| tags: | |
| - text-to-image | |
| - stable-diffusion | |
| - lora | |
| - diffusers | |
| base_model: runwayml/stable-diffusion-v1-5 | |
| license: mit | |
| library_name: diffusers | |
| # Model description | |
| Official TCD LoRA for Stable Diffusion v1.5 of the paper [Trajectory Consistency Distillation](https://arxiv.org/abs/2402.19159). | |
| For more usage please found at [Project Page](https://mhh0318.github.io/tcd/) | |
| Here is a simple example: | |
| ` | |
| ```python | |
| import torch | |
| from diffusers import StableDiffusionPipeline, TCDScheduler | |
| device = "cuda" | |
| base_model_id = "runwayml/stable-diffusion-v1-5" | |
| tcd_lora_id = "h1t/TCD-SD15-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(42), | |
| ).images[0] | |
| ``` | |
|  |