Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
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sent,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_encoder(text_input_ids.to(device), attention_mask=None)[0]
embeddings.append(prompt_embeds)
return torch.concatenate(embeddings, dim=0).mean(dim=0).unsqueeze(0)
source_embeddings = embed_captions(source_captions, tokenizer, text_encoder)
target_embeddings = embed_captions(target_captions, tokenizer, text_encoder)
And you’re done! Here is a Colab Notebook that you can use to interact with the entire process.
Now, you can use these embeddings directly while calling the pipeline:
Copied
from diffusers import DDIMScheduler
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
images = pipeline(
prompt,
source_embeds=source_embeddings,
target_embeds=target_embeddings,
num_inference_steps=50,
cross_attention_guidance_amount=0.15,
).images
images[0].save("edited_image_dog.png")
StableDiffusionPix2PixZeroPipeline
class diffusers.StableDiffusionPix2PixZeroPipeline
<
source
>
(
vae: AutoencoderKL
text_encoder: CLIPTextModel
tokenizer: CLIPTokenizer
unet: UNet2DConditionModel
scheduler: typing.Union[diffusers.schedulers.scheduling_ddpm.DDPMScheduler, diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler]
feature_extractor: CLIPFeatureExtractor
safety_checker: StableDiffusionSafetyChecker
inverse_scheduler: DDIMInverseScheduler
caption_generator: BlipForConditionalGeneration
caption_processor: BlipProcessor
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. Stable Diffusion uses the text portion of
CLIP, specifically
the clip-vit-large-patch14 variant.
tokenizer (CLIPTokenizer) —
Tokenizer of class
CLIPTokenizer.
unet (UNet2DConditionModel) — Conditional U-Net architecture to denoise the encoded image latents.
scheduler (SchedulerMixin) —
A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, EulerAncestralDiscreteScheduler, or DDPMScheduler.
safety_checker (StableDiffusionSafetyChecker) —
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the model card for details.
feature_extractor (CLIPFeatureExtractor) —
Model that extracts features from generated images to be used as inputs for the safety_checker.
requires_safety_checker (bool) —
Whether the pipeline requires a safety checker. We recommend setting it to True if you’re using the
pipeline publicly.
Pipeline for pixel-levl image editing using Pix2Pix Zero. Based on Stable Diffusion.
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__