaderMixin
The mochi pipeline for text-to-video generation.
Reference: https://github.com/genmoai/models
PARAMETER | DESCRIPTION |
---|---|
transformer |
Conditional Transformer architecture to denoise the encoded video latents.
TYPE:
|
scheduler |
A scheduler to be used in combination with
TYPE:
|
vae |
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
TYPE:
|
text_encoder |
T5, specifically the google/t5-v1_1-xxl variant.
TYPE:
|
tokenizer |
Tokenizer of class CLIPTokenizer.
TYPE:
|
tokenizer |
Second Tokenizer of class T5TokenizerFast.
TYPE:
|
Source code in mindone/diffusers/pipelines/mochi/pipeline_mochi.py
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mindone.diffusers.MochiPipeline.__call__(prompt=None, negative_prompt=None, height=None, width=None, num_frames=19, num_inference_steps=64, timesteps=None, guidance_scale=4.5, num_videos_per_prompt=1, generator=None, latents=None, prompt_embeds=None, prompt_attention_mask=None, negative_prompt_embeds=None, negative_prompt_attention_mask=None, output_type='pil', return_dict=False, attention_kwargs=None, callback_on_step_end=None, callback_on_step_end_tensor_inputs=['latents'], max_sequence_length=256)
¶
Function invoked when calling the pipeline for generation.
PARAMETER | DESCRIPTION |
---|---|
prompt |
The prompt or prompts to guide the image generation. If not defined, one has to pass
TYPE:
|
height |
The height in pixels of the generated image. This is set to 480 by default for the best results.
TYPE:
|
width |
The width in pixels of the generated image. This is set to 848 by default for the best results.
TYPE:
|
num_frames |
The number of video frames to generate
TYPE:
|
num_inference_steps |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
TYPE:
|
timesteps |
Custom timesteps to use for the denoising process with schedulers which support a
TYPE:
|
guidance_scale |
Guidance scale as defined in Classifier-Free Diffusion Guidance.
TYPE:
|
num_videos_per_prompt |
The number of videos to generate per prompt.
TYPE:
|
generator |
One or a list of numpy generator(s) to make generation deterministic.
TYPE:
|
latents |
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random
TYPE:
|
prompt_embeds |
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from
TYPE:
|
prompt_attention_mask |
Pre-generated attention mask for text embeddings.
TYPE:
|
negative_prompt_embeds |
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
provided, negative_prompt_embeds will be generated from
TYPE:
|
negative_prompt_attention_mask |
Pre-generated attention mask for negative text embeddings.
TYPE:
|
output_type |
The output format of the generate image. Choose between
PIL:
TYPE:
|
return_dict |
Whether or not to return a [
TYPE:
|
attention_kwargs |
A kwargs dictionary that if specified is passed along to the
TYPE:
|
callback_on_step_end |
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments:
TYPE:
|
callback_on_step_end_tensor_inputs |
The list of tensor inputs for the
TYPE:
|
max_sequence_length |
Maximum sequence length to use with the
TYPE:
|
RETURNS | DESCRIPTION |
---|---|
[ |
Source code in mindone/diffusers/pipelines/mochi/pipeline_mochi.py
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mindone.diffusers.MochiPipeline.disable_vae_slicing()
¶
Disable sliced VAE decoding. If enable_vae_slicing
was previously enabled, this method will go back to
computing decoding in one step.
Source code in mindone/diffusers/pipelines/mochi/pipeline_mochi.py
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|
mindone.diffusers.MochiPipeline.disable_vae_tiling()
¶
Disable tiled VAE decoding. If enable_vae_tiling
was previously enabled, this method will go back to
computing decoding in one step.
Source code in mindone/diffusers/pipelines/mochi/pipeline_mochi.py
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|
mindone.diffusers.MochiPipeline.enable_vae_slicing()
¶
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
Source code in mindone/diffusers/pipelines/mochi/pipeline_mochi.py
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|
mindone.diffusers.MochiPipeline.enable_vae_tiling()
¶
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.
Source code in mindone/diffusers/pipelines/mochi/pipeline_mochi.py
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|
mindone.diffusers.MochiPipeline.encode_prompt(prompt, negative_prompt=None, do_classifier_free_guidance=True, num_videos_per_prompt=1, prompt_embeds=None, negative_prompt_embeds=None, prompt_attention_mask=None, negative_prompt_attention_mask=None, max_sequence_length=256, dtype=None)
¶
Encodes the prompt into text encoder hidden states.
PARAMETER | DESCRIPTION |
---|---|
prompt |
prompt to be encoded
TYPE:
|
negative_prompt |
The prompt or prompts not to guide the image generation. If not defined, one has to pass
TYPE:
|
do_classifier_free_guidance |
Whether to use classifier free guidance or not.
TYPE:
|
num_videos_per_prompt |
Number of videos that should be generated per prompt. ms device to place the resulting embeddings on
TYPE:
|
prompt_embeds |
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not
provided, text embeddings will be generated from
TYPE:
|
negative_prompt_embeds |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, negative_prompt_embeds will be generated from
TYPE:
|
dtype |
(
TYPE:
|
Source code in mindone/diffusers/pipelines/mochi/pipeline_mochi.py
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|
mindone.diffusers.pipelines.mochi.pipeline_output.MochiPipelineOutput
dataclass
¶
Bases: BaseOutput
Output class for Mochi pipelines.
PARAMETER | DESCRIPTION |
---|---|
frames |
List of video outputs - It can be a nested list of length
TYPE:
|
Source code in mindone/diffusers/pipelines/mochi/pipeline_output.py
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