text
stringlengths 0
5.54k
|
|---|
tensor will ge generated by sampling using the supplied random generator. |
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. |
cross_attention_guidance_amount (float, defaults to 0.1) — |
Amount of guidance needed from the reference cross-attention maps. |
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 StableDiffusionPipelineOutput instead of a |
plain tuple. |
callback (Callable, optional) — |
A function that will be called every callback_steps steps during inference. The function will be |
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 will be called. If not specified, the callback will be |
called at every step. |
lambda_auto_corr (float, optional, defaults to 20.0) — |
Lambda parameter to control auto correction |
lambda_kl (float, optional, defaults to 20.0) — |
Lambda parameter to control Kullback–Leibler divergence output |
num_reg_steps (int, optional, defaults to 5) — |
Number of regularization loss steps |
num_auto_corr_rolls (int, optional, defaults to 5) — |
Number of auto correction roll steps |
Function used to generate inverted latents given a prompt and image. |
Examples: |
Copied |
>>> import torch |
>>> from transformers import BlipForConditionalGeneration, BlipProcessor |
>>> from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionPix2PixZeroPipeline |
>>> import requests |
>>> from PIL import Image |
>>> captioner_id = "Salesforce/blip-image-captioning-base" |
>>> processor = BlipProcessor.from_pretrained(captioner_id) |
>>> model = BlipForConditionalGeneration.from_pretrained( |
... captioner_id, torch_dtype=torch.float16, low_cpu_mem_usage=True |
... ) |
>>> sd_model_ckpt = "CompVis/stable-diffusion-v1-4" |
>>> pipeline = StableDiffusionPix2PixZeroPipeline.from_pretrained( |
... sd_model_ckpt, |
... caption_generator=model, |
... caption_processor=processor, |
... torch_dtype=torch.float16, |
... safety_checker=None, |
... ) |
>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) |
>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config) |
>>> pipeline.enable_model_cpu_offload() |
>>> img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png" |
>>> raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512)) |
>>> # generate caption |
>>> caption = pipeline.generate_caption(raw_image) |
>>> # "a photography of a cat with flowers and dai dai daie - daie - daie kasaii" |
>>> inv_latents = pipeline.invert(caption, image=raw_image).latents |
>>> # we need to generate source and target embeds |
>>> source_prompts = ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"] |
>>> target_prompts = ["a dog sitting on the street", "a dog playing in the field", "a face of a dog"] |
>>> source_embeds = pipeline.get_embeds(source_prompts) |
>>> target_embeds = pipeline.get_embeds(target_prompts) |
>>> # the latents can then be used to edit a real image |
>>> image = pipeline( |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.