omerbt / MultiDiffusion

Official Pytorch Implementation for "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation" presenting "MultiDiffusion" (ICML 2023)
https://multidiffusion.github.io/
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When will codes for region-based generation release? #14

Open Show-han opened 1 year ago

Show-han commented 1 year ago

Interesting work, and I wonder when will codes for region-based generation release. BTW, the hugging face demo for region-based generation crashed, can you fix it? I really want to try out

learningyan commented 1 year ago

the website is fixed now, I tried some examples on this website, but the results I obtained are significantly different from those presented in the paper. .......

企业微信截图_b4a81f02-ddfe-4968-b6cb-2d8aa5503352

hope to get some help from the authors.....

Show-han commented 1 year ago

the website is fixed now, I tried some examples on this website, but the results I obtained are significantly different from those presented in the paper. .......

企业微信截图_b4a81f02-ddfe-4968-b6cb-2d8aa5503352

hope to get some help from the authors.....

the website is fixed now, I tried some examples on this website, but the results I obtained are significantly different from those presented in the paper. .......

企业微信截图_b4a81f02-ddfe-4968-b6cb-2d8aa5503352

hope to get some help from the authors.....

I tried, and the results are significantly worse compared to those in the paper......

omerbt commented 1 year ago

Please note that by default, the bootstrapping parameter is set to 20, as in the application of tight masks. The high bootstrapping parameter encourages high-fidelity to accurate segmentation masks, but may not be suitable for rough bounding boxes (as in the provided example). The parameter can be controlled under 'advanced options'.

learningyan commented 1 year ago

hi Can you give me a set of detailed parameters to generate high-quality results? examples: prompts, bootstrapping parameter and so on . I have tried many experiments, but the results are bad ....

learningyan commented 1 year ago

Please note that by default, the bootstrapping parameter is set to 20, as in the application of tight masks. The high bootstrapping parameter encourages high-fidelity to accurate segmentation masks, but may not be suitable for rough bounding boxes (as in the provided example). The parameter can be controlled under 'advanced options'.

Could you give me an example of using this website to generate some good results?

Show-han commented 1 year ago

Please note that by default, the bootstrapping parameter is set to 20, as in the application of tight masks. The high bootstrapping parameter encourages high-fidelity to accurate segmentation masks, but may not be suitable for rough bounding boxes (as in the provided example). The parameter can be controlled under 'advanced options'.

I also tried almost every settings in adcanved options, but I found no one can generate images like the demos in your website.

alphacoder01 commented 1 year ago

Requesting the authors to please upload sample inputs on hugging-face demo for region-control. I've tried everything from accurate segmentation masks to varying the bootstrapping param, but nothing seems to generate images anywhere near the likes of that are presented in the paper.

uiyo commented 1 year ago

I have tried this example, with tiny modification, the result seems relatively good. image Any ideas when the author will release the codes?