Instructions to use phil329/face_lora_sd15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use phil329/face_lora_sd15 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("phil329/face_lora_sd15") prompt = "A young woman with smile, wearing a purple hat." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - stable-diffusion | |
| - lora | |
| - diffusers | |
| - template:sd-lora | |
| widget: | |
| - text: A young woman with smile, wearing a purple hat. | |
| parameters: | |
| negative_prompt: >- | |
| worst quality, low quality, bad anatomy, watermark, text, blurry, cartoon, | |
| unreal | |
| output: | |
| url: images/output.png | |
| base_model: runwayml/stable-diffusion-v1-5 | |
| instance_prompt: null | |
| license: mit | |
| # pytorch_lora_weights.safetensors | |
| <Gallery /> | |
| ## Model description | |
| This model is a fine-tuned version of the Stable Diffusion architecture, leveraging the Low-Rank Adaptation (LoRA) technique. It has been trained using the CelebA-HQ and FFHQ datasets, both renowned for their high-quality images of human faces. | |
| ### Training Details: | |
| - **Base Model**: Stable Diffusion | |
| - **Adaptation Technique**: Low-Rank Adaptation (LoRA) | |
| - **Datasets**: CelebA-HQ (30,000 images), FFHQ (70,000 images) | |
| - **Resolution**: resolution : 512*512 fine-tuning for detailed facial synthesis | |
| ### Example Usages: | |
| ```py | |
| import torch | |
| from diffusers import StableDiffusionPipeline,UNet2DConditionModel | |
| pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("cuda") | |
| pipeline.load_lora_weights("phil329/face_lora_sd15", weight_name="pytorch_lora_weights.safetensors") | |
| NEGATIVE_PROMPT = "worst quality, low quality, bad anatomy, watermark, text, blurry, cartoon, unreal" | |
| text = 'A young woman with smile, wearing a purple hat.' | |
| lora_image = pipeline(text,negative_prompt=NEGATIVE_PROMPT).images[0] | |
| display(lora_image) | |
| ``` | |
| ### Results | |
| We use four prompts as follows: | |
| - 'A young woman with smile, wearing a purple hat.' | |
| - 'A middle-aged man,beard ,attractive' | |
| - 'A girl with long blonde hair' | |
| - 'An young man with curry hair' | |
| The **negative prompt** are the same as the example codes. All the results are randomly generated and **not** cherry-picked. | |
| If the generation effect is not good, try adding a negative prompt, or try different prompts and seeds. | |
|  | |
| ## Download model | |
| Weights for this model are available in Safetensors format. | |
| [Download](/phil329/face_lora_sd15/tree/main) them in the Files & versions tab. | |