Unconditional Image Generation
Diffusers
Safetensors
English
lightningdit
image-generation
class-conditional
imagenet
flow-matching
Instructions to use BiliSakura/LightningDiT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/LightningDiT-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/LightningDiT-diffusers", 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
| { | |
| "_class_name": "AutoencoderKL", | |
| "_diffusers_version": "0.33.0", | |
| "act_fn": "silu", | |
| "block_out_channels": [ | |
| 128, | |
| 128, | |
| 256, | |
| 256, | |
| 512 | |
| ], | |
| "down_block_types": [ | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "DownEncoderBlock2D", | |
| "AttnDownEncoderBlock2D" | |
| ], | |
| "force_upcast": true, | |
| "in_channels": 3, | |
| "latent_channels": 32, | |
| "latents_mean": [ | |
| 0.5984622836112976, | |
| -0.49917176365852356, | |
| 0.6440029144287109, | |
| -0.09708389639854431, | |
| -1.1909630298614502, | |
| -1.4331622123718262, | |
| 0.468532919883728, | |
| 0.6259251832962036, | |
| 0.6319502592086792, | |
| -0.48967328667640686, | |
| -0.7445162534713745, | |
| 1.159562349319458, | |
| 0.8456217050552368, | |
| 0.5008237957954407, | |
| 0.22926893830299377, | |
| 0.4753556549549103, | |
| -0.4378734230995178, | |
| 0.831696093082428, | |
| -0.07508569955825806, | |
| 0.30632293224334717, | |
| 0.4664529263973236, | |
| -0.09140774607658386, | |
| -0.8271016478538513, | |
| 0.0780751183629036, | |
| 1.4150785207748413, | |
| 1.379238486289978, | |
| 0.2695842981338501, | |
| -0.757322371006012, | |
| 0.2812993824481964, | |
| -0.3091999292373657, | |
| 0.07785388082265854, | |
| 0.3496664762496948 | |
| ], | |
| "latents_std": [ | |
| 3.8461380004882812, | |
| 4.269914627075195, | |
| 3.576843738555908, | |
| 3.5911104679107666, | |
| 3.6230573654174805, | |
| 3.48101806640625, | |
| 3.307461738586426, | |
| 3.509265422821045, | |
| 3.554058074951172, | |
| 3.606724500656128, | |
| 3.7057900428771973, | |
| 3.6314077377319336, | |
| 3.6295316219329834, | |
| 3.62050199508667, | |
| 3.259028196334839, | |
| 3.186753034591675, | |
| 3.8258144855499268, | |
| 3.5999391078948975, | |
| 3.296635150909424, | |
| 3.2261290550231934, | |
| 3.2191944122314453, | |
| 3.105457305908203, | |
| 3.580496072769165, | |
| 4.356914043426514, | |
| 3.3085410594940186, | |
| 3.207587480545044, | |
| 4.515047073364258, | |
| 3.486992359161377, | |
| 3.0415804386138916, | |
| 3.486884832382202, | |
| 4.431032657623291, | |
| 4.088115692138672 | |
| ], | |
| "layers_per_block": 2, | |
| "mid_block_add_attention": true, | |
| "norm_num_groups": 32, | |
| "out_channels": 3, | |
| "sample_size": 32, | |
| "scaling_factor": null, | |
| "shift_factor": null, | |
| "up_block_types": [ | |
| "AttnUpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D", | |
| "UpDecoderBlock2D" | |
| ], | |
| "use_post_quant_conv": true, | |
| "use_quant_conv": true | |
| } | |