--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor. output: url: images/sd21_base.png base_model: stabilityai/stable-diffusion-2-1-base instance_prompt: null license: mit --- # TCD-SD21-LoRA ## Model description Official SD21(base) Model of the paper [Trajectory Consistency Distillation](https://arxiv.org/abs/2402.19159). For more usage please found at [Project Page](https://huggingface.co/h1t/TCD-SDXL-LoRA/) Here is a simple example: ```python import torch from diffusers import StableDiffusionPipeline, TCDScheduler device = "cuda" base_model_id = "stabilityai/stable-diffusion-2-1-base" tcd_lora_id = "h1t/TCD-SD21-base-LoRA" pipe = StableDiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to(device) pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() prompt = "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor." image = pipe( prompt=prompt, num_inference_steps=4, guidance_scale=0, # Eta (referred to as `gamma` in the paper) is used to control the stochasticity in every step. # A value of 0.3 often yields good results. # We recommend using a higher eta when increasing the number of inference steps. eta=0.3, generator=torch.Generator(device=device).manual_seed(0), ).images[0] ``` ![sd21_base.png](https://cdn-uploads.huggingface.co/production/uploads/630b77f68b327c7b8b98c409/ifzBOlPA7E4IKkysMpelC.png) ## Download model Weights for this model are available in Safetensors format. [Download](/h1t/TCD-SD21-base-LoRA/tree/main) them in the Files & versions tab.