Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
Film
Cinematic
Movies
LEOSAM
stable-diffusion
stable-diffusion-1.5
stable-diffusion-diffusers
Instructions to use Yntec/Film with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yntec/Film with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/Film", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download scheduler/scheduler_config.json from Yntec/Film: direct link, hf CLI and curl.
- Browser
- Download file 433 Bytes
-
https://huggingface.co/Yntec/Film/resolve/main/scheduler/scheduler_config.json
- Command line
-
hf download hf://Yntec/Film/scheduler/scheduler_config.json
-
curl -L -o scheduler_config.json https://huggingface.co/Yntec/Film/resolve/main/scheduler/scheduler_config.json
433 Bytes
| { | |
| "_class_name": "EulerDiscreteScheduler", | |
| "_diffusers_version": "0.19.0.dev0", | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "beta_start": 0.00085, | |
| "clip_sample": false, | |
| "interpolation_type": "linear", | |
| "num_train_timesteps": 1000, | |
| "prediction_type": "epsilon", | |
| "set_alpha_to_one": false, | |
| "steps_offset": 1, | |
| "timestep_spacing": "linspace", | |
| "trained_betas": null, | |
| "use_karras_sigmas": false | |
| } |