separat eval images

README.md

@@ -6,11 +6,11 @@ language:
 - en
 ---
 
-Controls image generation by edge maps generated with [Edge Drawing](https://github.com/CihanTopal/ED_Lib). Edge Drawing comes in different flavors: original (ed), parameter-free (edpf), color (edcolor).
+Controls image generation by edge maps generated with [Edge Drawing](https://github.com/CihanTopal/ED_Lib). Edge Drawing comes in different flavors: original (_ed_), parameter-free (_edpf_), color (_edcolor_).
 
 * Based on my monologs at [github.com - Edge Drawing](https://github.com/lllyasviel/ControlNet/discussions/318)
 * For usage see the model page on [civitai.com - Model](https://civitai.com/models/149740).
-* To generate edpf maps you can use the script [gitlab.com - edpf.py](https://gitlab.com/-/snippets/3601881).
+* To generate edpf maps you can use the [space](https://huggingface.co/spaces/GeroldMeisinger/edpf) or script from [gitlab.com - edpf.py](https://gitlab.com/-/snippets/3601881).
 * For evaluation see the corresponding .zip with images in "files".
 * To run your own evaluations you can use the script [gitlab.com - inference.py](https://gitlab.com/-/snippets/3602096).
 
@@ -63,8 +63,8 @@ To evaluate the model it makes sense to compare it with the original Canny model
 * canny 1.0 model was trained on 3M images with fp32, canny 1.1 model on even more, while edpf model so far is only trained on a 180k-360k with fp16.
 * canny edge-detector requires parameter tuning while edpf is parameter-free.
 * Do we manually fine-tune canny to find the perfect input image or do we leave it at default? We could argue that "no fine-tuning required" is the usp of edpf and we want to compare in the default setting, whereas canny fine-tuning is subjective.
-* Would the canny model actually benefit from a edpf pre-processor and we might not even require a edpf model? (2023-09-25: see `eval_canny_edpf.zip` but it seems as it doesn't work and the edpf model may be justified)
-* When evaluating human images we need to be aware of Stable Diffusion's inherent limits, like disformed faces and hands.
+* Would the canny model actually benefit from a edpf pre-processor and we might not even require a edpf model? (2023-09-25: see `eval_canny_edpf.zip` but it seems as if it doesn't work and the edpf model may be justified)
+* When evaluating human images we need to be aware of Stable Diffusion's inherent limits, like disformed faces and hands, and don't attribute them to the control net.
 * When evaluating style we need to be aware of the bias from the image dataset (`laion2b-en-aesthetics65`), which might tend to generate "aesthetic" images, and not actually work "intrisically better".
 
 # Versions