Merge branch 'main' into forest_flow

atong01 · Nov 10, 2023 · 703dbb6 · 703dbb6
2 parents 1ae5690 + 21cd0c8
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diff --git a/examples/cifar10/README.md b/examples/cifar10/README.md
@@ -14,10 +14,10 @@ To reproduce the experiments and save the weights, install the requirements from
 python3 train_cifar10.py --model "otcfm" --lr 2e-4 --ema_decay 0.9999 --batch_size 128 --total_steps 400001 --save_step 20000
 ```
 
-- For the Conditional Flow Matching method:
+- For the Independent Conditional Flow Matching (I-CFM) method:
 
 ```bash
-python3 train_cifar10.py --model "cfm" --lr 2e-4 --ema_decay 0.9999 --batch_size 128 --total_steps 400001 --save_step 20000
+python3 train_cifar10.py --model "icfm" --lr 2e-4 --ema_decay 0.9999 --batch_size 128 --total_steps 400001 --save_step 20000
 ```
 
 - For the original Flow Matching method:
@@ -32,9 +32,9 @@ To compute the FID from the OT-CFM model at end of training, run:
 python3 compute_fid.py --model "otcfm" --step 400000 --integration_method dopri5
 ```
 
-For the other models, change the "otcfm" argument by "cfm" or "fm". For easy reproducibility of our results, you can download the model weights at 400000 iterations here:
+For the other models, change the "otcfm" argument by "icfm" or "fm". For easy reproducibility of our results, you can download the model weights at 400000 iterations here:
 
-- [cfm weights](https://github.com/atong01/conditional-flow-matching/releases/download/1.0.4/cfm_cifar10_weights_step_400000.pt)
+- [icfm weights](https://github.com/atong01/conditional-flow-matching/releases/download/1.0.4/cfm_cifar10_weights_step_400000.pt)
 
 - [otcfm weights](https://github.com/atong01/conditional-flow-matching/releases/download/1.0.4/otcfm_cifar10_weights_step_400000.pt)
 

diff --git a/examples/cifar10/train_cifar10.py b/examples/cifar10/train_cifar10.py
@@ -128,13 +128,13 @@ def train(argv):
     sigma = 0.0
     if FLAGS.model == "otcfm":
         FM = ExactOptimalTransportConditionalFlowMatcher(sigma=sigma)
-    elif FLAGS.model == "cfm":
+    elif FLAGS.model == "icfm":
         FM = ConditionalFlowMatcher(sigma=sigma)
     elif FLAGS.model == "fm":
         FM = TargetConditionalFlowMatcher(sigma=sigma)
     else:
         raise NotImplementedError(
-            f"Unknown model {FLAGS.model}, must be one of ['otcfm', 'cfm', 'fm']"
+            f"Unknown model {FLAGS.model}, must be one of ['otcfm', 'icfm', 'fm']"
         )
 
     savedir = FLAGS.output_dir + FLAGS.model + "/"