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Browse files- README.md +45 -0
- checkpoint-30000/optimizer.bin +3 -0
- checkpoint-30000/random_states_0.pkl +3 -0
- checkpoint-30000/scheduler.bin +3 -0
- checkpoint-30000/unet/config.json +73 -0
- checkpoint-30000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-50000/optimizer.bin +3 -0
- checkpoint-50000/random_states_0.pkl +3 -0
- checkpoint-50000/scheduler.bin +3 -0
- checkpoint-50000/unet/config.json +73 -0
- checkpoint-50000/unet/diffusion_pytorch_model.safetensors +3 -0
- image_0.png +0 -0
- image_1.png +0 -0
- image_2.png +0 -0
- image_3.png +0 -0
- model_index.json +42 -0
- scheduler/scheduler_config.json +22 -0
- text_encoder/config.json +25 -0
- text_encoder/model.safetensors +3 -0
- text_encoder_2/config.json +25 -0
- text_encoder_2/model.safetensors +3 -0
- tokenizer/merges.txt +0 -0
- tokenizer/special_tokens_map.json +24 -0
- tokenizer/tokenizer_config.json +30 -0
- tokenizer/vocab.json +0 -0
- tokenizer_2/merges.txt +0 -0
- tokenizer_2/special_tokens_map.json +24 -0
- tokenizer_2/tokenizer_config.json +38 -0
- tokenizer_2/vocab.json +0 -0
- unet/config.json +73 -0
- unet/diffusion_pytorch_model.safetensors +3 -0
- vae/config.json +34 -0
- vae/diffusion_pytorch_model.safetensors +3 -0
README.md
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---
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license: creativeml-openrail-m
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library_name: diffusers
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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-training
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- diffusers
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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inference: true
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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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# Text-to-image finetuning - izzudd/sdxl-batik-llava
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This pipeline was finetuned from **stabilityai/stable-diffusion-xl-base-1.0** on the **../dataset/train/llava** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: batik with intricate cloud pattern:
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![img_0](./image_0.png)
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![img_1](./image_1.png)
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![img_2](./image_2.png)
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![img_3](./image_3.png)
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Special VAE used for training: None.
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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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checkpoint-30000/optimizer.bin
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checkpoint-30000/unet/config.json
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"_name_or_path": "stabilityai/stable-diffusion-xl-base-1.0",
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"act_fn": "silu",
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"addition_embed_type": "text_time",
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"addition_embed_type_num_heads": 64,
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"addition_time_embed_dim": 256,
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"attention_head_dim": [
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"conv_in_kernel": 3,
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"conv_out_kernel": 3,
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"cross_attention_dim": 2048,
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"cross_attention_norm": null,
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"down_block_types": [
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"DownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D"
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"downsample_padding": 1,
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"in_channels": 4,
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"layers_per_block": 2,
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"up_block_types": [
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"use_linear_projection": true
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}
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}
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checkpoint-50000/unet/diffusion_pytorch_model.safetensors
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image_0.png
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image_1.png
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image_2.png
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image_3.png
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model_index.json
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{
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"feature_extractor": [
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null,
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null,
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"scheduler": [
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"diffusers",
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"EulerDiscreteScheduler"
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],
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"text_encoder": [
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"transformers",
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"CLIPTextModel"
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],
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"text_encoder_2": [
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"transformers",
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"CLIPTextModelWithProjection"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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],
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"tokenizer_2": [
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"transformers",
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"CLIPTokenizer"
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],
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"unet": [
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"diffusers",
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"UNet2DConditionModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderKL"
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]
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}
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scheduler/scheduler_config.json
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{
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"_class_name": "EulerDiscreteScheduler",
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"_diffusers_version": "0.28.0.dev0",
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+
"scaling_factor": 0.13025,
|
28 |
+
"up_block_types": [
|
29 |
+
"UpDecoderBlock2D",
|
30 |
+
"UpDecoderBlock2D",
|
31 |
+
"UpDecoderBlock2D",
|
32 |
+
"UpDecoderBlock2D"
|
33 |
+
]
|
34 |
+
}
|
vae/diffusion_pytorch_model.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2741af7e84fe3b0a7aee02f89fa34c0858ed55f5782aab5931b94938983652da
|
3 |
+
size 167335590
|