scrapped_outputs/00a44ba96e48f08abc944973f3de6edb.txt

Cycle Diffusion Cycle Diffusion is a text guided image-to-image generation model proposed in Unifying Diffusion Models’ Latent Space, with Applications to CycleDiffusion and Guidance by Chen Henry Wu, Fernando De la Torre. The abstract from the paper is: Diffusion models have achieved unprecedented performance in generative modeling. The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e.g., Gaussian) latent space of GANs, VAEs, and normalizing flows. This paper provides an alternative, Gaussian formulation of the latent space of various diffusion models, as well as an invertible DPM-Encoder that maps images into the latent space. While our formulation is purely based on the definition of diffusion models, we demonstrate several intriguing consequences. (1) Empirically, we observe that a common latent space emerges from two diffusion models trained independently on related domains. In light of this finding, we propose CycleDiffusion, which uses DPM-Encoder for unpaired image-to-image translation. Furthermore, applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors. (2) One can guide pre-trained diffusion models and GANs by controlling the latent codes in a unified, plug-and-play formulation based on energy-based models. Using the CLIP model and a face recognition model as guidance, we demonstrate that diffusion models have better coverage of low-density sub-populations and individuals than GANs. The code is publicly available at this https URL. 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.  CycleDiffusionPipeline  class diffusers.CycleDiffusionPipeline  < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: DDIMScheduler safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor requires_safety_checker: bool = True  )   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 to denoise the encoded image latents.   scheduler (SchedulerMixin) —
A scheduler to be used in combination with unet to denoise the encoded image latents. Can only be an
instance of DDIMScheduler.   safety_checker (StableDiffusionSafetyChecker) —
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the model card for more details
about a model’s potential harms.   feature_extractor (CLIPImageProcessor) —
A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker.    Pipeline for text-guided image to image generation using Stable Diffusion. 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  __call__  < source > ( prompt: typing.Union[str, typing.List[str]] source_prompt: typing.Union[str, typing.List[str]] image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.FloatTensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.FloatTensor]] = None strength: float = 0.8 num_inference_steps: typing.Optional[int] = 50 guidance_scale: typing.Optional[float] = 7.5 source_guidance_scale: typing.Optional[float] = 1 num_images_per_prompt: typing.Optional[int] = 1 eta: typing.Optional[float] = 0.1 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: int = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None clip_skip: typing.Optional[int] = None  ) → StableDiffusionPipelineOutput or tuple  Parameters   prompt (str or List[str]) —
The prompt or prompts to guide the image generation.   image (torch.FloatTensor np.ndarray, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) —
Image or tensor representing an image batch to be used as the starting point. Can also accept image
latents as image, but if passing latents directly it is not encoded again.   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. This parameter is modulated by strength.   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.   source_guidance_scale (float, optional, defaults to 1) —
Guidance scale for the source prompt. This is useful to control the amount of influence the source
prompt has for encoding.   num_images_per_prompt (int, optional, defaults to 1) —
The number of images to generate per prompt.   eta (float, optional, defaults to 0.0) —
Corresponds to parameter eta (η) from the DDIM paper. Only applies
to the DDIMScheduler, and is ignored in other schedulers.   generator (torch.Generator or List[torch.Generator], optional) —
A torch.Generator to make
generation deterministic.   prompt_embeds (torch.FloatTensor, optional) —
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) —
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.   output_type (str, optional, defaults to "pil") —
The output format of the generated image. Choose between PIL.Image or np.array.   return_dict (bool, optional, defaults to True) —
Whether or not to return a StableDiffusionPipelineOutput instead of a
plain tuple.   callback (Callable, optional) —
A function that calls every callback_steps steps during inference. The function is called with the
following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor).   callback_steps (int, optional, defaults to 1) —
The frequency at which the callback function is called. If not specified, the callback is called at
every step.   cross_attention_kwargs (dict, optional) —
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in
self.processor.   clip_skip (int, optional) —
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.   Returns 
StableDiffusionPipelineOutput or tuple
  
If return_dict is True, StableDiffusionPipelineOutput is returned,
otherwise a tuple is returned where the first element is a list with the generated images and the
second element is a list of bools indicating whether the corresponding generated image contains
“not-safe-for-work” (nsfw) content.
  The call function to the pipeline for generation.  Example:   Copied import requests
import torch
from PIL import Image
from io import BytesIO

from diffusers import CycleDiffusionPipeline, DDIMScheduler

# load the pipeline
# make sure you're logged in with `huggingface-cli login`
model_id_or_path = "CompVis/stable-diffusion-v1-4"
scheduler = DDIMScheduler.from_pretrained(model_id_or_path, subfolder="scheduler")
pipe = CycleDiffusionPipeline.from_pretrained(model_id_or_path, scheduler=scheduler).to("cuda")

# let's download an initial image
url = "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/An%20astronaut%20riding%20a%20horse.png"
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
init_image.save("horse.png")

# let's specify a prompt
source_prompt = "An astronaut riding a horse"
prompt = "An astronaut riding an elephant"

# call the pipeline
image = pipe(
    prompt=prompt,
    source_prompt=source_prompt,
    image=init_image,
    num_inference_steps=100,
    eta=0.1,
    strength=0.8,
    guidance_scale=2,
    source_guidance_scale=1,
).images[0]

image.save("horse_to_elephant.png")

# let's try another example
# See more samples at the original repo: https://github.com/ChenWu98/cycle-diffusion
url = (
    "https://raw.githubusercontent.com/ChenWu98/cycle-diffusion/main/data/dalle2/A%20black%20colored%20car.png"
)
response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((512, 512))
init_image.save("black.png")

source_prompt = "A black colored car"
prompt = "A blue colored car"

# call the pipeline
torch.manual_seed(0)
image = pipe(
    prompt=prompt,
    source_prompt=source_prompt,
    image=init_image,
    num_inference_steps=100,
    eta=0.1,
    strength=0.85,
    guidance_scale=3,
    source_guidance_scale=1,
).images[0]

image.save("black_to_blue.png")  encode_prompt  < source > ( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None  )   Parameters   prompt (str or List[str], optional) —
prompt to be encoded
device — (torch.device):
torch device   num_images_per_prompt (int) —
number of images that should be generated per prompt   do_classifier_free_guidance (bool) —
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. 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.   lora_scale (float, optional) —
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.   clip_skip (int, optional) —
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.    Encodes the prompt into text encoder hidden states.  StableDiffusionPiplineOutput  class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput  < source > ( images: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray] nsfw_content_detected: typing.Optional[typing.List[bool]]  )   Parameters   images (List[PIL.Image.Image] or np.ndarray) —
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels).   nsfw_content_detected (List[bool]) —
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
None if safety checking could not be performed.    Output class for Stable Diffusion pipelines.