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sd35-training-loona
jimmycarter/sd35-training-loona
sd35-training-loona is a text-to-image model from jimmycarter. Use it when you need an image from a text prompt. It is set up for diffusers. The card lists the license as other.
This is a LyCORIS adapter derived from sd3/unknown-model.
All-time downloads
104
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Repo size
34.1 GB
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At a glance
- Task
- Text-to-Image
- Library
- diffusers
- License
- other
- Access
- Public
- Created
- Nov 23, 2024
- Updated
- Nov 25, 2024
- SHA
- 22a305ef
Try a prompt
Base models
Model card
From the Hugging Face model README
sd35-training-loona
This is a LyCORIS adapter derived from sd3/unknown-model.
The main validation prompt used during training was:
A figurine of a character with green hair, wearing a white shirt, a black vest, and a gray cap, sitting with one hand on their knee and the other hand making a peace sign. The character is wearing a blue pendant and has a gold bracelet. In the background, there are green plants and a tree branch.
Validation settings
- CFG:
5.5 - CFG Rescale:
0.0 - Steps:
20 - Sampler:
None - Seed:
42 - Resolution:
1024x1024
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
<Gallery />The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 0
- Training steps: 28000
- Learning rate: 5e-05
- Max grad norm: 0.01
- Effective batch size: 5
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 5
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
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"transformer_blocks.29.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.30.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.norm1*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.norm1_context*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.ff*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.31.*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.32.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.33.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.34.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.norm1*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.norm1_context*": {
"algo": "lokr",
"factor": 16,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.ff*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.35.*": {
"algo": "lokr",
"factor": 1,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.36.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.norm1*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.norm1_context*": {
"algo": "lokr",
"factor": 32,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.ff*": {
"algo": "lokr",
"factor": 2,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.37.*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
}
},
"use_fnmatch": true
}
}
Datasets
default_dataset_arb
- Repeats: 9999
- Total number of images: ~105
- Total number of aspect buckets: 11
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
default_dataset_arb2
- Repeats: 9999
- Total number of images: ~3295
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
default_dataset_arb3
- Repeats: 9999
- Total number of images: ~3305
- Total number of aspect buckets: 16
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
default_dataset
- Repeats: 9999
- Total number of images: ~70
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_512
- Repeats: 9999
- Total number of images: ~45
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_640
- Repeats: 9999
- Total number of images: ~70
- Total number of aspect buckets: 1
- Resolution: 0.4096 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_768
- Repeats: 9999
- Total number of images: ~45
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_896
- Repeats: 9999
- Total number of images: ~45
- Total number of aspect buckets: 1
- Resolution: 0.802816 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_uncaptioned
- Repeats: 9999
- Total number of images: ~3260
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_uncaptioned_512
- Repeats: 9999
- Total number of images: ~3140
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art
- Repeats: 9999
- Total number of images: ~2485
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_512
- Repeats: 9999
- Total number of images: ~3195
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_640
- Repeats: 9999
- Total number of images: ~3115
- Total number of aspect buckets: 1
- Resolution: 0.4096 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_768
- Repeats: 9999
- Total number of images: ~2990
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
default_dataset_art_896
- Repeats: 9999
- Total number of images: ~2790
- Total number of aspect buckets: 1
- Resolution: 0.802816 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = '/home/user/storage/models/sd35_merged_236000'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "A figurine of a character with green hair, wearing a white shirt, a black vest, and a gray cap, sitting with one hand on their knee and the other hand making a peace sign. The character is wearing a blue pendant and has a gold bracelet. In the background, there are green plants and a tree branch."
negative_prompt = ''
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1024,
height=1024,
guidance_scale=5.5,
).images[0]
image.save("output.png", format="PNG")
- Task
- Text-to-Image
- Library
- diffusers
- License
- other
- Created
- Nov 23, 2024
- Updated
- Nov 25, 2024