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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, mo... |
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 out... |
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 ) → Kan... |
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 |
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