Stop passing fps to the transformer in Cosmos2VideoToWorldPipeline

src/diffusers/pipelines/cosmos/pipeline_cosmos2_video2world.py

@@ -538,7 +538,10 @@ def __call__(
                 of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
                 `guidance_scale > 1`.
             fps (`int`, defaults to `16`):
-                The frames per second of the generated video.
+                The frames per second of the generated video. This is only a playback property used when saving the
+                output with e.g. [`export_to_video`]; it does not affect the denoising process. The released
+                Cosmos-Predict2 checkpoints were trained without FPS-conditioned RoPE, so `fps` is not passed to the
+                transformer.
             num_videos_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
@@ -710,7 +713,6 @@ def __call__(
                     hidden_states=cond_latent,
                     timestep=cond_timestep,
                     encoder_hidden_states=prompt_embeds,
-                    fps=fps,
                     condition_mask=cond_mask,
                     padding_mask=padding_mask,
                     return_dict=False,
@@ -729,7 +731,6 @@ def __call__(
                         hidden_states=uncond_latent,
                         timestep=uncond_timestep,
                         encoder_hidden_states=negative_prompt_embeds,
-                        fps=fps,
                         condition_mask=uncond_mask,
                         padding_mask=padding_mask,
                         return_dict=False,

tests/pipelines/cosmos/test_cosmos2_video2world.py

@@ -135,6 +135,20 @@ def test_inference(self):
         generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
         assert_tensors_close(generated_slice, expected_slice, atol=1e-3)
 
+    def test_fps_does_not_change_generation(self):
+        # The released Cosmos-Predict2 checkpoints are trained without FPS RoPE modulation
+        # (`rope_enable_fps_modulation=False`), so the playback `fps` must not influence generation.
+        pipe = self.get_pipeline()
+
+        videos = []
+        for fps in (16, 24, 30):
+            inputs = self.get_dummy_inputs()
+            inputs["fps"] = fps
+            videos.append(pipe(**inputs).frames[0])
+
+        for video in videos[1:]:
+            assert (videos[0] - video).abs().max().item() == 0
+
     def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=1e-2):
         super().test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)