Text-to-Image
Diffusers
TensorBoard
Safetensors
English
StableDiffusionPipeline
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use Pramodmunje/Text-to-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Pramodmunje/Text-to-Image with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Pramodmunje/Text-to-Image") prompt = "Draw a picture of two female boxers fighting each other." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| { | |
| "_class_name": "StableDiffusionPipeline", | |
| "_diffusers_version": "0.6.0", | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPImageProcessor" | |
| ], | |
| "safety_checker": [ | |
| "stable_diffusion", | |
| "StableDiffusionSafetyChecker" | |
| ], | |
| "scheduler": [ | |
| "diffusers", | |
| "PNDMScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "unet": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } |