End of training
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- README.md +66 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-1000/unet/config.json +68 -0
- checkpoint-1000/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-1000/unet_ema/config.json +75 -0
- checkpoint-1000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
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- checkpoint-10000/scaler.pt +3 -0
- checkpoint-10000/scheduler.bin +3 -0
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- checkpoint-10000/unet/diffusion_pytorch_model.safetensors +3 -0
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- checkpoint-10000/unet_ema/diffusion_pytorch_model.safetensors +3 -0
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- checkpoint-10500/unet/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-10500/unet_ema/config.json +75 -0
- checkpoint-10500/unet_ema/diffusion_pytorch_model.safetensors +3 -0
- checkpoint-11000/optimizer.bin +3 -0
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README.md
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---
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base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
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library_name: diffusers
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license: creativeml-openrail-m
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inference: true
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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- text-to-image
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- diffusers
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- diffusers-training
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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 - mohummadmahad/pakistani-fashion-generator-model-2
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This pipeline was finetuned from **stable-diffusion-v1-5/stable-diffusion-v1-5** on the **mohummadmahad/pakistani_fashion_dataset** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Pakistani Dress']:
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![val_imgs_grid](./val_imgs_grid.png)
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## Pipeline usage
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You can use the pipeline like so:
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```python
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from diffusers import DiffusionPipeline
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import torch
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pipeline = DiffusionPipeline.from_pretrained("mohummadmahad/pakistani-fashion-generator-model-2", torch_dtype=torch.float16)
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prompt = "Pakistani Dress"
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image = pipeline(prompt).images[0]
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image.save("my_image.png")
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```
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## Training info
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These are the key hyperparameters used during training:
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* Epochs: 6
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* Learning rate: 1e-05
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* Batch size: 1
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* Gradient accumulation steps: 4
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* Image resolution: 512
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* Mixed-precision: fp16
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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-1000/optimizer.bin
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checkpoint-10000/unet/config.json
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@@ -0,0 +1,68 @@
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.32.0.dev0",
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"_name_or_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D"
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"mid_block_type": "UNetMidBlock2DCrossAttn",
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"up_block_types": [
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D"
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}
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checkpoint-10000/unet/diffusion_pytorch_model.safetensors
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 3438167536
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checkpoint-10000/unet_ema/config.json
ADDED
@@ -0,0 +1,75 @@
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{
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"_class_name": "UNet2DConditionModel",
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"_diffusers_version": "0.32.0.dev0",
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"_name_or_path": "stable-diffusion-v1-5/stable-diffusion-v1-5",
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"CrossAttnDownBlock2D",
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"CrossAttnDownBlock2D",
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"DownBlock2D"
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"UpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D",
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"CrossAttnUpBlock2D"
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}
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checkpoint-10000/unet_ema/diffusion_pytorch_model.safetensors
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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checkpoint-10500/optimizer.bin
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checkpoint-10500/random_states_0.pkl
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checkpoint-10500/scaler.pt
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version https://git-lfs.github.com/spec/v1
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checkpoint-10500/scheduler.bin
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checkpoint-10500/unet/config.json
ADDED
@@ -0,0 +1,68 @@
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checkpoint-10500/unet/diffusion_pytorch_model.safetensors
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 3438167536
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checkpoint-10500/unet_ema/config.json
ADDED
@@ -0,0 +1,75 @@
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