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Upload folder using huggingface_hub

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  1. .gitattributes +1 -0
  2. README.md +46 -0
  3. config.yaml +6 -6
  4. lora.safetensors +3 -0
.gitattributes ADDED
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+ lora.safetensors filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ license: other
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+ license_name: flux-1-dev-non-commercial-license
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+ license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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+ language:
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+ - en
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+ tags:
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+ - flux
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+ - diffusers
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+ - lora
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+ - replicate
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+ base_model: "black-forest-labs/FLUX.1-dev"
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+ pipeline_tag: text-to-image
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+ # widget:
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+ # - text: >-
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+ # prompt
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+ # output:
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+ # url: https://...
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+ instance_prompt: l1n3s
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+ ---
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+
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+ # L1N3S
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+
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+ <!-- <Gallery /> -->
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+
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+ Trained on Replicate using:
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+
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+ https://replicate.com/ostris/flux-dev-lora-trainer/train
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+
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+
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+ ## Trigger words
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+ You should use `l1n3s` to trigger the image generation.
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+
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+
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+ ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
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+
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+ ```py
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+ from diffusers import AutoPipelineForText2Image
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+ import torch
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+
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+ pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
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+ pipeline.load_lora_weights('vcm07/l1n3s', weight_name='lora.safetensors')
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+ image = pipeline('your prompt').images[0]
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+ ```
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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)
config.yaml CHANGED
@@ -8,11 +8,11 @@ config:
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  trigger_word: l1n3s
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  network:
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  type: lora
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- linear: 32
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- linear_alpha: 32
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  save:
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  dtype: float16
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- save_every: 3001
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  max_step_saves_to_keep: 1
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  datasets:
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  - folder_path: input_images
@@ -26,8 +26,8 @@ config:
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  - 768
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  - 1024
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  train:
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- batch_size: 2
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- steps: 3000
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  gradient_accumulation_steps: 1
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  train_unet: true
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  train_text_encoder: false
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  quantize: true
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  sample:
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  sampler: flowmatch
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- sample_every: 3001
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  width: 1024
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  height: 1024
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  prompts: []
 
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  trigger_word: l1n3s
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  network:
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  type: lora
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+ linear: 128
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+ linear_alpha: 128
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  save:
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  dtype: float16
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+ save_every: 6001
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  max_step_saves_to_keep: 1
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  datasets:
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  - folder_path: input_images
 
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  - 768
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  - 1024
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  train:
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+ batch_size: 5
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+ steps: 6000
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  gradient_accumulation_steps: 1
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  train_unet: true
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  train_text_encoder: false
 
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  quantize: true
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  sample:
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  sampler: flowmatch
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+ sample_every: 6001
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  width: 1024
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  height: 1024
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  prompts: []
lora.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:7b7749abc61d9dc31ae5178b2c54065c4729aff4dcb2c84cc1d9c1836a8f5a98
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+ size 1374819808