Update README.md

README.md

@@ -189,51 +189,6 @@ Based on that information, we estimate the following CO2 emissions using the [Ma
 - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
 
 
-## Citation
-
-```bibtex
-    @InProceedings{Rombach_2022_CVPR,
-        author    = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
-        title     = {High-Resolution Image Synthesis With Latent Diffusion Models},
-        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-        month     = {June},
-        year      = {2022},
-        pages     = {10684-10695}
-    }
-```
-
-*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
-olution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
-filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
-- `sd-v1-3.ckpt`: Resumed from `sd-v1-2.ckpt`. 195k steps at resolution `512x512` on "laion-improved-aesthetics" and 10\% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
-
-
-- **Hardware:** 32 x 8 x A100 GPUs
-- **Optimizer:** AdamW
-- **Gradient Accumulations**: 2
-- **Batch:** 32 x 8 x 2 x 4 = 2048
-- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
-
-## Evaluation Results 
-Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
-5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling
-steps show the relative improvements of the checkpoints:
-
-![pareto](v1-variants-scores.jpg) 
-
-Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution.  Not optimized for FID scores.
-
-## Environmental Impact
-
-**Stable Diffusion v1** **Estimated Emissions**
-Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
-
-- **Hardware Type:** A100 PCIe 40GB
-- **Hours used:** 150000
-- **Cloud Provider:** AWS
-- **Compute Region:** US-east
-- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
-
 ## Citation
 
 ```bibtex