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image = pipe( |
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=8.0 |
).images[0] Notice that we use only 4 steps for generation which is way less than what’s typically used for standard SDXL. Some details to keep in mind: To perform classifier-free guidance, batch size is usually doubled inside the pipeline. LCM, however, applies guidance using guidance embeddings, so the batch size does not have to be doubled in this case. This leads to a faster inference time, with the drawback that negative prompts don’t have any effect on the denoising process. The UNet was trained using the [3., 13.] guidance scale range. So, that is the ideal range for guidance_scale. However, disabling guidance_scale using a value of 1.0 is also effective in most cases. Image-to-image LCMs can be applied to image-to-image tasks too. For this example, we’ll use the LCM_Dreamshaper_v7 model, but the same steps can be applied to other LCM models as well. Copied import torch |
from diffusers import AutoPipelineForImage2Image, UNet2DConditionModel, LCMScheduler |
from diffusers.utils import make_image_grid, load_image |
unet = UNet2DConditionModel.from_pretrained( |
"SimianLuo/LCM_Dreamshaper_v7", |
subfolder="unet", |
torch_dtype=torch.float16, |
) |
pipe = AutoPipelineForImage2Image.from_pretrained( |
"Lykon/dreamshaper-7", |
unet=unet, |
torch_dtype=torch.float16, |
variant="fp16", |
).to("cuda") |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
# prepare image |
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/img2img-init.png" |
init_image = load_image(url) |
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 be used with other styled LoRAs to generate styled-images in very few steps (4-8). In the following example, we’ll use the papercut LoRA. Copied from diffusers import StableDiffusionXLPipeline, UNet2DConditionModel, LCMScheduler |
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. T2I-Adapter This example shows how to use the lcm-sdxl with the Canny T2I-Adapter. Copied import torch |
import cv2 |
import numpy as np |
from PIL import Image |
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