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README.md

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+---
+license: other
+base_model: "stabilityai/stable-diffusion-3.5-large"
+tags:
+  - sd3
+  - sd3-diffusers
+  - text-to-image
+  - diffusers
+  - simpletuner
+  - safe-for-work
+  - lora
+  - template:sd-lora
+  - lycoris
+inference: true
+widget:
+- text: 'unconditional (blank prompt)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_0_0.png
+- text: 'ps2 graphics, liminal, hotel lobby, videogame screenshot'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_1_0.png
+---
+
+# subliminal_large
+
+This is a LyCORIS adapter derived from [stabilityai/stable-diffusion-3.5-large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large).
+
+
+The main validation prompt used during training was:
+
+
+
+```
+ps2 graphics, liminal, hotel lobby, videogame screenshot
+```
+
+## Validation settings
+- CFG: `5.0`
+- 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](#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: 500
+- Learning rate: 0.0001
+- Max grad norm: 0.01
+- Effective batch size: 1
+  - Micro-batch size: 1
+  - Gradient accumulation steps: 1
+  - Number of GPUs: 1
+- Prediction type: flow-matching
+- Rescaled betas zero SNR: False
+- Optimizer: adamw_bf16
+- Precision: Pure BF16
+- Quantised: Yes: int8-quanto
+- Xformers: Not used
+- LyCORIS Config:
+```json
+{
+    "algo": "lora",
+    "multiplier": 1.0,
+    "linear_dim": 64,
+    "linear_alpha": 32,
+    "apply_preset": {
+        "target_module": [
+            "Attention",
+            "FeedForward"
+        ],
+        "module_algo_map": {
+            "Attention": {
+                "factor": 16
+            },
+            "FeedForward": {
+                "factor": 8
+            }
+        }
+    }
+}
+```
+
+## Datasets
+
+### ps2_subliminal-512
+- Repeats: 10
+- Total number of images: 55
+- Total number of aspect buckets: 2
+- Resolution: 0.262144 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+### ps2_subliminal-1024
+- Repeats: 10
+- Total number of images: 55
+- Total number of aspect buckets: 2
+- Resolution: 1.048576 megapixels
+- Cropped: False
+- Crop style: None
+- Crop aspect: None
+- Used for regularisation data: No
+### ps2_subliminal-512-crop
+- Repeats: 10
+- Total number of images: 55
+- Total number of aspect buckets: 1
+- Resolution: 0.262144 megapixels
+- Cropped: True
+- Crop style: random
+- Crop aspect: square
+- Used for regularisation data: No
+### ps2_subliminal-1024-crop
+- Repeats: 10
+- Total number of images: 55
+- Total number of aspect buckets: 1
+- Resolution: 1.048576 megapixels
+- Cropped: True
+- Crop style: random
+- Crop aspect: square
+- Used for regularisation data: No
+
+
+## Inference
+
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+from lycoris import create_lycoris_from_weights
+
+model_id = 'stabilityai/stable-diffusion-3.5-large'
+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 = "ps2 graphics, liminal, hotel lobby, videogame screenshot"
+negative_prompt = 'blurry, cropped, ugly'
+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=5.0,
+).images[0]
+image.save("output.png", format="PNG")
+```
+