End of training
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- README.md +98 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-10000/optimizer.bin +3 -0
- checkpoint-10000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-10000/random_states_0.pkl +3 -0
- checkpoint-10000/scaler.pt +3 -0
- checkpoint-10000/scheduler.bin +3 -0
- checkpoint-1500/optimizer.bin +3 -0
- checkpoint-1500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1500/random_states_0.pkl +3 -0
- checkpoint-1500/scaler.pt +3 -0
- checkpoint-1500/scheduler.bin +3 -0
- checkpoint-2000/optimizer.bin +3 -0
- checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scaler.pt +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-2500/optimizer.bin +3 -0
- checkpoint-2500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2500/random_states_0.pkl +3 -0
- checkpoint-2500/scaler.pt +3 -0
- checkpoint-2500/scheduler.bin +3 -0
- checkpoint-3000/optimizer.bin +3 -0
- checkpoint-3000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-3000/random_states_0.pkl +3 -0
- checkpoint-3000/scaler.pt +3 -0
- checkpoint-3000/scheduler.bin +3 -0
- checkpoint-3500/optimizer.bin +3 -0
- checkpoint-3500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-3500/random_states_0.pkl +3 -0
- checkpoint-3500/scaler.pt +3 -0
- checkpoint-3500/scheduler.bin +3 -0
- checkpoint-4000/optimizer.bin +3 -0
- checkpoint-4000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-4000/random_states_0.pkl +3 -0
- checkpoint-4000/scaler.pt +3 -0
- checkpoint-4000/scheduler.bin +3 -0
- checkpoint-4500/optimizer.bin +3 -0
- checkpoint-4500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-4500/random_states_0.pkl +3 -0
- checkpoint-4500/scaler.pt +3 -0
- checkpoint-4500/scheduler.bin +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scaler.pt +3 -0
README.md
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---
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base_model: stabilityai/stable-diffusion-3-medium-diffusers
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library_name: diffusers
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license: openrail++
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tags:
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- text-to-image
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- diffusers-training
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- diffusers
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- lora
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- sd3
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- sd3-diffusers
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- template:sd-lora
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instance_prompt: <d3sign>, XXX.
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widget:
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- text: <d3sign>, use four illustrations to decorate <c0nt3ntpa9e>. Illustration 1
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is a cluster of stars. Illustration 2 is a cluster of stars. Illustration 3 is
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a cluster of flowers. Illustration 4 is a cactus.
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output:
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url: image_0.png
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- text: <d3sign>, use four illustrations to decorate <c0nt3ntpa9e>. Illustration 1
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is a cluster of stars. Illustration 2 is a cluster of stars. Illustration 3 is
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a cluster of flowers. Illustration 4 is a cactus.
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output:
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url: image_1.png
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- text: <d3sign>, use four illustrations to decorate <c0nt3ntpa9e>. Illustration 1
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is a cluster of stars. Illustration 2 is a cluster of stars. Illustration 3 is
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a cluster of flowers. Illustration 4 is a cactus.
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output:
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url: image_2.png
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- text: <d3sign>, use four illustrations to decorate <c0nt3ntpa9e>. Illustration 1
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is a cluster of stars. Illustration 2 is a cluster of stars. Illustration 3 is
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a cluster of flowers. Illustration 4 is a cactus.
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output:
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url: image_3.png
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# SD3 DreamBooth LoRA - jzy124/0829-v1
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<Gallery />
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## Model description
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These are jzy124/0829-v1 DreamBooth LoRA weights for stabilityai/stable-diffusion-3-medium-diffusers.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md).
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Was LoRA for the text encoder enabled? False.
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## Trigger words
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You should use `<d3sign>, XXX.` to trigger the image generation.
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## Download model
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[Download the *.safetensors LoRA](jzy124/0829-v1/tree/main) in the Files & versions tab.
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## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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```py
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from diffusers import AutoPipelineForText2Image
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import torch
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pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-3-medium-diffusers', torch_dtype=torch.float16).to('cuda')
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pipeline.load_lora_weights('jzy124/0829-v1', weight_name='pytorch_lora_weights.safetensors')
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image = pipeline('<d3sign>, use four illustrations to decorate <c0nt3ntpa9e>. Illustration 1 is a cluster of stars. Illustration 2 is a cluster of stars. Illustration 3 is a cluster of flowers. Illustration 4 is a cactus.').images[0]
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```
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### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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- **LoRA**: download **[`diffusers_lora_weights.safetensors` here 💾](/jzy124/0829-v1/blob/main/diffusers_lora_weights.safetensors)**.
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- Rename it and place it on your `models/Lora` folder.
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- On AUTOMATIC1111, load the LoRA by adding `<lora:your_new_name:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
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For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
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## License
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Please adhere to the licensing terms as described [here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE).
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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