Text-to-Image
Diffusers
TensorBoard
Safetensors
Sana
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
dreambooth
diffusers-training
lora
sana-diffusers
template:sd-lora
Instructions to use ainjarts/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ainjarts/output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("darkstorm2150/Protogen_x3.4_Official_Release", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("ainjarts/output") prompt = "a photo of ohwx woman" image = pipe(prompt).images[0] - Sana
How to use ainjarts/output with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://ainjarts/output") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| { | |
| "crop_size": { | |
| "height": 224, | |
| "width": 224 | |
| }, | |
| "do_center_crop": true, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "CLIPFeatureExtractor", | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "CLIPImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 224 | |
| }, | |
| "use_square_size": false | |
| } | |