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dyamagishi
/
human_place

Text-to-Image
Diffusers
Safetensors
stable-diffusion-xl
stable-diffusion-xl-diffusers
controlnet
diffusers-training
Model card Files Community
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End of training

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  1. .gitattributes +2 -0
  2. README.md +43 -0
  3. checkpoint-10000/controlnet/config.json +57 -0
  4. checkpoint-10000/controlnet/diffusion_pytorch_model.safetensors +3 -0
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  33. config.json +57 -0
  34. diffusion_pytorch_model.safetensors +3 -0
  35. image_control.png +3 -0
  36. images_0.png +3 -0
.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ base_model: cagliostrolab/animagine-xl-3.1
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+ library_name: diffusers
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+ license: openrail++
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+ tags:
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+ - stable-diffusion-xl
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+ - stable-diffusion-xl-diffusers
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+ - text-to-image
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+ - diffusers
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+ - controlnet
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+ - diffusers-training
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+ inference: true
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+ ---
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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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+
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+
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+ # controlnet-dyamagishi/human_place
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+
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+ These are controlnet weights trained on cagliostrolab/animagine-xl-3.1 with new type of conditioning.
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+ You can find some example images below.
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+
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+ prompt: outdoors, scenery, cloud, multiple_girls, sky, day, tree, grass, architecture, 2girls, blue_sky, building, standing, skirt, long_hair, mountain, east_asian_architecture, from_behind, castle, facing_away, black_skirt, school_uniform, pagoda, waterfall, white_shirt, white_hair, shirt, cloudy_sky, bag
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+ ![images_0)](./images_0.png)
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+
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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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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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
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