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add 4gotten alt-text to images

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mauwii 2022-10-11 07:49:11 +02:00
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docs/features/IMG2IMG.md
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@ -18,13 +18,13 @@ tree on a hill with a river, nature photograph, national geographic -I./test-pic
This will take the original image shown here:
<div align="center" markdown>
![original image](https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png){width=350}
<img src="https://user-images.githubusercontent.com/50542132/193946000-c42a96d8-5a74-4f8a-b4c3-5213e6cadcce.png" width=350>
</div>
and generate a new image based on it as shown here:
<div align="center" markdown>
![generated result](https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png){width=350}
<img src="https://user-images.githubusercontent.com/111189/194135515-53d4c060-e994-4016-8121-7c685e281ac9.png" width=350>
</div>
The `--init_img (-I)` option gives the path to the seed picture. `--strength (-f)` controls how much
@ -90,7 +90,7 @@ Put simply: starting from a frame of fuzz/static, SD finds details in each frame
### A concrete example
Say I want SD to draw a fire based on this hand-drawn image:
I want SD to draw a fire based on this hand-drawn image:
<div align="center" markdown>
![drawing of a fireplace](../assets/img2img/fire-drawing.png)
@ -99,13 +99,13 @@ Say I want SD to draw a fire based on this hand-drawn image:
Let's only do 10 steps, to make it easier to see what's happening. If strength is `0.7`, this is what the internal steps the algorithm has to take will look like:
<div align="center" markdown>
![](../assets/img2img/000032.steps.gravity.png)
![gravity32](../assets/img2img/000032.steps.gravity.png)
</div>
With strength `0.4`, the steps look more like this:
<div align="center" markdown>
![](../assets/img2img/000030.steps.gravity.png)
![gravity30](../assets/img2img/000030.steps.gravity.png)
</div>
Notice how much more fuzzy the starting image is for strength `0.7` compared to `0.4`, and notice also how much longer the sequence is with `0.7`:
@ -139,7 +139,7 @@ invoke> "fire" -s50 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.4
```
<div align="center" markdown>
![](../assets/img2img/000035.1592514025.png)
![000035.1592514025](../assets/img2img/000035.1592514025.png)
</div>
and here is strength `0.7` (note step count `30`, which is roughly `20 ÷ 0.7` to make sure SD does `20` steps from my image):
@ -149,28 +149,28 @@ invoke> "fire" -s30 -W384 -H384 -S1592514025 -I /tmp/fire-drawing.png -f 0.7
```
<div align="center" markdown>
![](../assets/img2img/000046.1592514025.png)
![000046.1592514025](../assets/img2img/000046.1592514025.png)
</div>
In both cases the image is nice and clean and "finished", but because at strength `0.7` Stable Diffusion has been give so much more freedom to improve on my badly-drawn flames, they've come out looking much better. You can really see the difference when looking at the latent steps. There's more noise on the first image with strength `0.7`:
![](../assets/img2img/000046.steps.gravity.png)
![gravity46](../assets/img2img/000046.steps.gravity.png)
than there is for strength `0.4`:
![](../assets/img2img/000035.steps.gravity.png)
![gravity35](../assets/img2img/000035.steps.gravity.png)
and that extra noise gives the algorithm more choices when it is evaluating how to denoise any particular pixel in the image.
Unfortunately, it seems that `img2img` is very sensitive to the step count. Here's strength `0.7` with a step count of `29` (SD did 19 steps from my image):
<div align="center" markdown>
![](../assets/img2img/000045.1592514025.png)
![gravity45](../assets/img2img/000045.1592514025.png)
</div>
By comparing the latents we can sort of see that something got interpreted differently enough on the third or fourth step to lead to a rather different interpretation of the flames.
![](../assets/img2img/000046.steps.gravity.png)
![](../assets/img2img/000045.steps.gravity.png)
![gravity46](../assets/img2img/000046.steps.gravity.png)
![gravity45](../assets/img2img/000045.steps.gravity.png)
This is the result of a difference in the de-noising "schedule" - basically the noise has to be cleaned by a certain degree each step or the model won't "converge" on the image properly (see [stable diffusion blog](https://huggingface.co/blog/stable_diffusion) for more about that). A different step count means a different schedule, which means things get interpreted slightly differently at every step.