End of training

3d-icon-Flux-LoRA_car1_emb.safetensors

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README.md

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+---
+base_model: black-forest-labs/FLUX.1-dev
+library_name: diffusers
+license: other
+instance_prompt: a photo of a car
+widget: []
+tags:
+- text-to-image
+- diffusers-training
+- diffusers
+- lora
+- flux
+- flux-diffusers
+- template:sd-lora
+---
+
+<!-- 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. -->
+
+
+# Flux DreamBooth LoRA - sachi1/3d-icon-Flux-LoRA_car1
+
+<Gallery />
+
+## Model description
+
+These are sachi1/3d-icon-Flux-LoRA_car1 DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
+
+The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
+
+Was LoRA for the text encoder enabled? False.
+
+Pivotal tuning was enabled: True.
+
+## 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 
+
+    
+
+## Download model
+
+[Download the *.safetensors LoRA](sachi1/3d-icon-Flux-LoRA_car1/tree/main) in the Files & versions tab.
+
+## 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("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
+pipeline.load_lora_weights('sachi1/3d-icon-Flux-LoRA_car1', weight_name='pytorch_lora_weights.safetensors')
+embedding_path = hf_hub_download(repo_id='sachi1/3d-icon-Flux-LoRA_car1', filename='3d-icon-Flux-LoRA_car1_emb.safetensors', repo_type="model")
+    state_dict = load_file(embedding_path)
+    pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
+    pipeline.load_textual_inversion(state_dict["t5"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
+            
+image = pipeline('a photo of a car').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)
+
+## License
+
+Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
+
+
+## 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]

checkpoint-500/optimizer.bin

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checkpoint-500/random_states_0.pkl

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pytorch_lora_weights.safetensors

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