Text-to-Image
Diffusers
Safetensors
StableDiffusion3Pipeline
diffusers-training
sd3
sd3-diffusers
template:sd-lora
Instructions to use cwz/trained-sd3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cwz/trained-sd3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cwz/trained-sd3", dtype=torch.bfloat16, device_map="cuda") prompt = "A photo of sks dog in a bucket" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download model_index.json from cwz/trained-sd3: direct link, hf CLI and curl.
- Browser
- Download file 776 Bytes
-
https://huggingface.co/cwz/trained-sd3/resolve/main/model_index.json
- Command line
-
hf download hf://cwz/trained-sd3/model_index.json
-
curl -L -o model_index.json https://huggingface.co/cwz/trained-sd3/resolve/main/model_index.json
776 Bytes
| { | |
| "_class_name": "StableDiffusion3Pipeline", | |
| "_diffusers_version": "0.30.0.dev0", | |
| "_name_or_path": "stabilityai/stable-diffusion-3-medium-diffusers", | |
| "scheduler": [ | |
| "diffusers", | |
| "FlowMatchEulerDiscreteScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "text_encoder_2": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "text_encoder_3": [ | |
| "transformers", | |
| "T5EncoderModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "tokenizer_2": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "tokenizer_3": [ | |
| "transformers", | |
| "T5TokenizerFast" | |
| ], | |
| "transformer": [ | |
| "diffusers", | |
| "SD3Transformer2DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |