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

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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.

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.