End of training

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+base_model: stabilityai/stable-diffusion-2-1
+library_name: diffusers
+license: openrail++
+inference: true
+instance_prompt: a <s0><s1> chicken in the woods
+widget:
+- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
+  output:
+    url: image_0.png
+- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
+  output:
+    url: image_1.png
+- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
+  output:
+    url: image_2.png
+- text: a <s0><s1> chicken on a beach, in the style of <s0><s1>
+  output:
+    url: image_3.png
+tags:
+- text-to-image
+- diffusers
+- diffusers-training
+- lora
+- template:sd-lorastable-diffusion
+- stable-diffusion-diffusers
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# SD1.5 LoRA DreamBooth - cindyloo337/sbne-chicken-v3
+
+<Gallery />
+
+## Model description
+
+### These are cindyloo337/sbne-chicken-v3 LoRA adaption weights for stabilityai/stable-diffusion-2-1.
+
+## Download model
+
+### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
+
+- **LoRA**: download **[`sbne-chicken-v3.safetensors` here 💾](/cindyloo337/sbne-chicken-v3/blob/main/sbne-chicken-v3.safetensors)**.
+    - Place it on your `models/Lora` folder.
+    - On AUTOMATIC1111, load the LoRA by adding `<lora:sbne-chicken-v3:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
+- *Embeddings*: download **[`sbne-chicken-v3_emb.safetensors` here 💾](/cindyloo337/sbne-chicken-v3/blob/main/sbne-chicken-v3_emb.safetensors)**.
+    - Place it on it on your `embeddings` folder
+    - Use it by adding `sbne-chicken-v3_emb` to your prompt. For example, `a sbne-chicken-v3_emb chicken in the woods`
+    (you need both the LoRA and the embeddings as they were trained together for this LoRA)
+    
+
+## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
+
+```py
+from diffusers import AutoPipelineForText2Image
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+        
+pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
+pipeline.load_lora_weights('cindyloo337/sbne-chicken-v3', weight_name='pytorch_lora_weights.safetensors')
+embedding_path = hf_hub_download(repo_id='cindyloo337/sbne-chicken-v3', filename='sbne-chicken-v3_emb.safetensors', repo_type="model")
+state_dict = load_file(embedding_path)
+pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
+        
+image = pipeline('a <s0><s1> chicken on a beach, in the style of <s0><s1>').images[0]
+```
+
+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)
+
+## Trigger words
+
+To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
+
+to trigger concept `TOK` → use `<s0><s1>` in your prompt 
+
+
+
+## Details
+All [Files & versions](/cindyloo337/sbne-chicken-v3/tree/main).
+
+The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py).
+
+LoRA for the text encoder was enabled. False.
+
+Pivotal tuning was enabled: True.
+
+Special VAE used for training: None.
+
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# TODO: add an example code snippet for running this diffusion pipeline
+```
+
+#### Limitations and bias
+
+[TODO: provide examples of latent issues and potential remediations]
+
+## Training details
+
+[TODO: describe the data used to train the model]

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