Refactor video and audio latent generation in generate_video and generate_video_with_audio

- Removed direct initialization of latents with random noise, replacing it with a conditional approach based on I2V (Image-to-Video) conditioning.
- Introduced a structured flow for applying noise during the latent state creation, enhancing the conditioning process for both video and audio.
- Updated the noise application logic to ensure proper handling of conditioned and unconditioned frames in both stages of video generation.
- Improved code clarity and maintainability by consolidating latent shape definitions and restructuring noise application logic.

mlx_video/generate_av.py

@@ -498,9 +498,6 @@ def generate_video_with_audio(
     # Initialize latents
     print(f"{Colors.YELLOW}⚡ Stage 1: Generating at {width//2}x{height//2} (8 steps)...{Colors.RESET}")
     mx.random.seed(seed)
-    video_latents = mx.random.normal((1, 128, latent_frames, stage1_h, stage1_w))
-    audio_latents = mx.random.normal((1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS))
-    mx.eval(video_latents, audio_latents)
 
     # Create position grids
     video_positions = create_video_position_grid(1, latent_frames, stage1_h, stage1_w)
@@ -509,10 +506,12 @@ def generate_video_with_audio(
 
     # Apply I2V conditioning for stage 1 if provided
     video_state1 = None
+    video_latent_shape = (1, 128, latent_frames, stage1_h, stage1_w)
     if is_i2v and stage1_image_latent is not None:
+        # PyTorch flow: create zeros -> apply conditioning -> apply noiser
         video_state1 = LatentState(
-            latent=video_latents,
-            clean_latent=mx.zeros_like(video_latents),
+            latent=mx.zeros(video_latent_shape),
+            clean_latent=mx.zeros(video_latent_shape),
             denoise_mask=mx.ones((1, 1, latent_frames, 1, 1)),
         )
         conditioning = VideoConditionByLatentIndex(
@@ -521,8 +520,26 @@ def generate_video_with_audio(
             strength=image_strength,
         )
         video_state1 = apply_conditioning(video_state1, [conditioning])
+
+        # Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale)
+        noise = mx.random.normal(video_latent_shape)
+        noise_scale = STAGE_1_SIGMAS[0]  # 1.0
+        scaled_mask = video_state1.denoise_mask * noise_scale
+        video_state1 = LatentState(
+            latent=noise * scaled_mask + video_state1.latent * (1.0 - scaled_mask),
+            clean_latent=video_state1.clean_latent,
+            denoise_mask=video_state1.denoise_mask,
+        )
         video_latents = video_state1.latent
         mx.eval(video_latents)
+    else:
+        # T2V: just use random noise
+        video_latents = mx.random.normal(video_latent_shape)
+        mx.eval(video_latents)
+
+    # Audio always uses pure noise (no I2V for audio)
+    audio_latents = mx.random.normal((1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS))
+    mx.eval(audio_latents)
 
     # Stage 1 denoising
     video_latents, audio_latents = denoise_av(
@@ -554,19 +571,12 @@ def generate_video_with_audio(
     video_positions = create_video_position_grid(1, latent_frames, stage2_h, stage2_w)
     mx.eval(video_positions)
 
-    # Add noise for refinement
-    noise_scale = STAGE_2_SIGMAS[0]
-    video_noise = mx.random.normal(video_latents.shape)
-    audio_noise = mx.random.normal(audio_latents.shape)
-    video_latents = video_noise * noise_scale + video_latents * (1 - noise_scale)
-    audio_latents = audio_noise * noise_scale + audio_latents * (1 - noise_scale)
-    mx.eval(video_latents, audio_latents)
-
     # Apply I2V conditioning for stage 2 if provided
     video_state2 = None
     if is_i2v and stage2_image_latent is not None:
+        # PyTorch flow: start with upscaled latent -> apply conditioning -> apply noiser
         video_state2 = LatentState(
-            latent=video_latents,
+            latent=video_latents,  # Start with upscaled latent
             clean_latent=mx.zeros_like(video_latents),
             denoise_mask=mx.ones((1, 1, latent_frames, 1, 1)),
         )
@@ -576,9 +586,32 @@ def generate_video_with_audio(
             strength=image_strength,
         )
         video_state2 = apply_conditioning(video_state2, [conditioning])
+
+        # Apply noiser: conditioned frames (mask=0) keep image latent, unconditioned get partial noise
+        video_noise = mx.random.normal(video_latents.shape)
+        noise_scale = STAGE_2_SIGMAS[0]
+        scaled_mask = video_state2.denoise_mask * noise_scale
+        video_state2 = LatentState(
+            latent=video_noise * scaled_mask + video_state2.latent * (1.0 - scaled_mask),
+            clean_latent=video_state2.clean_latent,
+            denoise_mask=video_state2.denoise_mask,
+        )
         video_latents = video_state2.latent
         mx.eval(video_latents)
 
+        # Audio still gets noise (no I2V for audio)
+        audio_noise = mx.random.normal(audio_latents.shape)
+        audio_latents = audio_noise * noise_scale + audio_latents * (1 - noise_scale)
+        mx.eval(audio_latents)
+    else:
+        # T2V: add noise to all frames for refinement
+        noise_scale = STAGE_2_SIGMAS[0]
+        video_noise = mx.random.normal(video_latents.shape)
+        audio_noise = mx.random.normal(audio_latents.shape)
+        video_latents = video_noise * noise_scale + video_latents * (1 - noise_scale)
+        audio_latents = audio_noise * noise_scale + audio_latents * (1 - noise_scale)
+        mx.eval(video_latents, audio_latents)
+
     video_latents, audio_latents = denoise_av(
         video_latents, audio_latents,
         video_positions, audio_positions,