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iamkaikai
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amazing-logos

Text-to-Image
Diffusers
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Inference Endpoints
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End of training

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  1. README.md +4 -4
  2. checkpoint-130000/optimizer.bin +3 -0
  3. checkpoint-130000/random_states_0.pkl +3 -0
  4. checkpoint-130000/scaler.pt +3 -0
  5. checkpoint-130000/scheduler.bin +3 -0
  6. checkpoint-130000/unet/config.json +65 -0
  7. checkpoint-130000/unet/diffusion_pytorch_model.bin +3 -0
  8. checkpoint-140000/optimizer.bin +3 -0
  9. checkpoint-140000/random_states_0.pkl +3 -0
  10. checkpoint-140000/scaler.pt +3 -0
  11. checkpoint-140000/scheduler.bin +3 -0
  12. checkpoint-140000/unet/config.json +65 -0
  13. checkpoint-140000/unet/diffusion_pytorch_model.bin +3 -0
  14. checkpoint-150000/optimizer.bin +3 -0
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  16. checkpoint-150000/scaler.pt +3 -0
  17. checkpoint-150000/scheduler.bin +3 -0
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  19. checkpoint-150000/unet/diffusion_pytorch_model.bin +3 -0
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  24. checkpoint-160000/unet/config.json +65 -0
  25. checkpoint-160000/unet/diffusion_pytorch_model.bin +3 -0
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  30. checkpoint-170000/unet/config.json +65 -0
  31. checkpoint-170000/unet/diffusion_pytorch_model.bin +3 -0
  32. unet/config.json +1 -1
  33. unet/diffusion_pytorch_model.bin +1 -1
  34. val_imgs_grid.png +0 -0
README.md CHANGED
@@ -14,7 +14,7 @@ inference: true
14
 
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  # Text-to-image finetuning - iamkaikai/amazing-logos
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- This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **iamkaikai/amazing_logos_v2** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Simple elegant logo for Digital Art, D A Square Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white']:
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  ![val_imgs_grid](./val_imgs_grid.png)
20
 
@@ -28,7 +28,7 @@ from diffusers import DiffusionPipeline
28
  import torch
29
 
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  pipeline = DiffusionPipeline.from_pretrained("iamkaikai/amazing-logos", torch_dtype=torch.float16)
31
- prompt = "Simple elegant logo for Digital Art, D A Square Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white"
32
  image = pipeline(prompt).images[0]
33
  image.save("my_image.png")
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  ```
@@ -37,7 +37,7 @@ image.save("my_image.png")
37
 
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  These are the key hyperparameters used during training:
39
 
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- * Epochs: 16
41
  * Learning rate: 1e-07
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  * Batch size: 1
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  * Gradient accumulation steps: 1
@@ -45,4 +45,4 @@ These are the key hyperparameters used during training:
45
  * Mixed-precision: fp16
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- More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/iam-kai-kai/text2image-fine-tune/runs/0av1w9qj).
 
14
 
15
  # Text-to-image finetuning - iamkaikai/amazing-logos
16
 
17
+ This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **iamkaikai/amazing_logos_v2** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Simple elegant logo for Digital Art, D A triangle Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white']:
18
 
19
  ![val_imgs_grid](./val_imgs_grid.png)
20
 
 
28
  import torch
29
 
30
  pipeline = DiffusionPipeline.from_pretrained("iamkaikai/amazing-logos", torch_dtype=torch.float16)
31
+ prompt = "Simple elegant logo for Digital Art, D A triangle Symmetrical, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white"
32
  image = pipeline(prompt).images[0]
33
  image.save("my_image.png")
34
  ```
 
37
 
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  These are the key hyperparameters used during training:
39
 
40
+ * Epochs: 17
41
  * Learning rate: 1e-07
42
  * Batch size: 1
43
  * Gradient accumulation steps: 1
 
45
  * Mixed-precision: fp16
46
 
47
 
48
+ More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/iam-kai-kai/text2image-fine-tune/runs/o936ikb3).
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