Model card auto-generated by SimpleTuner

README.md

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+---
+license: other
+base_model: "sd3/unknown-model"
+tags:
+  - sd3
+  - sd3-diffusers
+  - text-to-image
+  - diffusers
+  - simpletuner
+  - not-for-all-audiences
+  - 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: 'unconditional (blank prompt)'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_1_1.png
+- text: 'Create an image of a fabric design featuring a classic camouflage pattern in various shades of green, brown, and black, suitable for military clothing and outdoor gear.'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_2_0.png
+- text: 'Create an image of a fabric design featuring a classic camouflage pattern in various shades of green, brown, and black, suitable for military clothing and outdoor gear.'
+  parameters:
+    negative_prompt: 'blurry, cropped, ugly'
+  output:
+    url: ./assets/image_3_1.png
+---
+
+# simpletuner-lora
+
+This is a LyCORIS adapter derived from [sd3/unknown-model](https://huggingface.co/sd3/unknown-model).
+
+
+The main validation prompt used during training was:
+
+
+
+```
+Create an image of a fabric design featuring a classic camouflage pattern in various shades of green, brown, and black, suitable for military clothing and outdoor gear.
+```
+
+## Validation settings
+- CFG: `3.0`
+- CFG Rescale: `0.0`
+- Steps: `20`
+- Sampler: `None`
+- Seed: `42`
+- Resolutions: `1024x1024,1280x768`
+
+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: 1
+- 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: No
+- Xformers: Not used
+- LyCORIS Config:
+```json
+{
+    "algo": "lokr",
+    "multiplier": 1.0,
+    "linear_dim": 10000,
+    "linear_alpha": 1,
+    "factor": 16,
+    "apply_preset": {
+        "target_module": [
+            "Attention",
+            "FeedForward"
+        ],
+        "module_algo_map": {
+            "Attention": {
+                "factor": 16
+            },
+            "FeedForward": {
+                "factor": 8
+            }
+        }
+    }
+}
+```
+
+## Datasets
+
+### GPTwebd3
+- Repeats: 0
+- Total number of images: 409
+- Total number of aspect buckets: 1
+- Resolution: 1.0 megapixels
+- Cropped: True
+- Crop style: center
+- 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 = '/root/autodl-tmp/stable-diffusion-3-medium-diffusers'
+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 = "Create an image of a fabric design featuring a classic camouflage pattern in various shades of green, brown, and black, suitable for military clothing and outdoor gear."
+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=3.0,
+).images[0]
+image.save("output.png", format="PNG")
+```
+