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).images |
image = refiner( |
prompt=prompt, |
num_inference_steps=40, |
denoising_start=0.8, |
image=image, |
).images[0] |
image default base model ensemble of expert denoisers The refiner model can also be used for inpainting in the StableDiffusionXLInpaintPipeline: Copied from diffusers import StableDiffusionXLInpaintPipeline |
from diffusers.utils import load_image, make_image_grid |
import torch |
base = StableDiffusionXLInpaintPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
).to("cuda") |
refiner = StableDiffusionXLInpaintPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-refiner-1.0", |
text_encoder_2=base.text_encoder_2, |
vae=base.vae, |
torch_dtype=torch.float16, |
use_safetensors=True, |
variant="fp16", |
).to("cuda") |
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" |
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" |
init_image = load_image(img_url) |
mask_image = load_image(mask_url) |
prompt = "A majestic tiger sitting on a bench" |
num_inference_steps = 75 |
high_noise_frac = 0.7 |
image = base( |
prompt=prompt, |
image=init_image, |
mask_image=mask_image, |
num_inference_steps=num_inference_steps, |
denoising_end=high_noise_frac, |
output_type="latent", |
).images |
image = refiner( |
prompt=prompt, |
image=image, |
mask_image=mask_image, |
num_inference_steps=num_inference_steps, |
denoising_start=high_noise_frac, |
).images[0] |
make_image_grid([init_image, mask_image, image.resize((512, 512))], rows=1, cols=3) This ensemble of expert denoisers method works well for all available schedulers! Base to refiner model SDXL gets a boost in image quality by using the refiner model to add additional high-quality details to the fully-denoised image fr... |
import torch |
base = DiffusionPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
).to("cuda") |
refiner = DiffusionPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-refiner-1.0", |
text_encoder_2=base.text_encoder_2, |
vae=base.vae, |
torch_dtype=torch.float16, |
use_safetensors=True, |
variant="fp16", |
).to("cuda") Generate an image from the base model, and set the model output to latent space: Copied prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
image = base(prompt=prompt, output_type="latent").images[0] Pass the generated image to the refiner model: Copied image = refiner(prompt=prompt, image=image[None, :]).images[0] base model base model + refiner model For inpainting, load the base and the refiner model in the StableDiffusionXLInpaintPipeline, remove t... |
import torch |
pipe = StableDiffusionXLPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
).to("cuda") |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
image = pipe( |
prompt=prompt, |
negative_original_size=(512, 512), |
negative_target_size=(1024, 1024), |
).images[0] Images negatively conditioned on image resolutions of (128, 128), (256, 256), and (512, 512). Crop conditioning Images generated by previous Stable Diffusion models may sometimes appear to be cropped. This is because images are actually cropped during training so that all the images in a batch have the sa... |
import torch |
pipeline = StableDiffusionXLPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
).to("cuda") |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
image = pipeline(prompt=prompt, crops_coords_top_left=(256, 0)).images[0] |
image You can also specify negative cropping coordinates to steer generation away from certain cropping parameters: Copied from diffusers import StableDiffusionXLPipeline |
import torch |
pipe = StableDiffusionXLPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
).to("cuda") |
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" |
image = pipe( |
prompt=prompt, |
negative_original_size=(512, 512), |
negative_crops_coords_top_left=(0, 0), |
negative_target_size=(1024, 1024), |
).images[0] |
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