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inference: true
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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_v3** dataset.
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You can use the pipeline like so:
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import torch
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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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inference: true
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---
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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_v3** dataset.
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Update: this model supports colored logos
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## Training info
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These are the key hyperparameters used during training:
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* Dataset size: 110k
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* Epochs: 16
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* Learning rate: 5e-07
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* Batch size: 16
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* Gradient accumulation steps: 1
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* Image resolution: 512
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* Mixed-precision: fp16
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## Prompt Format
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The prompt format is as follows:
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```javascript
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{template keywords} + [company name] + [concept & country] + {template keywords}
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```
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For example:
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Colored logo
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```text
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Simple elegant logo for **[Google]**, **[G circle United states]**, successful vibe, minimalist, thought-provoking, abstract, recognizable
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```
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monochrome logo
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```text
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Simple elegant logo for **[Google]**, **[G circle United states]**, successful vibe, minimalist, thought-provoking, abstract, recognizable, black and white
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```
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The [concept & country] section can include words such as:
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- lines
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- circles
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- triangles
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- dot
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- crosses
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- waves
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- square
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- letters (A-Z)
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- 3D
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- Angled
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- Arrows
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- cube
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- Diamond
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- Hexagon
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- Loops
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- outline
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- ovals
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- rectangle
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- reflection
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- rings
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- round
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- semicircle
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- spiral
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- woven
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- stars
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Here are some examples of prompts:
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- Simple elegant logo for Digital Art, **D A square**, successful vibe, minimalist, thought-provoking, abstract, recognizable
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- Simple elegant logo for 3M Technology, **3 M square United states**, successful vibe, minimalist, thought-provoking, abstract, recognizable
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- Simple elegant logo for 42Studio, **lines drop fire flame water**, successful vibe, minimalist, thought provoking, abstract, recognizable, relatable, sharp, vector art, even edges
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