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layerdiffusion 2025-04-18 18:20:43 -07:00
parent 4292ab9812
commit 025c884323
2 changed files with 9 additions and 7 deletions
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12
demo_gradio.py
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@ -100,7 +100,7 @@ os.makedirs(outputs_folder, exist_ok=True)
@torch.no_grad()
def worker(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache):
def worker(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf):
total_latent_sections = (total_second_length * 30) / (latent_window_size * 4)
total_latent_sections = int(max(round(total_latent_sections), 1))
@ -295,7 +295,7 @@ def worker(input_image, prompt, n_prompt, seed, total_second_length, latent_wind
output_filename = os.path.join(outputs_folder, f'{job_id}_{total_generated_latent_frames}.mp4')
save_bcthw_as_mp4(history_pixels, output_filename, fps=30)
save_bcthw_as_mp4(history_pixels, output_filename, fps=30, crf=mp4_crf)
print(f'Decoded. Current latent shape {real_history_latents.shape}; pixel shape {history_pixels.shape}')
@ -315,7 +315,7 @@ def worker(input_image, prompt, n_prompt, seed, total_second_length, latent_wind
return
def process(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache):
def process(input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf):
global stream
assert input_image is not None, 'No input image!'
@ -323,7 +323,7 @@ def process(input_image, prompt, n_prompt, seed, total_second_length, latent_win
stream = AsyncStream()
async_run(worker, input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache)
async_run(worker, input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf)
output_filename = None
@ -385,13 +385,15 @@ with block:
gpu_memory_preservation = gr.Slider(label="GPU Inference Preserved Memory (GB) (larger means slower)", minimum=6, maximum=128, value=6, step=0.1, info="Set this number to a larger value if you encounter OOM. Larger value causes slower speed.")
mp4_crf = gr.Slider(label="MP4 Compression", minimum=0, maximum=100, value=16, step=1, info="Lower means better quality. 0 is uncompressed. Change to 16 if you get black outputs. ")
with gr.Column():
preview_image = gr.Image(label="Next Latents", height=200, visible=False)
result_video = gr.Video(label="Finished Frames", autoplay=True, show_share_button=False, height=512, loop=True)
gr.Markdown('Note that the ending actions will be generated before the starting actions due to the inverted sampling. If the starting action is not in the video, you just need to wait, and it will be generated later.')
progress_desc = gr.Markdown('', elem_classes='no-generating-animation')
progress_bar = gr.HTML('', elem_classes='no-generating-animation')
ips = [input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache]
ips = [input_image, prompt, n_prompt, seed, total_second_length, latent_window_size, steps, cfg, gs, rs, gpu_memory_preservation, use_teacache, mp4_crf]
start_button.click(fn=process, inputs=ips, outputs=[result_video, preview_image, progress_desc, progress_bar, start_button, end_button])
end_button.click(fn=end_process)

4
diffusers_helper/utils.py
View File

@ -263,7 +263,7 @@ def soft_append_bcthw(history, current, overlap=0):
return output.to(history)
def save_bcthw_as_mp4(x, output_filename, fps=10):
def save_bcthw_as_mp4(x, output_filename, fps=10, crf=0):
b, c, t, h, w = x.shape
per_row = b
@ -276,7 +276,7 @@ def save_bcthw_as_mp4(x, output_filename, fps=10):
x = torch.clamp(x.float(), -1., 1.) * 127.5 + 127.5
x = x.detach().cpu().to(torch.uint8)
x = einops.rearrange(x, '(m n) c t h w -> t (m h) (n w) c', n=per_row)
torchvision.io.write_video(output_filename, x, fps=fps, video_codec='libx264', options={'crf': '0'})
torchvision.io.write_video(output_filename, x, fps=fps, video_codec='libx264', options={'crf': str(int(crf))})
return x