End of training
Browse files- README.md +72 -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/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/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/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/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/scheduler.bin +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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base_model: models/Qwen-Image
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library_name: diffusers
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license: apache-2.0
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widget: []
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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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- qwen-image
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- qwen-image-diffusers
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- template:sd-lora
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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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# HiDream Image DreamBooth LoRA - viyer7/trained-qwen-lora_1500steps
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<Gallery />
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## Model description
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These are viyer7/trained-qwen-lora_1500steps DreamBooth LoRA weights for models/Qwen-Image.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Qwen Image diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_qwen.md).
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## Trigger words
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You should use `None` to trigger the image generation.
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## Download model
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[Download the *.safetensors LoRA](viyer7/trained-qwen-lora_1500steps/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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>>> import torch
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>>> from diffusers import QwenImagePipeline
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>>> pipe = QwenImagePipeline.from_pretrained(
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... "Qwen/Qwen-Image",
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... torch_dtype=torch.bfloat16,
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... )
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>>> pipe.enable_model_cpu_offload()
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>>> pipe.load_lora_weights(f"viyer7/trained-qwen-lora_1500steps")
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>>> image = pipe(f"None").images[0]
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```
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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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## 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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