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prompt = "Astronauts in a jungle, cold color palette, muted colors, detailed, 8k" |
# pass prompt and image to pipeline |
generator = torch.manual_seed(0) |
image = pipe( |
prompt, |
image=init_image, |
num_inference_steps=4, |
guidance_scale=7.5, |
strength=0.5, |
generator=generator |
).images[0] |
make_image_grid([init_image, image], rows=1, cols=2) You can get different results based on your prompt and the image you provide. To get the best results, we recommend trying different values for num_inference_steps, strength, and guidance_scale parameters and choose the best one. Combine with style LoRAs LCMs can b... |
import torch |
unet = UNet2DConditionModel.from_pretrained( |
"latent-consistency/lcm-sdxl", |
torch_dtype=torch.float16, |
variant="fp16", |
) |
pipe = StableDiffusionXLPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16", |
).to("cuda") |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
pipe.load_lora_weights("TheLastBen/Papercut_SDXL", weight_name="papercut.safetensors", adapter_name="papercut") |
prompt = "papercut, a cute fox" |
generator = torch.manual_seed(0) |
image = pipe( |
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=8.0 |
).images[0] |
image ControlNet/T2I-Adapter Let’s look at how we can perform inference with ControlNet/T2I-Adapter and a LCM. ControlNet For this example, we’ll use the LCM_Dreamshaper_v7 model with canny ControlNet, but the same steps can be applied to other LCM models as well. Copied import torch |
import cv2 |
import numpy as np |
from PIL import Image |
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, LCMScheduler |
from diffusers.utils import load_image, make_image_grid |
image = load_image( |
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png" |
).resize((512, 512)) |
image = np.array(image) |
low_threshold = 100 |
high_threshold = 200 |
image = cv2.Canny(image, low_threshold, high_threshold) |
image = image[:, :, None] |
image = np.concatenate([image, image, image], axis=2) |
canny_image = Image.fromarray(image) |
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) |
pipe = StableDiffusionControlNetPipeline.from_pretrained( |
"SimianLuo/LCM_Dreamshaper_v7", |
controlnet=controlnet, |
torch_dtype=torch.float16, |
safety_checker=None, |
).to("cuda") |
# set scheduler |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
generator = torch.manual_seed(0) |
image = pipe( |
"the mona lisa", |
image=canny_image, |
num_inference_steps=4, |
generator=generator, |
).images[0] |
make_image_grid([canny_image, image], rows=1, cols=2) The inference parameters in this example might not work for all examples, so we recommend trying different values for the `num_inference_steps`, `guidance_scale`, `controlnet_conditioning_scale`, and `cross_attention_kwargs` parameters and choosing the best one. T... |
import cv2 |
import numpy as np |
from PIL import Image |
from diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler |
from diffusers.utils import load_image, make_image_grid |
# Prepare image |
# Detect the canny map in low resolution to avoid high-frequency details |
image = load_image( |
"https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg" |
).resize((384, 384)) |
image = np.array(image) |
low_threshold = 100 |
high_threshold = 200 |
image = cv2.Canny(image, low_threshold, high_threshold) |
image = image[:, :, None] |
image = np.concatenate([image, image, image], axis=2) |
canny_image = Image.fromarray(image).resize((1024, 1216)) |
# load adapter |
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda") |
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