Instructions to use johnberg/resedit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use johnberg/resedit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("johnberg/resedit", 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
File size: 632 Bytes
7421aaa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"_class_name": "SD3Transformer2DModel",
"_diffusers_version": "0.35.2",
"_name_or_path": "/sensei-fs-3/users/abaykal/x2rgb_new_data_checkpoints/2026-01-05_05-32-50/checkpoint-80000",
"attention_head_dim": 64,
"caption_projection_dim": 1536,
"dual_attention_layers": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12
],
"in_channels": 80,
"joint_attention_dim": 4096,
"num_attention_heads": 24,
"num_layers": 24,
"out_channels": 16,
"patch_size": 2,
"pooled_projection_dim": 2048,
"pos_embed_max_size": 384,
"qk_norm": "rms_norm",
"sample_size": 128
}
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