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import spaces |
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import gc |
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import gradio as gr |
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import numpy as np |
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import os |
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from pathlib import Path |
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from diffusers import GGUFQuantizationConfig, HunyuanVideoPipeline, HunyuanVideoTransformer3DModel |
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from diffusers.utils import export_to_video |
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from huggingface_hub import snapshot_download |
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import torch |
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gc.collect() |
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torch.cuda.empty_cache() |
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torch.set_grad_enabled(False) |
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torch.backends.cudnn.deterministic = True |
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torch.backends.cudnn.benchmark = False |
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model_id = "hunyuanvideo-community/HunyuanVideo" |
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base_path = f"/home/user/app/{model_id}" |
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os.makedirs(base_path, exist_ok=True) |
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snapshot_download(repo_id=model_id, local_dir=base_path) |
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ckp_path = Path(base_path) |
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gguf_filename = "hunyuan-video-t2v-720p-Q4_0.gguf" |
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transformer_path = f"https://huggingface.co/city96/HunyuanVideo-gguf/blob/main/{gguf_filename}" |
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transformer = HunyuanVideoTransformer3DModel.from_single_file( |
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transformer_path, |
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quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16), |
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torch_dtype=torch.bfloat16, |
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) |
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transformer = transformer.to('cuda') |
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pipe = HunyuanVideoPipeline.from_pretrained( |
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ckp_path, |
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transformer=transformer, |
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torch_dtype=torch.float16 |
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) |
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if pipe.text_encoder: |
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pipe.text_encoder = pipe.text_encoder.to('cuda') |
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pipe.text_encoder.eval() |
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pipe.vae.enable_tiling() |
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pipe.vae.enable_slicing() |
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pipe.vae.eval() |
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pipe.vae = pipe.vae.to("cuda") |
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pipe = pipe.to("cuda") |
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pipe.load_lora_weights( |
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"sergidev/IllustrationTTV", |
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weight_name="hunyuan_flat_color_v2.safetensors", |
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adapter_name="hyvid_lora_adapter" |
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) |
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pipe.set_adapters("hyvid_lora_adapter", 1.2) |
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gc.collect() |
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torch.cuda.empty_cache() |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1024 |
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@spaces.GPU(duration=120) |
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def generate( |
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prompt, |
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height, |
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width, |
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num_frames, |
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num_inference_steps, |
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seed_value, |
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fps, |
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progress=gr.Progress(track_tqdm=True) |
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): |
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with torch.cuda.device(0): |
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if seed_value == -1: |
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seed_value = torch.randint(0, MAX_SEED, (1,)).item() |
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generator = torch.Generator('cuda').manual_seed(seed_value) |
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with torch.amp.autocast_mode.autocast('cuda', dtype=torch.bfloat16), torch.inference_mode(), torch.no_grad(): |
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output = pipe( |
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prompt=prompt, |
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height=height, |
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width=width, |
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num_frames=num_frames, |
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num_inference_steps=num_inference_steps, |
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generator=generator, |
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).frames[0] |
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output_path = "output.mp4" |
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export_to_video(output, output_path, fps=fps) |
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torch.cuda.empty_cache() |
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gc.collect() |
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return output_path |
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def apply_preset(preset_name, *current_values): |
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if preset_name == "Higher Resolution": |
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return [608, 448, 24, 29, 12] |
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elif preset_name == "More Frames": |
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return [512, 320, 42, 27, 14] |
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return current_values |
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css = """ |
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#col-container { |
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margin: 0 auto; |
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max-width: 850px; |
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} |
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.dark-theme { |
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background-color: #1f1f1f; |
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color: #ffffff; |
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} |
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.container { |
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margin: 0 auto; |
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padding: 20px; |
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border-radius: 10px; |
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background-color: #2d2d2d; |
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box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); |
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} |
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.title { |
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text-align: center; |
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margin-bottom: 1em; |
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color: #ffffff; |
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} |
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.description { |
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text-align: center; |
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margin-bottom: 2em; |
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color: #cccccc; |
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font-size: 0.95em; |
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line-height: 1.5; |
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} |
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.prompt-container { |
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background-color: #363636; |
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padding: 15px; |
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border-radius: 8px; |
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margin-bottom: 1em; |
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width: 100%; |
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} |
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.prompt-textbox { |
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min-height: 80px !important; |
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} |
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.preset-buttons { |
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display: flex; |
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gap: 10px; |
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justify-content: center; |
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margin-bottom: 1em; |
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} |
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.support-text { |
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text-align: center; |
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margin-top: 1em; |
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color: #cccccc; |
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font-size: 0.9em; |
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} |
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a { |
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color: #00a7e1; |
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text-decoration: none; |
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} |
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a:hover { |
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text-decoration: underline; |
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} |
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""" |
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with gr.Blocks(css=css, theme="dark") as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown("# 🎬 Illustration TTV", elem_classes=["title"]) |
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gr.Markdown( |
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"""Transform your text descriptions into illustrative videos using HunyuanVideo for free! |
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This space uses the 'hunyuan flat color v2' LORA by Motimalu to generate better 2d animated sequences. Prompt only handles 77 tokens. |
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If you find this useful, please consider giving the space a ❤️ and supporting me on [Ko-Fi](https://ko-fi.com/sergidev)!""", |
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elem_classes=["description"] |
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) |
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with gr.Column(elem_classes=["prompt-container"]): |
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prompt = gr.Textbox( |
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label="Prompt", |
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placeholder="Enter your prompt here (Include the terms 'flat color, no lineart, blending' for 2d illustration)", |
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show_label=False, |
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elem_classes=["prompt-textbox"], |
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lines=3 |
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) |
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with gr.Row(): |
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run_button = gr.Button("🎨 Generate", variant="primary", size="lg") |
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with gr.Row(elem_classes=["preset-buttons"]): |
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preset_high_res = gr.Button("📺 Higher Resolution Preset") |
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preset_more_frames = gr.Button("🎞️ More Frames Preset") |
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with gr.Row(): |
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result = gr.Video(label="Generated Video") |
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with gr.Accordion("⚙️ Advanced Settings", open=False): |
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seed = gr.Slider( |
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label="Seed (-1 for random)", |
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minimum=-1, |
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maximum=MAX_SEED, |
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step=1, |
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value=-1, |
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) |
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with gr.Row(): |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=16, |
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value=608, |
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) |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=16, |
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value=448, |
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) |
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with gr.Row(): |
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num_frames = gr.Slider( |
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label="Number of frames to generate", |
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minimum=1.0, |
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maximum=257.0, |
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step=1, |
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value=24, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=29, |
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) |
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fps = gr.Slider( |
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label="Frames per second", |
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minimum=1, |
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maximum=60, |
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step=1, |
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value=12, |
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) |
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run_button.click( |
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fn=generate, |
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inputs=[prompt, height, width, num_frames, num_inference_steps, seed, fps], |
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outputs=[result], |
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) |
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preset_high_res.click( |
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fn=lambda: apply_preset("Higher Resolution"), |
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outputs=[height, width, num_frames, num_inference_steps, fps] |
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) |
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preset_more_frames.click( |
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fn=lambda: apply_preset("More Frames"), |
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outputs=[height, width, num_frames, num_inference_steps, fps] |
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) |
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