Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
5.54k
Tips
The Semantic Guidance pipeline can be used with any Stable Diffusion checkpoint.
Run Semantic Guidance
The interface of SemanticStableDiffusionPipeline provides several additional parameters to influence the image generation.
Exemplary usage may look like this:
Copied
import torch
from diffusers import SemanticStableDiffusionPipeline
pipe = SemanticStableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
out = pipe(
prompt="a photo of the face of a woman",
num_images_per_prompt=1,
guidance_scale=7,
editing_prompt=[
"smiling, smile", # Concepts to apply
"glasses, wearing glasses",
"curls, wavy hair, curly hair",
"beard, full beard, mustache",
],
reverse_editing_direction=[False, False, False, False], # Direction of guidance i.e. increase all concepts
edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept
edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept
edit_threshold=[
0.99,
0.975,
0.925,
0.96,
], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance
edit_mom_beta=0.6, # Momentum beta
edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other
)
For more examples check the Colab notebook.
StableDiffusionSafePipelineOutput
class diffusers.pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput
<
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). PIL images or numpy array present the denoised images of the diffusion pipeline.
nsfw_content_detected (List[bool]) —
List of flags denoting whether the corresponding generated image likely represents “not-safe-for-work”
(nsfw) content, or None if safety checking could not be performed.
Output class for Stable Diffusion pipelines.
SemanticStableDiffusionPipeline
class diffusers.SemanticStableDiffusionPipeline
<
source
>
(
vae: AutoencoderKL
text_encoder: CLIPTextModel
tokenizer: CLIPTokenizer
unet: UNet2DConditionModel
scheduler: KarrasDiffusionSchedulers
safety_checker: StableDiffusionSafetyChecker
feature_extractor: CLIPImageProcessor
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) —