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

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init_image = load_image(img_url).resize((512, 512))
mask_image = load_image(mask_url).resize((512, 512))

repo_id = "stabilityai/stable-diffusion-2-inpainting"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3) Super-resolution Copied from diffusers import StableDiffusionUpscalePipeline
from diffusers.utils import load_image, make_image_grid
import torch

# load model and scheduler
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")

# let's download an image
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
low_res_img = load_image(url)
low_res_img = low_res_img.resize((128, 128))
prompt = "a white cat"
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
make_image_grid([low_res_img.resize((512, 512)), upscaled_image.resize((512, 512))], rows=1, cols=2) Depth-to-image Copied import torch
from diffusers import StableDiffusionDepth2ImgPipeline
from diffusers.utils import load_image, make_image_grid

pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth",
torch_dtype=torch.float16,
).to("cuda")


url = "http://images.cocodataset.org/val2017/000000039769.jpg"
init_image = load_image(url)
prompt = "two tigers"
negative_prompt = "bad, deformed, ugly, bad anotomy"
image = pipe(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
Kandinsky 2.2 Kandinsky 2.2 is created by Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, and Denis Dimitrov. The description from it’s GitHub page is: Kandinsky 2.2 brings substantial improvements upon its predecessor, Kandinsky 2.1, by introducing a new, more powerful image encoder - CLIP-ViT-G and the ControlNet support. The switch to CLIP-ViT-G as the image encoder significantly increases the model’s capability to generate more aesthetic pictures and better understand text, thus enhancing the model’s overall performance. The addition of the ControlNet mechanism allows the model to effectively control the process of generating images. This leads to more accurate and visually appealing outputs and opens new possibilities for text-guided image manipulation. The original codebase can be found at ai-forever/Kandinsky-2. Check out the Kandinsky Community organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting. 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. KandinskyV22PriorPipeline class diffusers.KandinskyV22PriorPipeline < source > ( prior: PriorTransformer image_encoder: CLIPVisionModelWithProjection text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer scheduler: UnCLIPScheduler image_processor: CLIPImageProcessor ) Parameters prior (PriorTransformer) —
The canonincal unCLIP prior to approximate the image embedding from the text embedding. image_encoder (CLIPVisionModelWithProjection) —
Frozen image-encoder. text_encoder (CLIPTextModelWithProjection) —
Frozen text-encoder. tokenizer (CLIPTokenizer) —
Tokenizer of class
CLIPTokenizer. scheduler (UnCLIPScheduler) —
A scheduler to be used in combination with prior to generate image embedding. image_processor (CLIPImageProcessor) —
A image_processor to be used to preprocess image from clip. Pipeline for generating image prior for Kandinsky This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( prompt: Union negative_prompt: Union = None num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None guidance_scale: float = 4.0 output_type: Optional = 'pt' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] ) → KandinskyPriorPipelineOutput or tuple Parameters prompt (str or List[str]) —
The prompt or prompts to guide the image generation. negative_prompt (str or List[str], optional) —
The prompt or prompts not to guide the image generation. 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. num_inference_steps (int, optional, defaults to 100) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. generator (torch.Generator or List[torch.Generator], optional) —
One or a list of torch generator(s)
to make generation deterministic. latents (torch.FloatTensor, optional) —
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random generator. guidance_scale (float, optional, defaults to 4.0) —
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. output_type (str, optional, defaults to "pt") —
The output format of the generate image. Choose between: "np" (np.array) or "pt"
(torch.Tensor). return_dict (bool, optional, defaults to True) —
Whether or not to return a ImagePipelineOutput 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
KandinskyPriorPipelineOutput or tuple
Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch

>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> image_emb, negative_image_emb = pipe_prior(prompt).to_tuple()

>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=negative_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png") interpolate < source > ( images_and_prompts: List weights: List num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None negative_prior_prompt: Optional = None negative_prompt: str = '' guidance_scale: float = 4.0 device = None ) → KandinskyPriorPipelineOutput or tuple Parameters images_and_prompts (List[Union[str, PIL.Image.Image, torch.FloatTensor]]) —
list of prompts and images to guide the image generation.
weights — (List[float]):
list of weights for each condition in images_and_prompts num_images_per_prompt (int, optional, defaults to 1) —
The number of images to generate per prompt. num_inference_steps (int, optional, defaults to 100) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the

init_image = load_image(img_url).resize((512, 512))

mask_image = load_image(mask_url).resize((512, 512))

repo_id = "stabilityai/stable-diffusion-2-inpainting"

pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, revision="fp16")

pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)

pipe = pipe.to("cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"

image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]

make_image_grid([init_image, mask_image, image], rows=1, cols=3) Super-resolution Copied from diffusers import StableDiffusionUpscalePipeline

from diffusers.utils import load_image, make_image_grid

import torch

# load model and scheduler

model_id = "stabilityai/stable-diffusion-x4-upscaler"

pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)

pipeline = pipeline.to("cuda")

# let's download an image

url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"

low_res_img = load_image(url)

low_res_img = low_res_img.resize((128, 128))

prompt = "a white cat"

upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]

make_image_grid([low_res_img.resize((512, 512)), upscaled_image.resize((512, 512))], rows=1, cols=2) Depth-to-image Copied import torch

from diffusers import StableDiffusionDepth2ImgPipeline

from diffusers.utils import load_image, make_image_grid

pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(

"stabilityai/stable-diffusion-2-depth",

torch_dtype=torch.float16,

).to("cuda")

url = "http://images.cocodataset.org/val2017/000000039769.jpg"

init_image = load_image(url)

prompt = "two tigers"

negative_prompt = "bad, deformed, ugly, bad anotomy"

image = pipe(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0]

make_image_grid([init_image, image], rows=1, cols=2)

Kandinsky 2.2 Kandinsky 2.2 is created by Arseniy Shakhmatov, Anton Razzhigaev, Aleksandr Nikolich, Vladimir Arkhipkin, Igor Pavlov, Andrey Kuznetsov, and Denis Dimitrov. The description from it’s GitHub page is: Kandinsky 2.2 brings substantial improvements upon its predecessor, Kandinsky 2.1, by introducing a new, more powerful image encoder - CLIP-ViT-G and the ControlNet support. The switch to CLIP-ViT-G as the image encoder significantly increases the model’s capability to generate more aesthetic pictures and better understand text, thus enhancing the model’s overall performance. The addition of the ControlNet mechanism allows the model to effectively control the process of generating images. This leads to more accurate and visually appealing outputs and opens new possibilities for text-guided image manipulation. The original codebase can be found at ai-forever/Kandinsky-2. Check out the Kandinsky Community organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting. 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. KandinskyV22PriorPipeline class diffusers.KandinskyV22PriorPipeline < source > ( prior: PriorTransformer image_encoder: CLIPVisionModelWithProjection text_encoder: CLIPTextModelWithProjection tokenizer: CLIPTokenizer scheduler: UnCLIPScheduler image_processor: CLIPImageProcessor ) Parameters prior (PriorTransformer) —

The canonincal unCLIP prior to approximate the image embedding from the text embedding. image_encoder (CLIPVisionModelWithProjection) —

Frozen image-encoder. text_encoder (CLIPTextModelWithProjection) —

Frozen text-encoder. tokenizer (CLIPTokenizer) —

Tokenizer of class

CLIPTokenizer. scheduler (UnCLIPScheduler) —

A scheduler to be used in combination with prior to generate image embedding. image_processor (CLIPImageProcessor) —

A image_processor to be used to preprocess image from clip. Pipeline for generating image prior for Kandinsky This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the

library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) __call__ < source > ( prompt: Union negative_prompt: Union = None num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None guidance_scale: float = 4.0 output_type: Optional = 'pt' return_dict: bool = True callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] ) → KandinskyPriorPipelineOutput or tuple Parameters prompt (str or List[str]) —

The prompt or prompts to guide the image generation. negative_prompt (str or List[str], optional) —

The prompt or prompts not to guide the image generation. 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. num_inference_steps (int, optional, defaults to 100) —

The number of denoising steps. More denoising steps usually lead to a higher quality image at the

expense of slower inference. generator (torch.Generator or List[torch.Generator], optional) —

One or a list of torch generator(s)

to make generation deterministic. latents (torch.FloatTensor, optional) —

Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image

generation. Can be used to tweak the same generation with different prompts. If not provided, a latents

tensor will ge generated by sampling using the supplied random generator. guidance_scale (float, optional, defaults to 4.0) —

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. output_type (str, optional, defaults to "pt") —

The output format of the generate image. Choose between: "np" (np.array) or "pt"

(torch.Tensor). return_dict (bool, optional, defaults to True) —

Whether or not to return a ImagePipelineOutput 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

KandinskyPriorPipelineOutput or tuple

Function invoked when calling the pipeline for generation. Examples: Copied >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline

>>> import torch

>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")

>>> pipe_prior.to("cuda")

>>> prompt = "red cat, 4k photo"

>>> image_emb, negative_image_emb = pipe_prior(prompt).to_tuple()

>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")

>>> pipe.to("cuda")

>>> image = pipe(

... image_embeds=image_emb,

... negative_image_embeds=negative_image_emb,

... height=768,

... width=768,

... num_inference_steps=50,

... ).images

>>> image[0].save("cat.png") interpolate < source > ( images_and_prompts: List weights: List num_images_per_prompt: int = 1 num_inference_steps: int = 25 generator: Union = None latents: Optional = None negative_prior_prompt: Optional = None negative_prompt: str = '' guidance_scale: float = 4.0 device = None ) → KandinskyPriorPipelineOutput or tuple Parameters images_and_prompts (List[Union[str, PIL.Image.Image, torch.FloatTensor]]) —

list of prompts and images to guide the image generation.

weights — (List[float]):

list of weights for each condition in images_and_prompts num_images_per_prompt (int, optional, defaults to 1) —

The number of images to generate per prompt. num_inference_steps (int, optional, defaults to 100) —

The number of denoising steps. More denoising steps usually lead to a higher quality image at the