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.
For more usage please found at Project Page
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]
```
Download model
Weights for this model are available in Safetensors format.
Download them in the Files & versions tab.