Open taotaow opened 1 year ago
lcm lora can reduce inference steps to 2~8，it's fast,even realtime! https://huggingface.co/blog/lcm_lora
from diffusers import DiffusionPipeline, LCMScheduler import torch
model_id = "wavymulder/collage-diffusion" lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(lcm_lora_id) pipe.to(device="cuda", dtype=torch.float16)
prompt = "collage style kid sits looking at the night sky, full of stars"
generator = torch.Generator(device=pipe.device).manual_seed(1337) images = pipe( prompt=prompt, generator=generator, negative_prompt=negative_prompt, num_inference_steps=4, guidance_scale=1, ).images[0]
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What would your feature do ?
lcm lora can reduce inference steps to 2~8，it's fast,even realtime! https://huggingface.co/blog/lcm_lora
Proposed workflow
from diffusers import DiffusionPipeline, LCMScheduler import torch
model_id = "wavymulder/collage-diffusion" lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(lcm_lora_id) pipe.to(device="cuda", dtype=torch.float16)
prompt = "collage style kid sits looking at the night sky, full of stars"
generator = torch.Generator(device=pipe.device).manual_seed(1337) images = pipe( prompt=prompt, generator=generator, negative_prompt=negative_prompt, num_inference_steps=4, guidance_scale=1, ).images[0]
Additional information
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