Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

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pipe.enable_model_cpu_offload()
pipe.enable_vae_slicing()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true"
)
image = image.resize((512, 512))
prompt = "cat in a hat"
negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality"
generator = torch.Generator("cpu").manual_seed(0)
output = pipe(image=image, prompt=prompt, generator=generator)
frames = output.frames[0]
export_to_gif(frames, "pia-freeinit-animation.gif") masterpiece, bestquality, sunset.
FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the num_iters parameter that is set when enabling it. Setting the use_fast_sampling parameter to True can improve the overall performance (at the cost of lower quality...
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) β€”
Frozen text-encoder (clip-vit-large-patch14). tokenizer (CLIPTokenizer) β€”
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) β€”
A UNet2DConditionModel used to create a UNetMotionModel to denoise the encoded video latents. motion_adapter (MotionAdapter) β€”
A MotionAdapter to be used in combination with unet to denoise the encoded video latents. scheduler (SchedulerMixin) β€”
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(...
The input image to be used for video generation. prompt (str or List[str], optional) β€”
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. strength (float, optional, defaults to 1.0) β€” Indicates extent to transform the reference image. Must be between 0 and 1. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The height in pixels of the generated video. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The width in pixels of the generated video. num_frames (int, optional, defaults to 16) β€”
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) β€”
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) β€”
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) β€”
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) β€”
Corresponds to parameter eta (Ξ·) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) β€”
A torch.Generator to make
generation deterministic. latents (torch.FloatTensor, optional) β€”
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random generator. Latents should be of shape
(batch_size, num_channel, num_frames, height, width). prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, negative_prompt_embeds are generated from the negative_prompt input argument.
ip_adapter_image β€” (PipelineImageInput, optional):
Optional image input to work with IP Adapters.
motion_scale β€” (int, optional, defaults to 0):
Parameter that controls the amount and type of motion that is added to the image. Increasing the value increases the amount of motion, while specific
ranges of values control the type of motion that is added. Must be between 0 and 8.
Set between 0-2 to only increase the amount of motion.
Set between 3-5 to create looping motion.
Set between 6-8 to perform motion with image style transfer. output_type (str, optional, defaults to "pil") β€”
The output format of the generated video. Choose between torch.FloatTensor, PIL.Image or
np.array. return_dict (bool, optional, defaults to True) β€”
Whether or not to return a TextToVideoSDPipelineOutput instead
of a plain tuple. cross_attention_kwargs (dict, optional) β€”
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in
self.processor. clip_skip (int, optional) β€”
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings. callback_on_step_end (Callable, optional) β€”
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict). callback_kwargs will include a list of all tensors as specified by
callback_on_step_end_tensor_inputs. callback_on_step_end_tensor_inputs (List, optional) β€”
The list of tensor inputs for the callback_on_step_end function. The tensors specified in the list
will be passed as callback_kwargs argument. You will only be able to include variables listed in the
._callback_tensor_inputs attribute of your pipeine class. Returns
TextToVideoSDPipelineOutput or tuple
If return_dict is True, TextToVideoSDPipelineOutput is
returned, otherwise a tuple is returned where the first element is a list with the generated frames.
The call function to the pipeline for generation. Examples: Copied >>> import torch
>>> from diffusers import (
... EulerDiscreteScheduler,
... MotionAdapter,
... PIAPipeline,
... )
>>> from diffusers.utils import export_to_gif, load_image
>>> adapter = MotionAdapter.from_pretrained("../checkpoints/pia-diffusers")
>>> pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter)
>>> pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
>>> image = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true"
... )
>>> image = image.resize((512, 512))
>>> prompt = "cat in a hat"
>>> negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality, deformed, distorted, disfigured, bad eyes, wrong lips,weird mouth, bad teeth, mutated hands and fingers, bad anatomy,wrong anatomy, amputation, extra limb, missing limb, floating,limbs, disconnected limbs, mutation, ugly, disgusting, ...
>>> generator = torch.Generator("cpu").manual_seed(0)
>>> output = pipe(image=image, prompt=prompt, negative_prompt=negative_prompt, generator=generator)
>>> frames = output.frames[0]
>>> export_to_gif(frames, "pia-animation.gif") disable_free_init < source > ( ) Disables the FreeInit mechanism if enabled. disable_freeu < source > ( ) Disables the FreeU mechanism if enabled. disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, t...
computing decoding in one step. disable_vae_tiling < source > ( ) Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to
computing decoding in one step. enable_free_init < source > ( num_iters: int = 3 use_fast_sampling: bool = False method: str = 'butterworth' order: int = 4 spatial_stop_frequency: float = 0.25 temporal_stop_frequency: float = 0.25 generator: Optional = None ) Parameters num_iters (int, optional, defaults to 3) β€”
Number of FreeInit noise re-initialization iterations. use_fast_sampling (bool, optional, defaults to False) β€”
Whether or not to speedup sampling procedure at the cost of probably lower quality results. Enables
the β€œCoarse-to-Fine Sampling” strategy, as mentioned in the paper, if set to True. method (str, optional, defaults to butterworth) β€”