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"_class_name": "StableDiffusionPipeline", |
"_diffusers_version": "0.6.0", |
"feature_extractor": [ |
"transformers", |
"CLIPImageProcessor" |
], |
"safety_checker": [ |
"stable_diffusion", |
"StableDiffusionSafetyChecker" |
], |
"scheduler": [ |
"diffusers", |
"PNDMScheduler" |
], |
"text_encoder": [ |
"transformers", |
"CLIPTextModel" |
], |
"tokenizer": [ |
"transformers", |
"CLIPTokenizer" |
], |
"unet": [ |
"diffusers", |
"UNet2DConditionModel" |
], |
"vae": [ |
"diffusers", |
"AutoencoderKL" |
] |
} |
Performing inference with LCM-LoRA Latent Consistency Models (LCM) enable quality image generation in typically 2-4 steps making it possible to use diffusion models in almost real-time settings. From the official website: LCMs can be distilled from any pre-trained Stable Diffusion (SD) in only 4,000 training steps (~32... |
This way, we don’t have to train the full model and keep the number of trainable parameters manageable. The resulting LoRAs can then be applied to any fine-tuned version of the model without distilling them separately. |
Additionally, the LoRAs can be applied to image-to-image, ControlNet/T2I-Adapter, inpainting, AnimateDiff etc. |
The LCM-LoRA can also be combined with other LoRAs to generate styled images in very few steps (4-8). LCM-LoRAs are available for stable-diffusion-v1-5, stable-diffusion-xl-base-1.0, and the SSD-1B model. All the checkpoints can be found in this collection. For more details about LCM-LoRA, refer to the technical report... |
LCM-LoRAs are similar to other Stable Diffusion LoRAs so they can be used with any DiffusionPipeline that supports LoRAs. Load the task specific pipeline and model. Set the scheduler to LCMScheduler. Load the LCM-LoRA weights for the model. Reduce the guidance_scale between [1.0, 2.0] and set the num_inference_steps be... |
from diffusers import DiffusionPipeline, LCMScheduler |
pipe = DiffusionPipeline.from_pretrained( |
"stabilityai/stable-diffusion-xl-base-1.0", |
variant="fp16", |
torch_dtype=torch.float16 |
).to("cuda") |
# set scheduler |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
# load LCM-LoRA |
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") |
prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" |
generator = torch.manual_seed(42) |
image = pipe( |
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=1.0 |
).images[0] Notice that we use only 4 steps for generation which is way less than what’s typically used for standard SDXL. You may have noticed that we set guidance_scale=1.0, which disables classifer-free-guidance. This is because the LCM-LoRA is trained with guidance, so the batch size does not have to be doubled in... |
pipe = DiffusionPipeline.from_pretrained( |
"Linaqruf/animagine-xl", |
variant="fp16", |
torch_dtype=torch.float16 |
).to("cuda") |
# set scheduler |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
# load LCM-LoRA |
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl") |
prompt = "face focus, cute, masterpiece, best quality, 1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck" |
generator = torch.manual_seed(0) |
image = pipe( |
prompt=prompt, num_inference_steps=4, generator=generator, guidance_scale=1.0 |
).images[0] Image-to-image LCM-LoRA can be applied to image-to-image tasks too. Let’s look at how we can perform image-to-image generation with LCMs. For this example we’ll use the dreamshaper-7 model and the LCM-LoRA for stable-diffusion-v1-5 . Copied import torch |
from diffusers import AutoPipelineForImage2Image, LCMScheduler |
from diffusers.utils import make_image_grid, load_image |
pipe = AutoPipelineForImage2Image.from_pretrained( |
"Lykon/dreamshaper-7", |
torch_dtype=torch.float16, |
variant="fp16", |
).to("cuda") |
# set scheduler |
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
# load LCM-LoRA |
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5") |
# 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, |
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