Generates images or video using the EasyAnimate pipeline based on the provided prompts.

>>> import torch
>>> from diffusers import EasyAnimatePipeline
>>> from diffusers.utils import export_to_video

>>> # Models: "alibaba-pai/EasyAnimateV5.1-12b-zh"
>>> pipe = EasyAnimatePipeline.from_pretrained(
...     "alibaba-pai/EasyAnimateV5.1-7b-zh-diffusers", torch_dtype=torch.float16
... ).to("cuda")
>>> prompt = (
...     "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. "
...     "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
...     "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
...     "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
...     "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
...     "atmosphere of this unique musical performance."
... )
>>> sample_size = (512, 512)
>>> video = pipe(
...     prompt=prompt,
...     guidance_scale=6,
...     negative_prompt="bad detailed",
...     height=sample_size[0],
...     width=sample_size[1],
...     num_inference_steps=50,
... ).frames[0]
>>> export_to_video(video, "output.mp4", fps=8)

prompt (str or List[str], optional): Text prompts to guide the image or video generation. If not provided, use prompt_embeds instead. num_frames (int, optional): Length of the generated video (in frames). height (int, optional): Height of the generated image in pixels. width (int, optional): Width of the generated image in pixels. num_inference_steps (int, optional, defaults to 50): Number of denoising steps during generation. More steps generally yield higher quality images but slow down inference. guidance_scale (float, optional, defaults to 5.0): Encourages the model to align outputs with prompts. A higher value may decrease image quality. negative_prompt (str or List[str], optional): Prompts indicating what to exclude in generation. If not specified, use negative_prompt_embeds. num_images_per_prompt (int, optional, defaults to 1): Number of images to generate for each prompt. eta (float, optional, defaults to 0.0): Applies to DDIM scheduling. Controlled by the eta parameter from the related literature. generator (torch.Generator or List[torch.Generator], optional): A generator to ensure reproducibility in image generation. latents (torch.Tensor, optional): Predefined latent tensors to condition generation. prompt_embeds (torch.Tensor, optional): Text embeddings for the prompts. Overrides prompt string inputs for more flexibility. negative_prompt_embeds (torch.Tensor, optional): Embeddings for negative prompts. Overrides string inputs if defined. prompt_attention_mask (torch.Tensor, optional): Attention mask for the primary prompt embeddings. negative_prompt_attention_mask (torch.Tensor, optional): Attention mask for negative prompt embeddings. output_type (str, optional, defaults to “latent”): Format of the generated output, either as a PIL image or as a NumPy array. return_dict (bool, optional, defaults to True): If True, returns a structured output. Otherwise returns a simple tuple. callback_on_step_end (Callable, optional): Functions called at the end of each denoising step. callback_on_step_end_tensor_inputs (List[str], optional): Tensor names to be included in callback function calls. guidance_rescale (float, optional, defaults to 0.0): Adjusts noise levels based on guidance scale. original_size (Tuple[int, int], optional, defaults to (1024, 1024)): Original dimensions of the output. target_size (Tuple[int, int], optional): Desired output dimensions for calculations. crops_coords_top_left (Tuple[int, int], optional, defaults to (0, 0)): Coordinates for cropping.

Encodes the prompt into text encoder hidden states.