soczech / genhowto Goto Github PK

Try to:
python genhowto.py --weights_path ./weights/GenHowTo_controlnet.pth --input_image ./imgs/input/01.png --prompt "a person slicing an avacado on a cutting board" --output_path ./imgs/output/output.png --num_images 1

\GenHowTo-main\genhowto_utils.py", line 16, in load_genhowto_model
with open(os.path.join(weights_path, "GenHowTo_controlnet_config.json")) as file:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: './weights/GenHowTo_controlnet.pth\GenHowTo_controlnet_config.json'

Thanks!
molyswu

Hi, thanks for releasing great work!
How to generate many states at the same time? or are they generated separately?
For example, how can I generate action and final prompts for this example?

Will it be possible to obtain the training code for this model

I am encountering some difficulties in achieving the same level of foreground and background consistency as presented in your work. My results so far have been quite inconsistent and not up to the mark.

Could you please provide any insights on how to achieve similar results? Are there specific techniques, preprocessing steps which you used , that you recommend to improve the consistency of the images?

I observe that there are many low-quality pairs in the dataset that is published, such as pairs with significant differences between the initial and final frames, or where the initial frame is the beginning of a video (with no substantive content). These instances are likely due to training noise. Are there any further filtering methods available?

Additionally, each video in ChangeIt contains multiple shots. In this case, how can we avoid the model from treating the contents of different shots as collected into one pair? Are there more granular annotations available in ChangeIt (such as distinguishing each shot), or do the models provided by ChangeIt have this capability? Alternatively, can the dataset be pre-clipped using other methods?

Looking forward to your response, thank you very much.

Thanks for sharing the amazing project!
Do you have plan to share training and dataset for this project?