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tin2tin 2 years ago committed by GitHub
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1 changed files with 67 additions and 35 deletions
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  1. 102
      __init__.py

102
__init__.py
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@ -213,33 +213,52 @@ class SEQUENCER_OT_generate_movie(Operator):
duration = scene.generate_movie_frames
movie_num_inference_steps = scene.movie_num_inference_steps
pipe = DiffusionPipeline.from_pretrained(
"damo-vilab/text-to-video-ms-1.7b",
torch_dtype=torch.float16,
variant="fp16",
)
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
# Options: https://huggingface.co/docs/diffusers/api/pipelines/text_to_video
video_frames = pipe(
prompt, num_inference_steps=25, height=y, width=x, num_frames=duration
).frames
src_path = export_to_video(video_frames)
dst_path = dirname(realpath(__file__)) + "/" + os.path.basename(src_path)
shutil.move(src_path, dst_path)
if os.path.isfile(dst_path):
empty_channel = find_first_empty_channel(0, 10000000000)
strip = scene.sequence_editor.sequences.new_movie(
name=context.scene.generate_movie_prompt,
frame_start=scene.frame_current,
filepath=dst_path,
channel=empty_channel,
fit_method="FIT",
wm = bpy.context.window_manager
tot = scene.movie_num_batch
wm.progress_begin(0, tot)
for i in range(scene.movie_num_batch):
wm.progress_update(i)
if i > 0:
empty_channel = scene.sequence_editor.active_strip.channel
start_frame = scene.sequence_editor.active_strip.frame_final_start + scene.sequence_editor.active_strip.frame_final_duration
else:
empty_channel = find_first_empty_channel(0, 10000000000)
start_frame = scene.frame_current
# Options: https://huggingface.co/docs/diffusers/api/pipelines/text_to_video
pipe = DiffusionPipeline.from_pretrained(
"damo-vilab/text-to-video-ms-1.7b",
torch_dtype=torch.float16,
variant="fp16",
)
else:
print("No resulting file found.")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
# memory optimization
pipe.enable_vae_slicing()
video_frames = pipe(
prompt, num_inference_steps=movie_num_inference_steps, height=y, width=x, num_frames=duration,
).frames
src_path = export_to_video(video_frames)
dst_path = dirname(realpath(__file__)) + "/" + os.path.basename(src_path)
shutil.move(src_path, dst_path)
if os.path.isfile(dst_path):
strip = scene.sequence_editor.sequences.new_movie(
name=context.scene.generate_movie_prompt,
frame_start=start_frame,
filepath=dst_path,
channel=empty_channel,
fit_method="FIT",
)
scene.sequence_editor.active_strip = strip
else:
print("No resulting file found.")
wm.progress_end()
return {"FINISHED"}
@ -271,9 +290,9 @@ class SEQUENCER_OT_generate_audio(Operator):
prompt = context.scene.generate_audio_prompt
# Options: https://huggingface.co/docs/diffusers/main/en/api/pipelines/audioldm
audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]
print(audio.tostring())
filename = dirname(realpath(__file__)) + "/" + clean_path(prompt + ".wav")
scipy.io.wavfile.write(filename, rate=48000, data=audio.transpose())
scipy.io.wavfile.write(filename, 48000, audio.transpose())
filepath = filename
if os.path.isfile(filepath):
@ -284,6 +303,7 @@ class SEQUENCER_OT_generate_audio(Operator):
channel=empty_channel,
frame_start=scene.frame_current,
)
scene.sequence_editor.active_strip = strip
else:
print("No resulting file found!")
return {"FINISHED"}
@ -304,15 +324,18 @@ class SEQEUNCER_PT_generate_movie(Panel):
row = layout.row()
row.scale_y = 1.2
row.prop(context.scene, "generate_movie_prompt", text="")
row = layout.row(align=True)
col = layout.column(align=True)
row = col.row()
row.prop(context.scene, "generate_movie_x", text="X")
row.prop(context.scene, "generate_movie_y", text="Y")
row = layout.row(align=True)
row.prop(context.scene, "generate_movie_frames", text="Frames")
row = col.row()
row.prop(context.scene, "generate_movie_y", text="Y")
row.prop(context.scene, "movie_num_inference_steps", text="Inference")
row = layout.row()
row = layout.row(align=True)
row.scale_y = 1.2
row.operator("sequencer.generate_movie", text="Generate Video")
row.operator("sequencer.generate_movie", text="Generate")
row.prop(context.scene, "movie_num_batch", text="")
class SEQEUNCER_PT_generate_audio(Panel):
@ -337,9 +360,9 @@ class SEQEUNCER_PT_generate_audio(Panel):
classes = (
SEQUENCER_OT_generate_movie,
SEQUENCER_OT_generate_audio,
#SEQUENCER_OT_generate_audio,
SEQEUNCER_PT_generate_movie,
SEQEUNCER_PT_generate_audio,
#SEQEUNCER_PT_generate_audio,
)
@ -361,16 +384,24 @@ def register():
step=64,
min=192,
)
# The number of frames to be generated.
bpy.types.Scene.generate_movie_frames = bpy.props.IntProperty(
name="generate_movie_y",
default=16,
min=1,
)
# The number of denoising steps. More denoising steps usually lead to a higher quality audio at the expense of slower inference.
bpy.types.Scene.movie_num_inference_steps = bpy.props.IntProperty(
name="movie_num_inference_steps",
default=25,
min=1,
)
# The number of videos to generate.
bpy.types.Scene.movie_num_batch = bpy.props.IntProperty(
name="movie_num_batch",
default=1,
min=1,
)
def unregister():
@ -381,6 +412,7 @@ def unregister():
del bpy.types.Scene.generate_movie_x
del bpy.types.Scene.generate_movie_y
del bpy.types.Scene.movie_num_inference_steps
del bpy.types.Scene.movie_num_batch
if __name__ == "__main__":

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