Deepfuze

nodes.py

@@ -26,7 +26,6 @@ from scipy.io.wavfile import write
 import folder_paths
 from .utils import ffmpeg_path, get_audio, hash_path, validate_path, requeue_workflow, gifski_path, calculate_file_hash, strip_path
 from comfy.utils import ProgressBar
-from .utils import BIGMAX, DIMMAX, calculate_file_hash, get_sorted_dir_files_from_directory, get_audio, lazy_eval, hash_path, validate_path, strip_path
 from .llm_node import LLM_node
 from .audio_playback import PlayBackAudio
 from .audio_playback import SaveAudio
@@ -153,102 +152,6 @@ def cv_frame_generator(video, force_rate, frame_load_cap, skip_first_frames,
     if prev_frame is not None:
         yield prev_frame
 
-def load_video_cv(video: str, force_rate: int, force_size: str,
-                  custom_width: int,custom_height: int, frame_load_cap: int,
-                  skip_first_frames: int, select_every_nth: int,
-                  meta_batch=None, unique_id=None, memory_limit_mb=None):
-    print(meta_batch)
-    if meta_batch is None or unique_id not in meta_batch.inputs:
-        gen = cv_frame_generator(video, force_rate, frame_load_cap, skip_first_frames,
-                                 select_every_nth, meta_batch, unique_id)
-        (width, height, fps, duration, total_frames, target_frame_time) = next(gen)
-
-        if meta_batch is not None:
-            meta_batch.inputs[unique_id] = (gen, width, height, fps, duration, total_frames, target_frame_time)
-
-    else:
-        (gen, width, height, fps, duration, total_frames, target_frame_time) = meta_batch.inputs[unique_id]
-
-    if memory_limit_mb is not None:
-        memory_limit *= 2 ** 20
-    else:
-        #TODO: verify if garbage collection should be performed here.
-        #leaves ~128 MB unreserved for safety
-        memory_limit = (psutil.virtual_memory().available + psutil.swap_memory().free) - 2 ** 27
-    #space required to load as f32, exist as latent with wiggle room, decode to f32
-    max_loadable_frames = int(memory_limit//(width*height*3*(4+4+1/10)))
-    if meta_batch is not None:
-        if meta_batch.frames_per_batch > max_loadable_frames:
-            raise RuntimeError(f"Meta Batch set to {meta_batch.frames_per_batch} frames but only {max_loadable_frames} can fit in memory")
-        gen = itertools.islice(gen, meta_batch.frames_per_batch)
-    else:
-        original_gen = gen
-        gen = itertools.islice(gen, max_loadable_frames)
-
-    #Some minor wizardry to eliminate a copy and reduce max memory by a factor of ~2
-    images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3)))))
-    if meta_batch is None:
-        try:
-            next(original_gen)
-            raise RuntimeError(f"Memory limit hit after loading {len(images)} frames. Stopping execution.")
-        except StopIteration:
-            pass
-    if len(images) == 0:
-        raise RuntimeError("No frames generated")
-    if force_size != "Disabled":
-        new_size = target_size(width, height, force_size, custom_width, custom_height)
-        if new_size[0] != width or new_size[1] != height:
-            s = images.movedim(-1,1)
-            s = common_upscale(s, new_size[0], new_size[1], "lanczos", "center")
-            images = s.movedim(1,-1)
-
-    #Setup lambda for lazy audio capture
-    audio = lambda : get_audio(video, skip_first_frames * target_frame_time,
-                               frame_load_cap*target_frame_time*select_every_nth)
-    #Adjust target_frame_time for select_every_nth
-    target_frame_time *= select_every_nth
-    video_info = {
-        "source_fps": fps,
-        "source_frame_count": total_frames,
-        "source_duration": duration,
-        "source_width": width,
-        "source_height": height,
-        "loaded_fps": 1/target_frame_time,
-        "loaded_frame_count": len(images),
-        "loaded_duration": len(images) * target_frame_time,
-        "loaded_width": images.shape[2],
-        "loaded_height": images.shape[1],
-    }
-    print("images", type(images))
-    return (images, len(images), lazy_eval(audio), video_info)
-
-
-
-class AudioData:
-    def __init__(self, audio_file) -> None:
-        
-        # Extract the sample rate
-        sample_rate = audio_file.frame_rate
-
-        # Get the number of audio channels
-        num_channels = audio_file.channels
-
-        # Extract the audio data as a NumPy array
-        audio_data = np.array(audio_file.get_array_of_samples())
-        self.audio_data = audio_data
-        self.sample_rate = sample_rate
-        self.num_channels = num_channels
-    
-    def get_channel_audio_data(self, channel: int):
-        if channel < 0 or channel >= self.num_channels:
-            raise IndexError(f"Channel '{channel}' out of range. total channels is '{self.num_channels}'.")
-        return self.audio_data[channel::self.num_channels]
-    
-    def get_channel_fft(self, channel: int):
-        audio_data = self.get_channel_audio_data(channel)
-        return fft(audio_data)
-
-
 def gen_format_widgets(video_format):
     for k in video_format:
         if k.endswith("_pass"):
@@ -582,7 +485,7 @@ def load_video_cv(video: str, force_rate: int, force_size: str,
             images = s.movedim(1,-1)
 
     #Setup lambda for lazy audio capture
-    audio = lambda : get_audio(video, skip_first_frames * target_frame_time,
+    audio =  get_audio(video, skip_first_frames * target_frame_time,
                                frame_load_cap*target_frame_time*select_every_nth)
     #Adjust target_frame_time for select_every_nth
     target_frame_time *= select_every_nth
@@ -599,7 +502,7 @@ def load_video_cv(video: str, force_rate: int, force_size: str,
         "loaded_height": images.shape[1],
     }
     print("images", type(images))
-    return (images, len(images), lazy_eval(audio), video_info)
+    return (images, len(images), audio, video_info)
 
 
 
@@ -1018,7 +921,7 @@ class DeepFuzeFaceSwap:
 
         print(result.stderr)
         audio_file = os.path.join(audio_dir,str(time.time()).replace(".","")+".wav")
-        torchaudio.save(audio_file,audio["waveform"],audio["sample_rate"])
+        torchaudio.save(audio_file,audio["waveform"][0],audio["sample_rate"])
         subprocess.run(f"ffmpeg -i {faceswap_filename} -i {audio_file} -c copy {faceswap_filename.replace('.mp4','_.mp4')} -y".split())
 
         return load_video_cv(faceswap_filename.replace('.mp4','_.mp4'),0,'Disabled',512,512,0,0,1)
@@ -1336,7 +1239,7 @@ class DeepFuzeAdavance:
             output_files.append(file_path)
 
         audio_file = os.path.join(audio_dir,str(time.time()).replace(".","")+".wav")
-        torchaudio.save(audio_file,audio["waveform"],audio["sample_rate"])
+        torchaudio.save(audio_file,audio["waveform"][0],audio["sample_rate"])
         print(audio_file)
         filename = os.path.join(result_dir,f"{str(time.time()).replace('.','')}.mp4")
         enhanced_filename = os.path.join(result_dir,f"enhanced_{str(time.time()).replace('.','')}.mp4")
@@ -1513,7 +1416,7 @@ class TTS_generation:
         
         language = supported_language.split("(")[1][:-1]
         file_path = os.path.join(audio_path,str(time.time()).replace(".","")+".wav")
-        torchaudio.save(audio_file,audio["waveform"],audio["sample_rate"])
+        torchaudio.save(audio_file,audio["waveform"][0],audio["sample_rate"])
         command = [
             'python', 'tts_generation.py',
             '--model', checkpoint_path_voice,
@@ -1531,7 +1434,7 @@ class TTS_generation:
         print("stdout:", result.stdout)
         print("stderr:", result.stderr)
         audio = get_audio(file_path)
-        return (audio)
+        return (audio,)