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prompt. cross_attention_kwargs (dict, optional) — |
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under |
self.processor in |
diffusers.models.attention_processor. Returns |
ImagePipelineOutput or tuple |
Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import AutoPipelineForText2Image |
>>> import torch |
>>> pipe = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16) |
>>> pipe.enable_model_cpu_offload() |
>>> prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background." |
>>> generator = torch.Generator(device="cpu").manual_seed(0) |
>>> image = pipe(prompt, num_inference_steps=25, generator=generator).images[0] encode_prompt < source > ( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None _cut_context = False attention_mask: Optional = None negative_attention_mask: Optional = None ) Parameters prompt (str or List[str], optional) — |
prompt to be encoded |
device — (torch.device, optional): |
torch device to place the resulting embeddings on num_images_per_prompt (int, optional, defaults to 1) — |
number of images that should be generated per prompt do_classifier_free_guidance (bool, optional, defaults to True) — |
whether to use classifier free guidance or not negative_prompt (str or List[str], optional) — |
The prompt or prompts not to guide the image generation. If not defined, one has to pass |
negative_prompt_embeds. instead. If not defined, one has to pass negative_prompt_embeds. instead. |
Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1). prompt_embeds (torch.FloatTensor, optional) — |
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 prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) — |
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 negative_prompt input |
argument. attention_mask (torch.FloatTensor, optional) — |
Pre-generated attention mask. Must provide if passing prompt_embeds directly. negative_attention_mask (torch.FloatTensor, optional) — |
Pre-generated negative attention mask. Must provide if passing negative_prompt_embeds directly. Encodes the prompt into text encoder hidden states. Kandinsky3Img2ImgPipeline class diffusers.Kandinsky3Img2ImgPipeline < source > ( tokenizer: T5Tokenizer text_encoder: T5EncoderModel unet: Kandinsky3UNet scheduler: DDPMScheduler movq: VQModel ) __call__ < source > ( prompt: Union = None image: Union = None strength: float = 0.3 num_inference_steps: int = 25 guidance_scale: float = 3.0 negative_prompt: Union = None num_images_per_prompt: Optional = 1 generator: Union = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None attention_mask: Optional = None negative_attention_mask: Optional = None output_type: Optional = 'pil' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → ImagePipelineOutput or tuple Parameters prompt (str or List[str], optional) — |
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds. |
instead. image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) — |
Image, or tensor representing an image batch, that will be used as the starting point for the |
process. strength (float, optional, defaults to 0.8) — |
Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a |
starting point and more noise is added the higher the strength. The number of denoising steps depends |
on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising |
process runs for the full number of iterations specified in num_inference_steps. A value of 1 |
essentially ignores image. num_inference_steps (int, optional, defaults to 50) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. guidance_scale (float, optional, defaults to 3.0) — |
Guidance scale as defined in Classifier-Free Diffusion Guidance. |
guidance_scale is defined as w of equation 2. of Imagen |
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt, |
usually at the expense of lower image quality. negative_prompt (str or List[str], optional) — |
The prompt or prompts not to guide the image generation. If not defined, one has to pass |
negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is |
less than 1). num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. generator (torch.Generator or List[torch.Generator], optional) — |
One or a list of torch generator(s) |
to make generation deterministic. prompt_embeds (torch.FloatTensor, optional) — |
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 prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) — |
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 negative_prompt input |
argument. attention_mask (torch.FloatTensor, optional) — |
Pre-generated attention mask. Must provide if passing prompt_embeds directly. negative_attention_mask (torch.FloatTensor, optional) — |
Pre-generated negative attention mask. Must provide if passing negative_prompt_embeds directly. output_type (str, optional, defaults to "pil") — |
The output format of the generate image. Choose between |
PIL: PIL.Image.Image or np.array. return_dict (bool, optional, defaults to True) — |
Whether or not to return a ~pipelines.stable_diffusion.IFPipelineOutput instead of a plain tuple. callback_on_step_end (Callable, optional) — |
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) — |
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 pipeline class. Returns |
ImagePipelineOutput or tuple |
Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import AutoPipelineForImage2Image |
>>> from diffusers.utils import load_image |
>>> import torch |
>>> pipe = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16) |
>>> pipe.enable_model_cpu_offload() |
>>> prompt = "A painting of the inside of a subway train with tiny raccoons." |
>>> image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky3/t2i.png") |
>>> generator = torch.Generator(device="cpu").manual_seed(0) |
>>> image = pipe(prompt, image=image, strength=0.75, num_inference_steps=25, generator=generator).images[0] encode_prompt < source > ( prompt do_classifier_free_guidance = True num_images_per_prompt = 1 device = None negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None _cut_context = False attention_mask: Optional = None negative_attention_mask: Optional = None ) Parameters prompt (str or List[str], optional) — |
prompt to be encoded Encodes the prompt into text encoder hidden states. device: (torch.device, optional): |
torch device to place the resulting embeddings on |
num_images_per_prompt (int, optional, defaults to 1): |
number of images that should be generated per prompt |
do_classifier_free_guidance (bool, optional, defaults to True): |
whether to use classifier free guidance or not |
negative_prompt (str or List[str], optional): |
The prompt or prompts not to guide the image generation. If not defined, one has to pass |
negative_prompt_embeds. instead. If not defined, one has to pass negative_prompt_embeds. instead. |
Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1). |
prompt_embeds (torch.FloatTensor, optional): |
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 prompt input argument. |
negative_prompt_embeds (torch.FloatTensor, optional): |
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 negative_prompt input |
argument. |
attention_mask (torch.FloatTensor, optional): |
Pre-generated attention mask. Must provide if passing prompt_embeds directly. |
negative_attention_mask (torch.FloatTensor, optional): |
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