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A10G
Running
on
A10G
Commit
•
b105b21
1
Parent(s):
7977eff
Update base/app.py
Browse files- base/app.py +1 -18
base/app.py
CHANGED
@@ -21,12 +21,6 @@ config_path = "./base/configs/sample.yaml"
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args = OmegaConf.load("./base/configs/sample.yaml")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# ------- get model ---------------
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# model_t2V = model_t2v_fun(args)
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# model_t2V.to(device)
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# if device == "cuda":
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# model_t2V.enable_xformers_memory_efficient_attention()
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-
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css = """
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h1 {
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text-align: center;
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@@ -77,7 +71,6 @@ def infer(prompt, seed_inp, ddim_steps,cfg, infer_type):
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if device == "cuda":
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model.enable_xformers_memory_efficient_attention()
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videos = model(prompt, video_length=16, height = 320, width= 512, num_inference_steps=ddim_steps, guidance_scale=cfg).video
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print(videos[0].shape)
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if not os.path.exists(args.output_folder):
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os.mkdir(args.output_folder)
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torchvision.io.write_video(args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4', videos[0], fps=8)
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@@ -86,9 +79,6 @@ def infer(prompt, seed_inp, ddim_steps,cfg, infer_type):
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return args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4'
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# def clean():
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# return gr.Video.update(value=None)
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title = """
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<div style="text-align: center; max-width: 700px; margin: 0 auto;">
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<div
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@@ -118,8 +108,6 @@ with gr.Blocks(css='style.css') as demo:
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)
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with gr.Column():
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with gr.Row(elem_id="col-container"):
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# inputs = [prompt, seed_inp, ddim_steps]
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# outputs = [video_out]
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with gr.Column():
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prompt = gr.Textbox(value="a corgi walking in the park at sunrise, oil painting style", label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in", min_width=200, lines=2)
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@@ -127,12 +115,9 @@ with gr.Blocks(css='style.css') as demo:
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ddim_steps = gr.Slider(label='Steps', minimum=50, maximum=300, value=50, step=1)
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seed_inp = gr.Slider(value=-1,label="seed (for random generation, use -1)",show_label=True,minimum=-1,maximum=2147483647)
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cfg = gr.Number(label="guidance_scale",value=7.5)
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# seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=400, elem_id="seed-in")
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-
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with gr.Column():
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submit_btn = gr.Button("Generate video")
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# clean_btn = gr.Button("Clean video")
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video_out = gr.Video(label="Video result", elem_id="video-output")
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inputs = [prompt, seed_inp, ddim_steps, cfg, infer_type]
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@@ -157,10 +142,8 @@ with gr.Blocks(css='style.css') as demo:
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cache_examples=False,
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)
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ex.dataset.headers = [""]
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# clean_btn.click(clean, inputs=[], outputs=[video_out], queue=False)
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submit_btn.click(infer, inputs, outputs)
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# share_button.click(None, [], [], _js=share_js)
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demo.queue(max_size=12).launch()
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args = OmegaConf.load("./base/configs/sample.yaml")
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device = "cuda" if torch.cuda.is_available() else "cpu"
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css = """
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h1 {
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text-align: center;
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if device == "cuda":
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model.enable_xformers_memory_efficient_attention()
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videos = model(prompt, video_length=16, height = 320, width= 512, num_inference_steps=ddim_steps, guidance_scale=cfg).video
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if not os.path.exists(args.output_folder):
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os.mkdir(args.output_folder)
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torchvision.io.write_video(args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4', videos[0], fps=8)
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return args.output_folder + prompt[0:30].replace(' ', '_') + '-'+str(seed_inp)+'-'+str(ddim_steps)+'-'+str(cfg)+ '-.mp4'
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title = """
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<div style="text-align: center; max-width: 700px; margin: 0 auto;">
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<div
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)
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with gr.Column():
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with gr.Row(elem_id="col-container"):
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with gr.Column():
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prompt = gr.Textbox(value="a corgi walking in the park at sunrise, oil painting style", label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in", min_width=200, lines=2)
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ddim_steps = gr.Slider(label='Steps', minimum=50, maximum=300, value=50, step=1)
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seed_inp = gr.Slider(value=-1,label="seed (for random generation, use -1)",show_label=True,minimum=-1,maximum=2147483647)
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cfg = gr.Number(label="guidance_scale",value=7.5)
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with gr.Column():
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submit_btn = gr.Button("Generate video")
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video_out = gr.Video(label="Video result", elem_id="video-output")
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inputs = [prompt, seed_inp, ddim_steps, cfg, infer_type]
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cache_examples=False,
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)
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ex.dataset.headers = [""]
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+
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submit_btn.click(infer, inputs, outputs)
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demo.queue(max_size=12).launch()
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