Update README.md

Author: stefanradev93

README.md

@@ -56,7 +56,7 @@ Before inference, we can use simulation-based calibration (SBC, https://arxiv.or
 ```python
 fig = trainer.diagnose_sbc_histograms()
 ```
-![SBC](https://github.com/stefanradev93/BayesFlow/blob/Future/img/showcase_sbc.png?raw=true)
+<img src="https://github.com/stefanradev93/BayesFlow/blob/Future/img/showcase_sbc.png" width=50% height=50%>
 Amortized inference on new (real or simulated) data is then easy and fast:
 ```python
 # Simulate 200 new data sets and generate 500 posterior draws per data set
@@ -67,12 +67,12 @@ We can then quickly inspect the parameter recoverability of the model
 ```python
 fig = bf.diagnostics.plot_recovery(posterior_draws, new_sims['parameters'])
 ```
-![Recovery](https://github.com/stefanradev93/BayesFlow/blob/Future/img/showcase_recovery.png?raw=true)
+<img src="https://github.com/stefanradev93/BayesFlow/blob/Future/img/showcase_recovery.png" width=50% height=50%>
 or look at single posteriors in relation to the prior:
 ```python
 fig = bf.diagnostics.plot_posterior_2d(posterior_draws[0], prior=generative_model.prior)
 ```
-![Posterior](https://github.com/stefanradev93/BayesFlow/blob/Future/img/showcase_posterior.png?raw=true)
+<img src="https://github.com/stefanradev93/BayesFlow/blob/Future/img/showcase_posterior.png" width=40% height=40%>
 
 ### Further Reading