sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

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scheduler = DDIMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
clip_sample=False,
beta_schedule="linear",
timestep_spacing="linspace",
steps_offset=1,
)
pipe.scheduler = scheduler

# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()

output = pipe(
prompt=(
"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "
"orange sky, warm lighting, fishing boats, ocean waves seagulls, "
"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "
"golden hour, coastal landscape, seaside scenery"
),
negative_prompt="bad quality, worse quality",
num_frames=16,
guidance_scale=7.5,
num_inference_steps=25,
generator=torch.Generator("cpu").manual_seed(42),
)
frames = output.frames[0]
export_to_gif(frames, "animation.gif")
masterpiece, bestquality, sunset.
Using Motion LoRAs with PEFT You can also leverage the PEFT backend to combine Motion LoRA’s and create more complex animations. First install PEFT with Copied pip install peft Then you can use the following code to combine Motion LoRAs. Copied import torch
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
from diffusers.utils import export_to_gif

# Load the motion adapter
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
# load SD 1.5 based finetuned model
model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)

pipe.load_lora_weights(
"diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out",
)
pipe.load_lora_weights(
"diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left",
)
pipe.set_adapters(["zoom-out", "pan-left"], adapter_weights=[1.0, 1.0])

scheduler = DDIMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
steps_offset=1,
)
pipe.scheduler = scheduler

# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()

output = pipe(
prompt=(
"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "
"orange sky, warm lighting, fishing boats, ocean waves seagulls, "
"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "
"golden hour, coastal landscape, seaside scenery"
),
negative_prompt="bad quality, worse quality",
num_frames=16,
guidance_scale=7.5,
num_inference_steps=25,
generator=torch.Generator("cpu").manual_seed(42),
)
frames = output.frames[0]
export_to_gif(frames, "animation.gif")
masterpiece, bestquality, sunset.
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. AnimateDiffPipeline class diffusers.AnimateDiffPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel motion_adapter: MotionAdapter scheduler: Union feature_extractor: CLIPImageProcessor = None image_encoder: CLIPVisionModelWithProjection = None ) Parameters vae (AutoencoderKL) —
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) —
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) —
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) —
A UNet2DConditionModel used to create a UNetMotionModel to denoise the encoded video latents. motion_adapter (MotionAdapter) —
A MotionAdapter to be used in combination with unet to denoise the encoded video latents. scheduler (SchedulerMixin) —
A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. Pipeline for text-to-video generation. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights load_ip_adapter() for loading IP Adapters __call__ < source > ( prompt: Union = None num_frames: Optional = 16 height: Optional = None width: Optional = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_videos_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: Optional = 1 cross_attention_kwargs: Optional = None clip_skip: Optional = None ) → TextToVideoSDPipelineOutput or tuple Parameters prompt (str or List[str], optional) —
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The height in pixels of the generated video. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The width in pixels of the generated video. num_frames (int, optional, defaults to 16) —
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
amounts to 2 seconds of video. num_inference_steps (int, optional, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference. guidance_scale (float, optional, defaults to 7.5) —
A higher guidance scale value encourages the model to generate images closely linked to the text
prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) —
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). eta (float, optional, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies

scheduler = DDIMScheduler.from_pretrained(

model_id,

subfolder="scheduler",

clip_sample=False,

beta_schedule="linear",

timestep_spacing="linspace",

steps_offset=1,

)

pipe.scheduler = scheduler

# enable memory savings

pipe.enable_vae_slicing()

pipe.enable_model_cpu_offload()

output = pipe(

prompt=(

"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "

"orange sky, warm lighting, fishing boats, ocean waves seagulls, "

"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "

"golden hour, coastal landscape, seaside scenery"

),

negative_prompt="bad quality, worse quality",

num_frames=16,

guidance_scale=7.5,

num_inference_steps=25,

generator=torch.Generator("cpu").manual_seed(42),

)

frames = output.frames[0]

export_to_gif(frames, "animation.gif")

masterpiece, bestquality, sunset.

Using Motion LoRAs with PEFT You can also leverage the PEFT backend to combine Motion LoRA’s and create more complex animations. First install PEFT with Copied pip install peft Then you can use the following code to combine Motion LoRAs. Copied import torch

from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter

from diffusers.utils import export_to_gif

# Load the motion adapter

adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)

# load SD 1.5 based finetuned model

model_id = "SG161222/Realistic_Vision_V5.1_noVAE"

pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)

pipe.load_lora_weights(

"diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out",

)

pipe.load_lora_weights(

"diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left",

)

pipe.set_adapters(["zoom-out", "pan-left"], adapter_weights=[1.0, 1.0])

scheduler = DDIMScheduler.from_pretrained(

model_id,

subfolder="scheduler",

clip_sample=False,

timestep_spacing="linspace",

beta_schedule="linear",

steps_offset=1,

)

pipe.scheduler = scheduler

# enable memory savings

pipe.enable_vae_slicing()

pipe.enable_model_cpu_offload()

output = pipe(

prompt=(

"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "

"orange sky, warm lighting, fishing boats, ocean waves seagulls, "

"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "

"golden hour, coastal landscape, seaside scenery"

),

negative_prompt="bad quality, worse quality",

num_frames=16,

guidance_scale=7.5,

num_inference_steps=25,

generator=torch.Generator("cpu").manual_seed(42),

)

frames = output.frames[0]

export_to_gif(frames, "animation.gif")

masterpiece, bestquality, sunset.

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. AnimateDiffPipeline class diffusers.AnimateDiffPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel motion_adapter: MotionAdapter scheduler: Union feature_extractor: CLIPImageProcessor = None image_encoder: CLIPVisionModelWithProjection = None ) Parameters vae (AutoencoderKL) —

Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) —

Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) —

A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) —

A UNet2DConditionModel used to create a UNetMotionModel to denoise the encoded video latents. motion_adapter (MotionAdapter) —

A MotionAdapter to be used in combination with unet to denoise the encoded video latents. scheduler (SchedulerMixin) —

A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of

DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler. Pipeline for text-to-video generation. This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods

implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: load_textual_inversion() for loading textual inversion embeddings load_lora_weights() for loading LoRA weights save_lora_weights() for saving LoRA weights load_ip_adapter() for loading IP Adapters __call__ < source > ( prompt: Union = None num_frames: Optional = 16 height: Optional = None width: Optional = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_videos_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = None output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: Optional = 1 cross_attention_kwargs: Optional = None clip_skip: Optional = None ) → TextToVideoSDPipelineOutput or tuple Parameters prompt (str or List[str], optional) —

The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —

The height in pixels of the generated video. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —

The width in pixels of the generated video. num_frames (int, optional, defaults to 16) —

The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds

amounts to 2 seconds of video. num_inference_steps (int, optional, defaults to 50) —

The number of denoising steps. More denoising steps usually lead to a higher quality videos at the

expense of slower inference. guidance_scale (float, optional, defaults to 7.5) —

A higher guidance scale value encourages the model to generate images closely linked to the text

prompt at the expense of lower image quality. Guidance scale is enabled when guidance_scale > 1. negative_prompt (str or List[str], optional) —

The prompt or prompts to guide what to not include in image generation. If not defined, you need to

pass negative_prompt_embeds instead. Ignored when not using guidance (guidance_scale < 1). eta (float, optional, defaults to 0.0) —

Corresponds to parameter eta (η) from the DDIM paper. Only applies