Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
5.54k
feature_extractor=feature_extractor,
torch_dtype=torch.float16,
)
guided_pipeline.enable_attention_slicing()
guided_pipeline = guided_pipeline.to("cuda")
prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
generator = torch.Generator(device="cuda").manual_seed(0)
images = []
for i in range(4):
image = guided_pipeline(
prompt,
num_inference_steps=50,
guidance_scale=7.5,
clip_guidance_scale=100,
num_cutouts=4,
use_cutouts=False,
generator=generator,
).images[0]
images.append(image)
# save images locally
for i, img in enumerate(images):
img.save(f"./clip_guided_sd/image_{i}.png")
The images list contains a list of PIL images that can be saved locally or displayed directly in a google colab.
Generated images tend to be of higher qualtiy than natively using stable diffusion. E.g. the above script generates the following images:
.
One Step Unet
The dummy “one-step-unet” can be run as follows:
Copied
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="one_step_unet")
pipe()
Note: This community pipeline is not useful as a feature, but rather just serves as an example of how community pipelines can be added (see https://github.com/huggingface/diffusers/issues/841).
Stable Diffusion Interpolation
The following code can be run on a GPU of at least 8GB VRAM and should take approximately 5 minutes.
Copied
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
torch_dtype=torch.float16,
safety_checker=None, # Very important for videos...lots of false positives while interpolating
custom_pipeline="interpolate_stable_diffusion",
).to("cuda")
pipe.enable_attention_slicing()
frame_filepaths = pipe.walk(
prompts=["a dog", "a cat", "a horse"],
seeds=[42, 1337, 1234],
num_interpolation_steps=16,
output_dir="./dreams",
batch_size=4,
height=512,
width=512,
guidance_scale=8.5,
num_inference_steps=50,
)
The output of the walk(...) function returns a list of images saved under the folder as defined in output_dir. You can use these images to create videos of stable diffusion.
Please have a look at https://github.com/nateraw/stable-diffusion-videos for more in-detail information on how to create videos using stable diffusion as well as more feature-complete functionality.
Stable Diffusion Mega
The Stable Diffusion Mega Pipeline lets you use the main use cases of the stable diffusion pipeline in a single class.
Copied
#!/usr/bin/env python3
from diffusers import DiffusionPipeline
import PIL
import requests
from io import BytesIO
import torch
def download_image(url):
response = requests.get(url)
return PIL.Image.open(BytesIO(response.content)).convert("RGB")
pipe = DiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4",
custom_pipeline="stable_diffusion_mega",
torch_dtype=torch.float16,
)
pipe.to("cuda")
pipe.enable_attention_slicing()