Datasets:
sayakpaul
/
diffusers-qa-chatbot-artifacts

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
5.54k
Copied output_image = pipeline(image=init_image, mask_image=mask, **prior_output, height=768, width=768, num_inference_steps=150).images[0]
mask = Image.fromarray((mask*255).astype('uint8'), 'L')
make_image_grid([init_image, mask, output_image], rows=1, cols=3)
</hfoption>
</hfoptions>
You can also use the end-to-end KandinskyInpaintCombinedPipeline and KandinskyV22InpaintCombinedPipeline to call the prior and decoder pipelines together under the hood. Use the AutoPipelineForInpainting for this:
<hfoptions id="inpaint">
<hfoption id="Kandinsky 2.1">
Copied import torch
import numpy as np
from PIL import Image
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid
pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
init_image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
mask = np.zeros((768, 768), dtype=np.float32)
# mask area above cat's head
mask[:250, 250:-250] = 1
prompt = "a hat"
output_image = pipe(prompt=prompt, image=init_image, mask_image=mask).images[0]
mask = Image.fromarray((mask*255).astype('uint8'), 'L')
make_image_grid([init_image, mask, output_image], rows=1, cols=3)
</hfoption>
<hfoption id="Kandinsky 2.2">
Copied import torch
import numpy as np
from PIL import Image
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid
pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
init_image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
mask = np.zeros((768, 768), dtype=np.float32)
# mask area above cat's head
mask[:250, 250:-250] = 1
prompt = "a hat"
output_image = pipe(prompt=prompt, image=original_image, mask_image=mask).images[0]
mask = Image.fromarray((mask*255).astype('uint8'), 'L')
make_image_grid([init_image, mask, output_image], rows=1, cols=3)
</hfoption>
</hfoptions>
Interpolation Interpolation allows you to explore the latent space between the image and text embeddings which is a cool way to see some of the prior model’s intermediate outputs. Load the prior pipeline and two images you’d like to interpolate:
<hfoptions id="interpolate">
<hfoption id="Kandinsky 2.1">
Copied from diffusers import KandinskyPriorPipeline, KandinskyPipeline
from diffusers.utils import load_image, make_image_grid
import torch
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
img_1 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
img_2 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg")
make_image_grid([img_1.resize((512,512)), img_2.resize((512,512))], rows=1, cols=2)
</hfoption>
<hfoption id="Kandinsky 2.2">
Copied from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline
from diffusers.utils import load_image, make_image_grid
import torch
prior_pipeline = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
img_1 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
img_2 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg")
make_image_grid([img_1.resize((512,512)), img_2.resize((512,512))], rows=1, cols=2)
</hfoption>
</hfoptions>
a cat Van Gogh's Starry Night painting Specify the text or images to interpolate, and set the weights for each text or image. Experiment with the weights to see how they affect the interpolation! Copied images_texts = ["a cat", img_1, img_2]
weights = [0.3, 0.3, 0.4] Call the interpolate function to generate the embeddings, and then pass them to the pipeline to generate the image:
<hfoptions id="interpolate">
<hfoption id="Kandinsky 2.1">
Copied # prompt can be left empty
prompt = ""
prior_out = prior_pipeline.interpolate(images_texts, weights)
pipeline = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
image = pipeline(prompt, **prior_out, height=768, width=768).images[0]
image
</hfoption>
<hfoption id="Kandinsky 2.2">
Copied # prompt can be left empty
prompt = ""
prior_out = prior_pipeline.interpolate(images_texts, weights)
pipeline = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
image = pipeline(prompt, **prior_out, height=768, width=768).images[0]
image
</hfoption>