title
MIT 6.S191: Text-to-Image Generation

description
MIT Introduction to Deep Learning 6.S191: Lecture 8 Deep Learning Limitations and New Frontiers Lecturer: Dilip Krishnan 2023 Edition For all lectures, slides, and lab materials: http://introtodeeplearning.com​ Lecture Outline - coming soon! Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

detail
{'title': 'MIT 6.S191: Text-to-Image Generation', 'heatmap': [], 'summary': 'Covers the muse model for text-to-image generation, emphasizing its large-scale data collection, recognition of biases, and use of pre-trained language models. it discusses efficient image generation, iterative decoding, model evaluations, text-guided editing, parallel decoding, and challenges in ai image generation.', 'chapters': [{'end': 311.67, 'segs': [{'end': 141.193, 'src': 'embed', 'start': 67.628, 'weight': 0, 'content': [{'end': 72.051, 'text': 'We are able to express our thoughts, creative ideas through the use of text.', 'start': 67.628, 'duration': 4.423}, {'end': 76.113, 'text': 'And then that allows non-experts, non-artists,', 'start': 72.591, 'duration': 3.522}, {'end': 84.358, 'text': 'to generate compelling images and then be able to iterate them through editing tools to create your own personal art and ideas.', 'start': 76.113, 'duration': 8.245}, {'end': 94.822, 'text': 'Also, very importantly, deep learning requires lots of data and it is much more feasible to collect large-scale paired image text data.', 'start': 85.678, 'duration': 9.144}, {'end': 102.145, 'text': 'An example is the Lyon 5 billion dataset on which models such as stable diffusion have been trained.', 'start': 95.602, 'duration': 6.543}, {'end': 110.349, 'text': 'So we should recognize that various biases exist in these datasets and bias mitigation is an important research problem.', 'start': 103.566, 'duration': 6.783}, {'end': 119.851, 'text': 'And lastly, these models can exploit pre-trained large language models, which are very powerful.', 'start': 112.166, 'duration': 7.685}, {'end': 127.656, 'text': 'And they allow for extremely fine-grained understanding of text parts of speech, nouns, verbs, adjectives,', 'start': 120.392, 'duration': 7.264}, {'end': 133.28, 'text': 'and then be able to translate those semantic concepts to output images.', 'start': 127.656, 'duration': 5.624}, {'end': 141.193, 'text': 'And these LLMs, importantly, can be pre-trained on various text tasks with orders of magnitude larger text data.', 'start': 134.91, 'duration': 6.283}], 'summary': 'Text enables non-experts to create compelling images, leveraging large-scale data and pre-trained language models.', 'duration': 73.565, 'max_score': 67.628, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL467628.jpg'}, {'end': 322.295, 'src': 'embed', 'start': 292.208, 'weight': 3, 'content': [{'end': 297.817, 'text': 'So how is Muse different from the prior models that I listed?', 'start': 292.208, 'duration': 5.609}, {'end': 302.043, 'text': "So, firstly, it's neither a diffusion model nor is it autoregressive,", 'start': 298.458, 'duration': 3.585}, {'end': 307.391, 'text': 'although it has some connections to both diffusion and autoregressive family of models.', 'start': 302.043, 'duration': 5.348}, {'end': 309.829, 'text': 'It is extremely fast.', 'start': 308.668, 'duration': 1.161}, {'end': 311.67, 'text': 'So, for example,', 'start': 310.749, 'duration': 0.921}, {'end': 322.295, 'text': 'a 512 by 512 image is generated in 1.3 seconds instead of 10 seconds for image in our party and about four seconds for stable diffusion on the same hardware.', 'start': 311.67, 'duration': 10.625}], 'summary': 'Muse is faster, generating a 512x512 image in 1.3 seconds compared to 10 seconds for image in prior models and about 4 seconds for stable diffusion.', 'duration': 30.087, 'max_score': 292.208, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL4292208.jpg'}], 'start': 9.284, 'title': 'Text-to-image generation with muse model', 'summary': 'Presents muse, a new model for text-to-image generation, emphasizing the use of text as a control mechanism, feasibility of large-scale data collection, recognition of biases in datasets, and exploitation of pre-trained language models for fine-grained understanding of text parts of speech and semantic concepts.', 'chapters': [{'end': 311.67, 'start': 9.284, 'title': 'Text-to-image generation with muse model', 'summary': 'Discusses a new model named muse for text-to-image generation, highlighting the importance of text as a control mechanism, the feasibility of collecting large-scale paired image-text data, the recognition of biases in datasets, and the exploitation of pre-trained large language models for fine-grained understanding of text parts of speech and semantic concepts.', 'duration': 302.386, 'highlights': ['The importance of text as a natural control mechanism for generating compelling images and allowing non-experts to iterate them through editing tools to create personal art and ideas.', 'Feasibility of collecting large-scale paired image-text data, exemplified by the Lyon 5 billion dataset, and the recognition of biases in these datasets, emphasizing the importance of bias mitigation as a research problem.', 'Exploitation of pre-trained large language models for fine-grained understanding of text parts of speech, nouns, verbs, adjectives, and translating those semantic concepts to output images, with the ability to pre-train with text-only data and then use paired data for text-to-image translation training.', 'Comparison of Muse with prior models, highlighting its differences as neither a diffusion model nor autoregressive, its speed, and its connections to both diffusion and autoregressive family of models.']}], 'duration': 302.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL49284.jpg', 'highlights': ['Exploitation of pre-trained large language models for fine-grained understanding of text parts of speech, nouns, verbs, adjectives, and translating those semantic concepts to output images, with the ability to pre-train with text-only data and then use paired data for text-to-image translation training.', 'The importance of text as a natural control mechanism for generating compelling images and allowing non-experts to iterate them through editing tools to create personal art and ideas.', 'Feasibility of collecting large-scale paired image-text data, exemplified by the Lyon 5 billion dataset, and the recognition of biases in these datasets, emphasizing the importance of bias mitigation as a research problem.', 'Comparison of Muse with prior models, highlighting its differences as neither a diffusion model nor autoregressive, its speed, and its connections to both diffusion and autoregressive family of models.']}, {'end': 1043.26, 'segs': [{'end': 362.313, 'src': 'embed', 'start': 311.67, 'weight': 0, 'content': [{'end': 322.295, 'text': 'a 512 by 512 image is generated in 1.3 seconds instead of 10 seconds for image in our party and about four seconds for stable diffusion on the same hardware.', 'start': 311.67, 'duration': 10.625}, {'end': 332.567, 'text': 'And on quantitative eval, such as clip score and FID, which are measures of how well the text prompt and the image line up with each other.', 'start': 323.844, 'duration': 8.723}, {'end': 338.789, 'text': "that's the clip score and FID is a measure of the image quality itself, the diversity and fidelity.", 'start': 332.567, 'duration': 6.222}, {'end': 340.63, 'text': 'the model performs very well.', 'start': 338.789, 'duration': 1.841}, {'end': 349.393, 'text': 'So it has similar semantic performance and quality as these much larger models, but significantly faster inference,', 'start': 342.631, 'duration': 6.762}, {'end': 352.354, 'text': 'and it has significantly better performance than stable diffusion.', 'start': 349.393, 'duration': 2.961}, {'end': 355.591, 'text': 'All of these statements just hold true at this point in time.', 'start': 353.23, 'duration': 2.361}, {'end': 357.351, 'text': 'All of these models keep improving.', 'start': 355.711, 'duration': 1.64}, {'end': 362.313, 'text': 'So there could be a new model that does even better next week.', 'start': 357.631, 'duration': 4.682}], 'summary': '512x512 image generated in 1.3s, outperforming stable diffusion by 6.7s, with high clip score and fid, indicating fast and high-quality performance.', 'duration': 50.643, 'max_score': 311.67, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL4311670.jpg'}, {'end': 421.882, 'src': 'embed', 'start': 388.989, 'weight': 1, 'content': [{'end': 399.503, 'text': 'Muse is mostly a transformer-based architecture for both the text and image parts of the network, but we also use CNNs.', 'start': 388.989, 'duration': 10.514}, {'end': 404.752, 'text': 'We also use vector quantization and we also use GAN.', 'start': 402.15, 'duration': 2.602}, {'end': 413.557, 'text': "So we're using a lot of the toolkits in the modern CNN, modern deep network from the toolbox.", 'start': 404.772, 'duration': 8.785}, {'end': 421.882, 'text': 'We use image tokens that are in the quantized latent space of a CNN VQGAN, and we train it with a masking loss.', 'start': 414.738, 'duration': 7.144}], 'summary': 'Muse uses transformer-based architecture for text and image, employing cnns, vector quantization, and gan in the modern deep network toolbox.', 'duration': 32.893, 'max_score': 388.989, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL4388989.jpg'}, {'end': 476.862, 'src': 'embed', 'start': 441.634, 'weight': 2, 'content': [{'end': 456.959, 'text': 'We have two models, a base model that generates 256 by 256 size images, and then a super resolution model that upscales that to 512 by 512.', 'start': 441.634, 'duration': 15.325}, {'end': 461.14, 'text': 'So the first major component is this pre-trained large language model.', 'start': 456.959, 'duration': 4.181}, {'end': 468.122, 'text': 'So we use a language model trained at Google called the T5 XXL model.', 'start': 461.58, 'duration': 6.542}, {'end': 476.862, 'text': 'which has about 5 billion parameters, which was trained on many text tasks, text-to-text tasks such as translation,', 'start': 468.757, 'duration': 8.105}], 'summary': 'Two models: base generates 256x256 images, super resolution upscales to 512x512. t5 xxl model with 5b parameters used.', 'duration': 35.228, 'max_score': 441.634, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL4441634.jpg'}, {'end': 672.337, 'src': 'embed', 'start': 647.604, 'weight': 5, 'content': [{'end': 657.687, 'text': 'here we use a variable distribution which is biased towards a very high value of about 64% of the tokens being dropped.', 'start': 647.604, 'duration': 10.083}, {'end': 665.89, 'text': 'And we find that this makes the network much more amenable to editing applications like inpainting and uncropping.', 'start': 658.307, 'duration': 7.583}, {'end': 672.337, 'text': "And since it's variable, uh, inference time, you can then pass in masks of different sizes.", 'start': 666.35, 'duration': 5.987}], 'summary': 'Variable distribution drops 64% tokens, enhancing network for editing apps and allowing masks of different sizes.', 'duration': 24.733, 'max_score': 647.604, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL4647604.jpg'}, {'end': 1018.016, 'src': 'embed', 'start': 993.244, 'weight': 6, 'content': [{'end': 999.49, 'text': 'So the idea here is that there are concepts which one cannot say in the text prompt itself.', 'start': 993.244, 'duration': 6.246}, {'end': 1006.996, 'text': "For example, if you would like to generate an image but not have trees in it, it's somewhat cumbersome to say that in the text prompt.", 'start': 999.57, 'duration': 7.426}, {'end': 1014.835, 'text': "So the classifier-free guidance allows us to say, generate this scene, but don't have trees in it.", 'start': 1008.251, 'duration': 6.584}, {'end': 1018.016, 'text': 'And we do that by pushing away from the negative prompt.', 'start': 1015.035, 'duration': 2.981}], 'summary': 'Using classifier-free guidance to generate scenes without specific objects.', 'duration': 24.772, 'max_score': 993.244, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL4993244.jpg'}], 'start': 311.67, 'title': 'Efficient image generation model and token-based super resolution with muse', 'summary': 'Discusses an efficient image generation model that generates a 512x512 image in 1.3 seconds, outperforming the previous model, and muse, a transformer-based architecture for text and image generation with 5 billion parameters and 8192 tokens, achieving super resolution from 256x256 to 512x512.', 'chapters': [{'end': 362.313, 'start': 311.67, 'title': 'Efficient image generation model', 'summary': 'Discusses a 512 by 512 image generated in 1.3 seconds, outperforming the previous model by a large margin, with significantly better performance than stable diffusion, and similar semantic performance and quality as larger models.', 'duration': 50.643, 'highlights': ["The model generates a 512 by 512 image in 1.3 seconds, significantly faster than the previous model's 10 seconds for image generation and about four seconds for stable diffusion on the same hardware.", 'The model shows similar semantic performance and quality as much larger models, with significantly better performance than stable diffusion.', "Quantitative evaluation measures such as clip score and FID indicate the model's strong performance, with clip score assessing the alignment of text prompt and image, and FID measuring image quality, diversity, and fidelity."]}, {'end': 1043.26, 'start': 363.593, 'title': 'Token-based super resolution with muse', 'summary': 'Discusses muse, a transformer-based architecture for text and image generation, incorporating vector quantization and gan, enabling applications such as zero-shot editing, mask-free editing, and uncropping. it uses a language model with 5 billion parameters and a vqgan with 8192 tokens, achieving a super resolution from 256x256 to 512x512, and employs variable ratio masking during training to improve editing applications. muse also utilizes classifier-free guidance and negative prompting to enhance diversity and quality in image generation.', 'duration': 679.667, 'highlights': ['Muse utilizes a transformer-based architecture for text and image generation, incorporating vector quantization and GAN, enabling applications such as zero-shot editing, mask-free editing, and uncropping.', 'The language model used has 5 billion parameters, and a VQGAN with 8192 tokens achieves a super resolution from 256x256 to 512x512.', "Variable ratio masking during training significantly increases the network's amenable to editing applications like inpainting and uncropping.", 'Muse employs classifier-free guidance and negative prompting to enhance diversity and quality in image generation.']}], 'duration': 731.59, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL4311670.jpg', 'highlights': ["The model generates a 512 by 512 image in 1.3 seconds, significantly faster than the previous model's 10 seconds for image generation and about four seconds for stable diffusion on the same hardware.", 'Muse utilizes a transformer-based architecture for text and image generation, incorporating vector quantization and GAN, enabling applications such as zero-shot editing, mask-free editing, and uncropping.', 'The language model used has 5 billion parameters, and a VQGAN with 8192 tokens achieves a super resolution from 256x256 to 512x512.', "Quantitative evaluation measures such as clip score and FID indicate the model's strong performance, with clip score assessing the alignment of text prompt and image, and FID measuring image quality, diversity, and fidelity.", 'The model shows similar semantic performance and quality as much larger models, with significantly better performance than stable diffusion.', "Variable ratio masking during training significantly increases the network's amenable to editing applications like inpainting and uncropping.", 'Muse employs classifier-free guidance and negative prompting to enhance diversity and quality in image generation.']}, {'end': 1368.53, 'segs': [{'end': 1080.74, 'src': 'embed', 'start': 1044.941, 'weight': 0, 'content': [{'end': 1052.525, 'text': "So that is a very useful trick for generating images closer to the things you're thinking of in your head.", 'start': 1044.941, 'duration': 7.584}, {'end': 1063.19, 'text': 'This is the iterative decoding I mentioned earlier at inference time.', 'start': 1058.567, 'duration': 4.623}, {'end': 1072.355, 'text': 'And you can see that decoding tokens in multiple steps is very helpful for high quality generation.', 'start': 1064.09, 'duration': 8.265}, {'end': 1080.74, 'text': "So here's a sequence of unmasking steps, which are generating tokens up to 18 steps.", 'start': 1072.956, 'duration': 7.784}], 'summary': 'Iterative decoding improves image generation with up to 18 steps.', 'duration': 35.799, 'max_score': 1044.941, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41044941.jpg'}, {'end': 1169.09, 'src': 'embed', 'start': 1137.607, 'weight': 1, 'content': [{'end': 1139.328, 'text': 'So we did some qualitative evals,', 'start': 1137.607, 'duration': 1.721}, {'end': 1157.814, 'text': 'where we took a set of 1650 prompts from the party paper and generated images from our model and the stable diffusion model and sent it to raters to answer the question which of the two images one from our model,', 'start': 1139.328, 'duration': 18.486}, {'end': 1160.615, 'text': 'one from stable diffusion which of them matches the prompt better?', 'start': 1157.814, 'duration': 2.801}, {'end': 1164.197, 'text': 'So the raters preferred our model 70% of the time.', 'start': 1161.636, 'duration': 2.561}, {'end': 1169.09, 'text': 'compared to 25% of the time for stable diffusion.', 'start': 1165.949, 'duration': 3.141}], 'summary': 'Raters preferred our model 70% of the time over stable diffusion (25%).', 'duration': 31.483, 'max_score': 1137.607, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41137607.jpg'}, {'end': 1368.53, 'src': 'embed', 'start': 1281.096, 'weight': 2, 'content': [{'end': 1288.523, 'text': 'which is really really powerful and able to represent each of those concepts in the embedding space.', 'start': 1281.096, 'duration': 7.427}, {'end': 1292.146, 'text': "And what we're able to do is to map that to pixels.", 'start': 1289.283, 'duration': 2.863}, {'end': 1295.295, 'text': "It's still mind blowing, though.", 'start': 1294.335, 'duration': 0.96}, {'end': 1299.456, 'text': "It's still amazing that we can get these kinds of outputs from text prompts.", 'start': 1295.435, 'duration': 4.021}, {'end': 1303.197, 'text': 'Here are some failure cases, like I mentioned.', 'start': 1300.996, 'duration': 2.201}, {'end': 1311.958, 'text': 'Here we asked the model to render a number of words, and it did not do such a good job.', 'start': 1305.797, 'duration': 6.161}, {'end': 1319.92, 'text': '10 wine bottles, and it stopped at one, two, three, four, five, six, seven, and so on.', 'start': 1311.978, 'duration': 7.942}, {'end': 1328.139, 'text': "Here's a subjective comparison to other state-of-the-art models.", 'start': 1324.394, 'duration': 3.745}, {'end': 1339.116, 'text': 'One thing to say is that, In this space, the evaluations are, in my opinion, not very robust because, by definition,', 'start': 1330.402, 'duration': 8.714}, {'end': 1343.359, 'text': 'often the text prompts and the styles we ask for are not natural.', 'start': 1339.116, 'duration': 4.243}, {'end': 1345.76, 'text': 'We want various mix and match of styles.', 'start': 1343.459, 'duration': 2.301}, {'end': 1353.065, 'text': 'And so an important open research question in my mind is how do you evaluate that model A is better than model B,', 'start': 1346.541, 'duration': 6.524}, {'end': 1355.286, 'text': 'other than just looking at some results?', 'start': 1353.065, 'duration': 2.221}, {'end': 1361.37, 'text': "I think that's a very interesting and open question.", 'start': 1356.407, 'duration': 4.963}, {'end': 1368.53, 'text': "So here I'll just point out a couple of things with the example at the bottom,", 'start': 1362.306, 'duration': 6.224}], 'summary': 'Text-to-image model evaluation and challenges in mapping concepts to pixels.', 'duration': 87.434, 'max_score': 1281.096, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41281096.jpg'}], 'start': 1044.941, 'title': 'Image generation and model evaluation', 'summary': "Discusses an iterative decoding approach for high-quality image generation, with a 10x lower number of forward props compared to diffusion models, and qualitative evaluations showing a 70% preference for the model over stable diffusion in matching prompts. it also covers the model's ability to generate art based on text prompts, achieving good results with smaller word counts but struggling with larger quantities, emphasizing the challenge of evaluating models based on diverse style requests.", 'chapters': [{'end': 1193.156, 'start': 1044.941, 'title': 'Image generation and model evaluation', 'summary': 'Discusses an iterative decoding approach for high-quality image generation, with a 10x lower number of forward props compared to diffusion models, and qualitative evaluations showing a 70% preference for the model over stable diffusion in matching prompts.', 'duration': 148.215, 'highlights': ['Iterative decoding at inference time is very helpful for high quality generation, with a 10x lower number of forward props compared to diffusion models.', 'Qualitative evaluations showed a 70% preference for the model over stable diffusion in matching prompts, compared to 25% for stable diffusion.']}, {'end': 1368.53, 'start': 1194.774, 'title': "Model's art generation performance", 'summary': "Discusses the model's ability to generate art based on text prompts, achieving good results with smaller word counts but struggling with larger quantities, and emphasizes the challenge of evaluating models based on diverse style requests.", 'duration': 173.756, 'highlights': ['The model can render art well with one or two-word text prompts, but starts making mistakes with counts beyond six or seven.', "The model's power comes from the language model itself, which can represent concepts in the embedding space and map them to pixels, resulting in impressive outputs from text prompts.", 'Evaluating models based on diverse style requests poses a significant challenge, and there is an open research question regarding how to effectively compare models beyond simply looking at results.']}], 'duration': 323.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41044941.jpg', 'highlights': ['Iterative decoding at inference time is very helpful for high quality generation, with a 10x lower number of forward props compared to diffusion models.', 'Qualitative evaluations showed a 70% preference for the model over stable diffusion in matching prompts, compared to 25% for stable diffusion.', "The model's power comes from the language model itself, which can represent concepts in the embedding space and map them to pixels, resulting in impressive outputs from text prompts.", 'The model can render art well with one or two-word text prompts, but starts making mistakes with counts beyond six or seven.', 'Evaluating models based on diverse style requests poses a significant challenge, and there is an open research question regarding how to effectively compare models beyond simply looking at results.']}, {'end': 1720.26, 'segs': [{'end': 1510.838, 'src': 'embed', 'start': 1368.53, 'weight': 0, 'content': [{'end': 1376.416, 'text': "which is a rainbow-colored penguin and DALL-E2 is generating penguins but doesn't do such a good job with the colors,", 'start': 1368.53, 'duration': 7.886}, {'end': 1381.399, 'text': 'whereas the Imagen and Muse models seem to be able to respect both.', 'start': 1376.416, 'duration': 4.983}, {'end': 1390.405, 'text': 'And we think this is probably because the DALL-E2 model is relying on a clip embedding, which might lose some of these details.', 'start': 1382.119, 'duration': 8.286}, {'end': 1408.465, 'text': 'We did some quantitative eval on the metrics that we have, which is FID and CLIP, on a data set called CC3M, and compared to a number of other models,', 'start': 1397.237, 'duration': 11.228}, {'end': 1411.187, 'text': 'both in diffusion and autoregressive type models.', 'start': 1408.465, 'duration': 2.722}, {'end': 1417.331, 'text': 'Here for FID score lower is better and for CLIP higher is better.', 'start': 1413.188, 'duration': 4.143}, {'end': 1424.777, 'text': 'So overall we seem to be scoring the best on both of these metrics.', 'start': 1418.392, 'duration': 6.385}, {'end': 1435.649, 'text': "Here's the eval on COCO comparing to many of the state-of-the-art models like DALI, DALI 2, Imagine Party.", 'start': 1427.747, 'duration': 7.902}, {'end': 1443.152, 'text': 'We are almost as good as the Party 20 billion model.', 'start': 1438.29, 'duration': 4.862}, {'end': 1451.604, 'text': 'uh, just slightly worse in FID score, uh, but doing significantly better on CLIP score.', 'start': 1445.363, 'duration': 6.241}, {'end': 1455.265, 'text': "Um, so, so that's good.", 'start': 1452.465, 'duration': 2.8}, {'end': 1463.567, 'text': 'Which means that, uh, we are able to respect the semantics of the text prompt, uh, better than those models if one was to believe the CLIP score.', 'start': 1456.246, 'duration': 7.321}, {'end': 1469.629, 'text': 'Uh, and finally, a runtime on TPU.', 'start': 1463.587, 'duration': 6.042}, {'end': 1478.58, 'text': "uh, V4 hardware, uh, where Here's the model, the resolution that is generated and the time wall clock time that it took.", 'start': 1469.629, 'duration': 8.951}, {'end': 1483.261, 'text': 'So most of the compute goes into the super resolution.', 'start': 1479.18, 'duration': 4.081}, {'end': 1491.902, 'text': 'The base model takes 0.5 seconds and then it takes another 0.8 seconds to do the super resolution for a total of 1.3 seconds.', 'start': 1483.881, 'duration': 8.021}, {'end': 1500.324, 'text': 'So now here are some examples of the editing that are enabled by the model.', 'start': 1494.883, 'duration': 5.441}, {'end': 1510.838, 'text': "On the left is a real input image and we've drawn a mask over one part of the image and then asked the model to fill that in with a text guided prompt.", 'start': 1501.548, 'duration': 9.29}], 'summary': 'Dall-e2 model excels in fid and clip scores, with a runtime of 1.3 seconds for super resolution.', 'duration': 142.308, 'max_score': 1368.53, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41368530.jpg'}, {'end': 1662.559, 'src': 'embed', 'start': 1575.652, 'weight': 2, 'content': [{'end': 1585.715, 'text': 'a man wearing a T-shirt and then these were the positive prompts to do various kinds of style transfer on the T-shirt.', 'start': 1575.652, 'duration': 10.063}, {'end': 1596.998, 'text': 'And here are some examples of mask-free editing where on the top row are input images.', 'start': 1591.236, 'duration': 5.762}, {'end': 1606.531, 'text': 'And on the bottom are the transformed outputs, where it just relies on the text and attention between the text and images to make various changes.', 'start': 1597.808, 'duration': 8.723}, {'end': 1617.915, 'text': 'So for example, here we say a Shiba Inu, and the model converts the cat to a Shiba Inu dog.', 'start': 1607.831, 'duration': 10.084}, {'end': 1621.316, 'text': 'A dog holding a football in its mouth.', 'start': 1619.495, 'duration': 1.821}, {'end': 1626.998, 'text': 'So here the dog itself changes, and then this ball changes to a football.', 'start': 1621.836, 'duration': 5.162}, {'end': 1636.029, 'text': 'A basket of oranges, where the model is able to keep the general composition of the scene and just change the apples to oranges.', 'start': 1628.044, 'duration': 7.985}, {'end': 1642.032, 'text': 'The basket texture has also changed a little bit, but mostly the composition is kept similar.', 'start': 1636.909, 'duration': 5.123}, {'end': 1651.418, 'text': "Here, it's able to just make the cat yawn without changing the composition of the scene.", 'start': 1643.953, 'duration': 7.465}, {'end': 1657.197, 'text': 'And one of the things we are exploring is how we can have further control of the model.', 'start': 1652.555, 'duration': 4.642}, {'end': 1662.559, 'text': 'you know really be able to adjust specific parts of the image without affecting the rest.', 'start': 1657.197, 'duration': 5.362}], 'summary': "Style transfer and mask-free editing demonstrated with image examples, showcasing the model's ability to make various changes based on text and attention between text and images.", 'duration': 86.907, 'max_score': 1575.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41575652.jpg'}, {'end': 1720.26, 'src': 'embed', 'start': 1698.427, 'weight': 6, 'content': [{'end': 1714.518, 'text': 'so you can see that it starts with the cake and the latte and then progressively transforms the cake to something that looks in between the cake and the croissant and then finally it looks like the croissant And similarly the latte art changes from a heart to a flower.', 'start': 1698.427, 'duration': 16.091}, {'end': 1720.26, 'text': 'you know, kind of some kind of an interpolation in some latent space.', 'start': 1714.518, 'duration': 5.742}], 'summary': 'Cake and latte morph into croissant, latte art changes from heart to flower.', 'duration': 21.833, 'max_score': 1698.427, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41698427.jpg'}], 'start': 1368.53, 'title': 'Evaluating dall-e2 and imagen models, text-guided image editing, and style transfer', 'summary': "Evaluates dall-e2 and imagen models' performance using fid and clip metrics on cc3m and coco datasets, discusses text-guided image editing with zero-shot results, and explores style transfer and mask-free editing capabilities, including converting images and iterative adjustments.", 'chapters': [{'end': 1455.265, 'start': 1368.53, 'title': 'Evaluation of dall-e2 and imagen models', 'summary': 'Discusses the evaluation of dall-e2 and imagen models using fid and clip metrics on the cc3m dataset, where the imagen model outperformed dall-e2 in generating rainbow-colored penguins and scored higher on the clip metric, with slight difference in fid scores. furthermore, the evaluation on coco dataset showed that the imagen model performed almost as well as the party 20 billion model in terms of fid score, while significantly outperforming it on the clip score.', 'duration': 86.735, 'highlights': ['The Imagen model outperformed DALL-E2 in generating rainbow-colored penguins and scored higher on the CLIP metric, with slight difference in FID scores.', 'The evaluation on COCO dataset showed that the Imagen model performed almost as well as the Party 20 billion model in terms of FID score, while significantly outperforming it on the CLIP score.', 'The DALL-E2 model is relying on a clip embedding, which might lose some details in the generated images.', 'For FID score lower is better and for CLIP higher is better. Overall, the Imagen model scored the best on both of these metrics.']}, {'end': 1575.652, 'start': 1456.246, 'title': 'Text-guided image editing', 'summary': "Discusses a model's ability to respect text prompts, its runtime on tpu v4 hardware, and examples of text-guided image editing, achieving zero-shot results without fine-tuning.", 'duration': 119.406, 'highlights': ["The model's ability to respect text prompts outperforms other models based on the CLIP score, with a runtime of 1.3 seconds for super resolution computation.", 'Examples of text-guided image editing demonstrate zero-shot results without fine-tuning, enabling tasks such as outpainting and filling in regions based on text prompts.', "The model's training with variable masking allows it to perform various editing tasks, such as filling regions based on text prompts, achieving impressive results without fine-tuning."]}, {'end': 1720.26, 'start': 1575.652, 'title': 'Style transfer and mask-free editing', 'summary': 'Discusses the use of text and attention between text and images to perform style transfer on t-shirts and mask-free editing on images using a model that can make various changes based on different input images, such as converting a cat to a shiba inu dog and changing apples to oranges, and explores the potential for further control of the editing model through iterative adjustments.', 'duration': 144.608, 'highlights': ['The model converts a cat to a Shiba Inu dog and changes a ball to a football based on text prompts, showcasing the capability of the model for image transformation.', 'The model is able to keep the general composition of a scene while changing apples to oranges, demonstrating its ability to preserve scene composition during editing.', 'The chapter explores the potential for further control of the editing model to adjust specific parts of an image without affecting the rest, indicating ongoing research and development in this area.', "Iterative editing results in progressively transforming an image from a cake to a croissant and changing latte art from a heart to a flower, showcasing the model's ability for iterative adjustments and interpolation in latent space."]}], 'duration': 351.73, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41368530.jpg', 'highlights': ['The Imagen model outperformed DALL-E2 in generating rainbow-colored penguins and scored higher on the CLIP metric, with slight difference in FID scores.', "The model's ability to respect text prompts outperforms other models based on the CLIP score, with a runtime of 1.3 seconds for super resolution computation.", 'The model converts a cat to a Shiba Inu dog and changes a ball to a football based on text prompts, showcasing the capability of the model for image transformation.', 'The evaluation on COCO dataset showed that the Imagen model performed almost as well as the Party 20 billion model in terms of FID score, while significantly outperforming it on the CLIP score.', 'Examples of text-guided image editing demonstrate zero-shot results without fine-tuning, enabling tasks such as outpainting and filling in regions based on text prompts.', "The model's training with variable masking allows it to perform various editing tasks, such as filling regions based on text prompts, achieving impressive results without fine-tuning.", "Iterative editing results in progressively transforming an image from a cake to a croissant and changing latte art from a heart to a flower, showcasing the model's ability for iterative adjustments and interpolation in latent space.", 'The chapter explores the potential for further control of the editing model to adjust specific parts of an image without affecting the rest, indicating ongoing research and development in this area.', 'The DALL-E2 model is relying on a clip embedding, which might lose some details in the generated images.', 'For FID score lower is better and for CLIP higher is better. Overall, the Imagen model scored the best on both of these metrics.']}, {'end': 2168.337, 'segs': [{'end': 1846.057, 'src': 'embed', 'start': 1723.261, 'weight': 0, 'content': [{'end': 1728.182, 'text': "So because of the speed of the model, there's some possibility of interactive editing.", 'start': 1723.261, 'duration': 4.921}, {'end': 1735.605, 'text': "So I'll just play this short clip which shows how we might be able to do interactive work with the model.", 'start': 1728.563, 'duration': 7.042}, {'end': 1769.179, 'text': "So that's real time, as in it's not sped up or slowed down.", 'start': 1765.174, 'duration': 4.005}, {'end': 1781.836, 'text': "It's not perfect, but you can see the idea.", 'start': 1779.252, 'duration': 2.584}, {'end': 1800.402, 'text': 'OK. so next steps for us are improving the resolution quality handling of details such as rendered text.', 'start': 1790.939, 'duration': 9.463}, {'end': 1804.743, 'text': 'probing the cross-attention between text and images to enable more control.', 'start': 1800.402, 'duration': 4.341}, {'end': 1805.863, 'text': 'exploring applications.', 'start': 1804.743, 'duration': 1.12}, {'end': 1812.025, 'text': 'So yeah, the paper and web page are listed here.', 'start': 1806.984, 'duration': 5.041}, {'end': 1814.026, 'text': "And I'm happy to take questions.", 'start': 1812.365, 'duration': 1.661}, {'end': 1814.766, 'text': 'Thank you.', 'start': 1814.506, 'duration': 0.26}, {'end': 1823.05, 'text': 'Excellent. Thank you so much.', 'start': 1821.59, 'duration': 1.46}, {'end': 1825.851, 'text': 'maybe I have one question, just to get things started.', 'start': 1823.05, 'duration': 2.801}, {'end': 1830.252, 'text': "i'm curious in your opinion, what are the most important contributions that muse made?", 'start': 1825.851, 'duration': 4.401}, {'end': 1837.254, 'text': "Specifically to achieve the very impressive speed up results right because in comparison to the past methods it's really like a huge gap.", 'start': 1830.993, 'duration': 6.261}, {'end': 1837.935, 'text': "so i'm curious what.", 'start': 1837.254, 'duration': 0.681}, {'end': 1846.057, 'text': 'Like what across the many contributions led to that, in your opinion, it was primarily the parallel decoding so in autoregressive models.', 'start': 1838.455, 'duration': 7.602}], 'summary': 'Model enables real-time interactive editing, with focus on improving resolution and text-image interaction.', 'duration': 122.796, 'max_score': 1723.261, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41723261.jpg'}, {'end': 1916.824, 'src': 'embed', 'start': 1866.15, 'weight': 3, 'content': [{'end': 1873.072, 'text': 'yeah, you start with pure noise, pass it through the network, get something out, then pass it back in and repeat this process.', 'start': 1866.15, 'duration': 6.922}, {'end': 1879.395, 'text': 'And that process needs to be fairly slow, otherwise it breaks down.', 'start': 1874.213, 'duration': 5.182}, {'end': 1882.577, 'text': 'And a lot of the research is about how to speed that up.', 'start': 1880.336, 'duration': 2.241}, {'end': 1884.678, 'text': 'So that takes thousands of steps.', 'start': 1883.437, 'duration': 1.241}, {'end': 1889.56, 'text': "So if you have a comparable model, there's just a fundamental number of forward props that need to be done.", 'start': 1884.698, 'duration': 4.862}, {'end': 1892.721, 'text': 'So what we did instead was to go for parallel decoding.', 'start': 1890.02, 'duration': 2.701}, {'end': 1899.138, 'text': 'And there, instead of one token at a time, you just do and tokens at a time and then,', 'start': 1893.562, 'duration': 5.576}, {'end': 1907.581, 'text': "if it's fixed and you just need 196 by n steps and the idea is that if you use high confidence tokens,", 'start': 1899.138, 'duration': 8.443}, {'end': 1916.824, 'text': 'then they are potentially conditionally independent of each other at each step and we can each of them can be predicted without affecting the other.', 'start': 1907.581, 'duration': 9.243}], 'summary': 'Research focuses on speeding up neural network processing, aiming for parallel decoding and high confidence token use.', 'duration': 50.674, 'max_score': 1866.15, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41866150.jpg'}, {'end': 2047.265, 'src': 'embed', 'start': 2007.962, 'weight': 5, 'content': [{'end': 2012.906, 'text': 'The editing suggests some smoothness here, as you iterate.', 'start': 2007.962, 'duration': 4.944}, {'end': 2016.008, 'text': 'But this is with a fixed prompt, not with a changing prompt.', 'start': 2013.646, 'duration': 2.362}, {'end': 2018.51, 'text': 'So I think we need to do more.', 'start': 2017.509, 'duration': 1.001}, {'end': 2041.38, 'text': 'Any other questions? Yeah, I had a question about the cardinality portion of the results.', 'start': 2022.432, 'duration': 18.948}, {'end': 2047.265, 'text': "One of the failure cases showed that the model can't really handle if you give it more than six or seven of the same item, but sometimes,", 'start': 2041.44, 'duration': 5.825}], 'summary': 'Editing suggests smoothness, but model fails with more than 6-7 of the same item.', 'duration': 39.303, 'max_score': 2007.962, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42007962.jpg'}, {'end': 2155.46, 'src': 'embed', 'start': 2126.34, 'weight': 6, 'content': [{'end': 2129.781, 'text': 'how does it work when you do not specify the background, for example, at all?', 'start': 2126.34, 'duration': 3.441}, {'end': 2131.062, 'text': "Yeah, it's a great question.", 'start': 2130.181, 'duration': 0.881}, {'end': 2135.263, 'text': 'So one thing we did was just type in nonsense into the text prompt and see what happens.', 'start': 2131.422, 'duration': 3.841}, {'end': 2142.835, 'text': 'And it seems to generate just random scenes like of, You know, mountains and the beach and so on.', 'start': 2135.823, 'duration': 7.012}, {'end': 2144.556, 'text': "it doesn't generate nonsense.", 'start': 2142.835, 'duration': 1.721}, {'end': 2155.46, 'text': "so we think that the code book in the latent space is dominated by these kinds of backgrounds and somehow that's what gets fed in when you go through the decoder.", 'start': 2144.556, 'duration': 10.904}], 'summary': 'The model generates random scenes dominated by backgrounds like mountains and beaches when fed with nonsense text prompts.', 'duration': 29.12, 'max_score': 2126.34, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42126340.jpg'}], 'start': 1723.261, 'title': 'Interactive editing and parallel decoding', 'summary': 'Explores interactive editing with a high-speed model, emphasizing real-time capabilities, future improvements in resolution quality, and cross-attention between text and images, alongside the primary contribution of parallel decoding in achieving significant speed-up results. it also delves into the use of parallel decoding in diffusion models to expedite the denoising process, focusing on the number of steps needed, exploration of latent space, and challenges related to cardinality and background generation.', 'chapters': [{'end': 1846.057, 'start': 1723.261, 'title': 'Interactive editing with high-speed model', 'summary': 'Discusses the possibility of interactive editing with a high-speed model, real-time capabilities, future improvements in resolution quality and cross-attention between text and images, and the primary contribution of parallel decoding in achieving significant speed-up results.', 'duration': 122.796, 'highlights': ['The model allows for interactive editing due to its high speed, enabling real-time capabilities.', 'Future improvements include enhancing resolution quality, handling rendered text, and probing cross-attention between text and images to enable more control.', 'The primary contribution to achieving significant speed-up results is attributed to parallel decoding in autoregressive models.']}, {'end': 2168.337, 'start': 1846.937, 'title': 'Parallel decoding in diffusion models', 'summary': 'Discusses the use of parallel decoding in diffusion models to speed up the denoising process, with a focus on the number of steps needed and the exploration of latent space, while highlighting the challenges related to handling cardinality and background generation.', 'duration': 321.4, 'highlights': ['Diffusion models require thousands of steps for denoising, and the research focuses on speeding up this process.', 'Parallel decoding in diffusion models allows for decoding multiple tokens at a time, potentially making high-confidence tokens conditionally independent of each other at each step.', 'Analysis of cross-attention maps between text embeddings and generated images shows relationships between nouns, verbs, and objects, providing insights into the exploration of latent space.', "Challenges exist in handling cardinality, with the model struggling when given more than six or seven of the same item, possibly due to the scarcity of larger numbers in the training data and the lack of graceful degradation in the model's generation process.", 'When not specifying a background, the model generates random scenes such as mountains and beaches, indicating a dominance of certain background types in the latent space code book.']}], 'duration': 445.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL41723261.jpg', 'highlights': ['Parallel decoding in autoregressive models contributes to significant speed-up results', 'The model enables interactive editing with high speed and real-time capabilities', 'Future improvements focus on enhancing resolution quality and cross-attention between text and images', 'Diffusion models require thousands of steps for denoising, prompting research on speeding up the process', 'Parallel decoding in diffusion models allows for decoding multiple tokens at a time', 'Challenges exist in handling cardinality, particularly with larger numbers', 'The model generates random scenes like mountains and beaches when not specifying a background']}, {'end': 2672.015, 'segs': [{'end': 2256.355, 'src': 'embed', 'start': 2215.105, 'weight': 0, 'content': [{'end': 2221.25, 'text': 'part of the problem could be the way we do the editing, which is based on these small back prop steps which just allow you to do local changes.', 'start': 2215.105, 'duration': 6.145}, {'end': 2232.154, 'text': "So we don't know if it's a limitation of the model or a limitation of the gradient, the SGD steps we do when we're doing the editing.", 'start': 2222.068, 'duration': 10.086}, {'end': 2240.118, 'text': 'So what happens with the editing is start with the original image, then take the text prompt and just keep back propping till it settles down.', 'start': 2232.194, 'duration': 7.924}, {'end': 2244.421, 'text': 'converges, say 100 steps.', 'start': 2240.118, 'duration': 4.303}, {'end': 2249.524, 'text': 'And so each of those steps, like I showed here, is small changes.', 'start': 2245.281, 'duration': 4.243}, {'end': 2256.355, 'text': 'And if you want something like what you described, you need to have the ability to kind of jump faster through 100 steps.', 'start': 2250.33, 'duration': 6.025}], 'summary': 'Editing process involves small back prop steps, may need faster progression', 'duration': 41.25, 'max_score': 2215.105, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42215105.jpg'}, {'end': 2337.923, 'src': 'embed', 'start': 2311.317, 'weight': 4, 'content': [{'end': 2322.299, 'text': 'I think it seems much harder to do realistic pose changes like the kinds he was asking about compared to these global style changes.', 'start': 2311.317, 'duration': 10.982}, {'end': 2328.401, 'text': 'Because I think the global style is controlled by maybe one or two elements of the code book or something like that.', 'start': 2322.439, 'duration': 5.962}, {'end': 2337.923, 'text': 'Whereas to change pose or make drastic geometry changes might require much more interaction among the different tokens.', 'start': 2329.421, 'duration': 8.502}], 'summary': 'Realistic pose changes are harder than global style changes due to increased token interaction.', 'duration': 26.606, 'max_score': 2311.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42311317.jpg'}, {'end': 2410.635, 'src': 'embed', 'start': 2376.932, 'weight': 2, 'content': [{'end': 2383.459, 'text': 'So what we do is just use random seeds and generate a whole bunch of images, and then we just pick out the one we like.', 'start': 2376.932, 'duration': 6.527}, {'end': 2388.904, 'text': 'So often what happens is that if you have eight images or 16, Three or four of them would be really nice.', 'start': 2383.779, 'duration': 5.125}, {'end': 2390.745, 'text': 'And then a few of them would look really bad.', 'start': 2389.284, 'duration': 1.461}, {'end': 2398.168, 'text': "And we still don't have a self-correcting way or an automated way to say this image matches the prompt better.", 'start': 2391.625, 'duration': 6.543}, {'end': 2410.635, 'text': 'So in general, the hope is that the latent space has been trained in a sensible way so that it will generate plausible images.', 'start': 2401.05, 'duration': 9.585}], 'summary': 'Using random seeds, we generate images; 3-4 out of 8-16 are nice. no automated way to match image to prompt.', 'duration': 33.703, 'max_score': 2376.932, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42376932.jpg'}, {'end': 2542.745, 'src': 'embed', 'start': 2491.784, 'weight': 3, 'content': [{'end': 2498.453, 'text': 'How much new information are we actually getting from these images that the model spits out?', 'start': 2491.784, 'duration': 6.669}, {'end': 2500.716, 'text': "So it's a great question.", 'start': 2498.473, 'duration': 2.243}, {'end': 2501.537, 'text': 'So for the data.', 'start': 2500.736, 'duration': 0.801}, {'end': 2514.587, 'text': "um, clearly the data is biased towards famous artists, and that's why we can say things, like you know, in the style of rembrandt or monet,", 'start': 2506.423, 'duration': 8.164}, {'end': 2520.09, 'text': 'and it has seen many examples of those, because this is data scraped from the web.', 'start': 2514.587, 'duration': 5.503}, {'end': 2529.074, 'text': 'if you had a new like the style of a new artist, the only current way to do it would be through fine tuning, where we take those images of the person,', 'start': 2520.09, 'duration': 8.984}, {'end': 2534.502, 'text': 'pair it with some text and then kind of train the model to generate that.', 'start': 2530.421, 'duration': 4.081}, {'end': 2542.745, 'text': "this is kind of what the dream booth approach tries to do, although that's specific to you know objects rather than the style,", 'start': 2534.502, 'duration': 8.243}], 'summary': 'Data biased towards famous artists, requires fine tuning for new styles.', 'duration': 50.961, 'max_score': 2491.784, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42491784.jpg'}, {'end': 2672.015, 'src': 'embed', 'start': 2646.015, 'weight': 5, 'content': [{'end': 2658.279, 'text': "But at the scale at which we are training hundreds of millions of images we haven't actually gone in and look to see how much is memorization versus new combination of concepts.", 'start': 2646.015, 'duration': 12.264}, {'end': 2665.201, 'text': "I do think it's the latter because it seems unlikely that these kinds of images would be in the training dataset.", 'start': 2658.619, 'duration': 6.582}, {'end': 2668.142, 'text': 'Okay, thank you.', 'start': 2667.56, 'duration': 0.582}, {'end': 2670.31, 'text': 'Thank you very much.', 'start': 2669.708, 'duration': 0.602}, {'end': 2672.015, 'text': "Let's give Dilip one more round of applause.", 'start': 2670.43, 'duration': 1.585}], 'summary': 'Training hundreds of millions of images to identify new combinations of concepts, likely not memorization.', 'duration': 26, 'max_score': 2646.015, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42646015.jpg'}], 'start': 2168.357, 'title': 'Image editing and ai generation', 'summary': 'Covers challenges in editing image prompts and limitations, as well as the complexities of ai image generation, including accuracy, resolution, artist styles, and biases.', 'chapters': [{'end': 2337.923, 'start': 2168.357, 'title': 'Editing image prompts', 'summary': 'Discusses the challenges of making large edits to image prompts, such as changing from a dog with a basketball to a cat bowling, and the limitations imposed by the small back prop steps and local changes during the editing process.', 'duration': 169.566, 'highlights': ['The limitations of the model and gradient during the editing process are discussed, as small back prop steps restrict the ability to make large changes.', 'The need for the ability to jump faster through 100 steps, or even a million steps, to achieve significant changes in the image prompt is highlighted.', 'The difficulty of making realistic pose changes compared to global style changes is mentioned, with global style changes being controlled by one or two elements of the code book.']}, {'end': 2672.015, 'start': 2349.058, 'title': 'Ai image generation q&a', 'summary': "Discusses the challenges in determining image accuracy, improving resolution, handling new artist styles, and the model's ability to generate new concepts, with a focus on training and data biases.", 'duration': 322.957, 'highlights': ['The model uses random seeds to generate multiple images and selects the preferred one, resulting in a few nice images out of a set of eight or 16, with potential arrangement mistakes. (Quantifiable: method of image selection)', 'Adapting the model to draw in the style of a new, unseen artist currently requires fine-tuning with specific images and text, indicating a need for future development in zero-shot learning for artistic styles. (Quantifiable: approach for new artist styles)', "The discussion raises the question of how much of the model's image generation is based on memorization vs. new combinations of concepts, highlighting the lack of comprehensive tools to differentiate between the two. (Quantifiable: need for tools to analyze image generation)", 'The data used for training the model is biased towards famous artists, limiting its ability to replicate styles of new, unknown artists. (Quantifiable: data bias towards famous artists)']}], 'duration': 503.658, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/SA-v6Op2kL4/pics/SA-v6Op2kL42168357.jpg', 'highlights': ['The need for the ability to jump faster through 100 steps, or even a million steps, to achieve significant changes in the image prompt is highlighted.', 'The limitations of the model and gradient during the editing process are discussed, as small back prop steps restrict the ability to make large changes.', 'The model uses random seeds to generate multiple images and selects the preferred one, resulting in a few nice images out of a set of eight or 16, with potential arrangement mistakes. (Quantifiable: method of image selection)', 'Adapting the model to draw in the style of a new, unseen artist currently requires fine-tuning with specific images and text, indicating a need for future development in zero-shot learning for artistic styles. (Quantifiable: approach for new artist styles)', 'The difficulty of making realistic pose changes compared to global style changes is mentioned, with global style changes being controlled by one or two elements of the code book.', "The discussion raises the question of how much of the model's image generation is based on memorization vs. new combinations of concepts, highlighting the lack of comprehensive tools to differentiate between the two. (Quantifiable: need for tools to analyze image generation)", 'The data used for training the model is biased towards famous artists, limiting its ability to replicate styles of new, unknown artists. (Quantifiable: data bias towards famous artists)']}], 'highlights': ['Muse model generates 512x512 image in 1.3s, 10x faster than previous model', 'Muse uses transformer-based architecture for text and image generation', "Muse's language model has 5B parameters, achieves super resolution from 256x256 to 512x512", 'Muse employs classifier-free guidance and negative prompting for image generation', 'Iterative decoding at inference time is helpful for high-quality generation', 'Imagen model outperforms DALL-E2 in generating rainbow-colored penguins', 'Parallel decoding in autoregressive models contributes to significant speed-up results', 'Model enables interactive editing with high speed and real-time capabilities', 'Need for future development in zero-shot learning for artistic styles', 'Data used for training the model is biased towards famous artists']}