license: creativeml-openrail-m | |
base_model: runwayml/stable-diffusion-v1-5 | |
tags: | |
- stable-diffusion | |
- stable-diffusion-diffusers | |
- text-to-image | |
- diffusers | |
inference: true | |
# LoRA text2image fine-tuning - https://huggingface.co/pcuenq/pokemon-lora | |
These are LoRA adaption weights trained on base model https://huggingface.co/runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. | |
## How to Use | |
The script below loads the base model, then applies the LoRA weights and performs inference: | |
```Python | |
import torch | |
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler | |
from huggingface_hub import model_info | |
# LoRA weights ~3 MB | |
model_path = "pcuenq/pokemon-lora" | |
info = model_info(model_path) | |
model_base = info.cardData["base_model"] | |
pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16) | |
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
pipe.unet.load_attn_procs(model_path) | |
pipe.to("cuda") | |
image = pipe("Green pokemon with menacing face", num_inference_steps=25).images[0] | |
image.save("green_pokemon.png") | |
``` | |
Please, check [our blog post](https://huggingface.co/blog/lora) or [documentation](https://huggingface.co/docs/diffusers/v0.15.0/en/training/lora#text-to-image-inference) for more details. | |
## Example Images | |
![img_0](./image_0.png) | |
![img_1](./image_1.png) | |
![img_2](./image_2.png) | |
![img_3](./image_3.png) | |