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| import spaces | |
| import sys | |
| import os | |
| import torch | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image, ImageOps, ImageDraw, ImageFont, ImageColor | |
| from urllib.request import urlopen | |
| #os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1" | |
| root = os.path.dirname(os.path.abspath(__file__)) | |
| static = os.path.join(root, "static") | |
| from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler | |
| from diffusers.pipelines import TextToVideoSDPipeline | |
| from diffusers.utils import export_to_video | |
| from TrailBlazer.Misc import ConfigIO | |
| from TrailBlazer.Misc import Logger as log | |
| from TrailBlazer.Pipeline.TextToVideoSDPipelineCall import ( | |
| text_to_video_sd_pipeline_call, | |
| ) | |
| from TrailBlazer.Pipeline.UNet3DConditionModelCall import ( | |
| unet3d_condition_model_forward, | |
| ) | |
| TextToVideoSDPipeline.__call__ = text_to_video_sd_pipeline_call | |
| from diffusers.models.unet_3d_condition import UNet3DConditionModel | |
| unet3d_condition_model_forward_copy = UNet3DConditionModel.forward | |
| UNet3DConditionModel.forward = unet3d_condition_model_forward | |
| from diffusers.utils import export_to_video | |
| model_id = "cerspense/zeroscope_v2_576w" | |
| model_path = model_id | |
| pipe = DiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| #pipe.enable_model_cpu_offload() | |
| pipe.to('cuda') | |
| def core(bundle): | |
| generator = torch.Generator().manual_seed(int(bundle["seed"])) | |
| result = pipe( | |
| bundle=bundle, | |
| height=512, | |
| width=512, | |
| generator=generator, | |
| num_inference_steps=40, | |
| ) | |
| return result.frames | |
| def clear_btn_fn(): | |
| return "", "", "", "" | |
| def gen_btn_fn( | |
| prompts, | |
| bboxes, | |
| frames, | |
| word_prompt_indices, | |
| trailing_length, | |
| n_spatial_steps, | |
| n_temporal_steps, | |
| spatial_strengthen_scale, | |
| spatial_weaken_scale, | |
| temporal_strengthen_scale, | |
| temporal_weaken_scale, | |
| rand_seed, | |
| ): | |
| bundle = {} | |
| bundle["trailing_length"] = trailing_length | |
| bundle["num_dd_spatial_steps"] = n_spatial_steps | |
| bundle["num_dd_temporal_steps"] = n_temporal_steps | |
| bundle["num_frames"] = 24 | |
| bundle["seed"] = rand_seed | |
| bundle["spatial_strengthen_scale"] = spatial_strengthen_scale | |
| bundle["spatial_weaken_scale"] = spatial_weaken_scale | |
| bundle["temp_strengthen_scale"] = temporal_strengthen_scale | |
| bundle["temp_weaken_scale"] = temporal_weaken_scale | |
| bundle["token_inds"] = [int(v) for v in word_prompt_indices.split(",")] | |
| bundle["keyframe"] = [] | |
| frames = frames.split(";") | |
| bboxes = bboxes.split(";") | |
| if ";" in prompts: | |
| prompts = prompts.split(";") | |
| else: | |
| prompts = [prompts for i in range(len(frames))] | |
| assert ( | |
| len(frames) == len(bboxes) == len(prompts) | |
| ), "Inconsistent number of keyframes in the given inputs." | |
| frames.pop() | |
| bboxes.pop() | |
| prompts.pop() | |
| for i in range(len(frames)): | |
| keyframe = {} | |
| keyframe["bbox_ratios"] = [float(v) for v in bboxes[i].split(",")] | |
| keyframe["frame"] = int(frames[i]) | |
| keyframe["prompt"] = prompts[i] | |
| bundle["keyframe"].append(keyframe) | |
| print(bundle) | |
| result = core(bundle) | |
| path = export_to_video(result) | |
| return path | |
| def save_mask(inputs): | |
| layers = inputs["layers"] | |
| if not layers: | |
| return inputs["background"] | |
| mask = layers[0] | |
| new_image = Image.new("RGBA", mask.size, color="white") | |
| new_image.paste(mask, mask=mask) | |
| new_image = new_image.convert("RGB") | |
| print("SAve") | |
| return ImageOps.invert(new_image) | |
| def out_label_cb(im): | |
| layers = im["layers"] | |
| if not isinstance(layers, list): | |
| layers = [layers] | |
| img = None | |
| text = "Bboxes: " | |
| for idx, layer in enumerate(layers): | |
| mask = np.array(layer).sum(axis=-1) | |
| ys, xs = np.where(mask != 0) | |
| h, w = mask.shape | |
| if not list(xs) or not list(ys): | |
| continue | |
| x_min = np.min(xs) | |
| x_max = np.max(xs) | |
| y_min = np.min(ys) | |
| y_max = np.max(ys) | |
| text += "{:.2f},{:.2f},{:.2f},{:.2f}".format( | |
| x_min * 1.0 / w, y_min * 1.0 / h, x_max * 1.0 / w, y_max * 1.0 / h | |
| ) | |
| text += ";\n" | |
| return text | |
| def out_board_cb(im): | |
| layers = im["layers"] | |
| if not isinstance(layers, list): | |
| layers = [layers] | |
| img = None | |
| for idx, layer in enumerate(layers): | |
| mask = np.array(layer).sum(axis=-1) | |
| ys, xs = np.where(mask != 0) | |
| if not list(xs) or not list(ys): | |
| continue | |
| h, w = mask.shape | |
| if not img: | |
| img = Image.new("RGBA", (w, h)) | |
| x_min = np.min(xs) | |
| x_max = np.max(xs) | |
| y_min = np.min(ys) | |
| y_max = np.max(ys) | |
| # output | |
| shape = [(x_min, y_min), (x_max, y_max)] | |
| colors = list(ImageColor.colormap.keys()) | |
| draw = ImageDraw.Draw(img) | |
| draw.rectangle(shape, outline=colors[idx], width=5) | |
| text = "Bbox#{}".format(idx) | |
| font = ImageFont.load_default() | |
| draw.text((x_max - 0.5 * (x_max - x_min), y_max), text, font=font, align="left") | |
| return img | |
| with gr.Blocks( | |
| analytics_enabled=False, | |
| title="TrailBlazer Demo", | |
| ) as main: | |
| description = """ | |
| <h1 align="center" style="font-size: 48px">TrailBlazer: Trajectory Control for Diffusion-Based Video Generation</h1> | |
| <h4 align="center" style="margin: 0;">If you like our project, please give us a star ✨ at our Huggingface space, and our Github repository.</h4> | |
| <br> | |
| <span align="center" style="font-size: 18px"> | |
| [<a href="https://hohonu-vicml.github.io/Trailblazer.Page/" target="_blank">Project Page</a>] | |
| [<a href="http://arxiv.org/abs/2401.00896" target="_blank">Paper</a>] | |
| [<a href="https://github.com/hohonu-vicml/Trailblazer" target="_blank">GitHub</a>] | |
| [<a href="https://www.youtube.com/watch?v=kEN-32wN-xQ" target="_blank">Project Video</a>] | |
| [<a href="https://www.youtube.com/watch?v=P-PSkS7sNco" target="_blank">Result Video</a>] | |
| </span> | |
| </p> | |
| <p> | |
| <strong>Usage:</strong> Our Gradio app is implemented based on our executable script CmdTrailBlazer in our github repository. Please see our general information below for a quick guidance, as well as the hints within the app widgets. | |
| <ul> | |
| <li>Basic: The bounding box (bbox) is the tuple of four floats for the rectangular corners: left, top, right, bottom in the normalized ratio. The Word prompt indices is a list of 1-indexed numbers determining the prompt word.</li> | |
| <li>Advanced Options: We also offer some key parameters to adjust the synthesis result. Please see our paper for more information about the ablations.</li> | |
| </ul> | |
| </p> | |
| """ | |
| gr.HTML(description) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| with gr.Row(): | |
| with gr.Tab("Main"): | |
| text_prompt_tb = gr.Textbox( | |
| interactive=True, label="Keyframe: Prompt" | |
| ) | |
| bboxes_tb = gr.Textbox(interactive=True, label="Keyframe: Bboxes") | |
| frame_tb = gr.Textbox( | |
| interactive=True, label="Keyframe: frame indices" | |
| ) | |
| with gr.Row(): | |
| word_prompt_indices_tb = gr.Textbox( | |
| interactive=True, label="Word prompt indices:" | |
| ) | |
| text = "Hint: Each keyframe ends with <strong>SEMICOLON</strong>, and <strong>COMMA</strong> for separating each value in the keyframe. The prompt field can be a single prompt without semicolon, or multiple prompts ended semicolon. One can use the SketchPadHelper tab to help to design the bboxes field." | |
| gr.HTML(text) | |
| with gr.Row(): | |
| clear_btn = gr.Button(value="Clear") | |
| gen_btn = gr.Button(value="Generate") | |
| with gr.Accordion("Advanced Options", open=False): | |
| text = "Hint: This default value should be sufficient for most tasks. However, it's important to note that our approach is currently implemented on ZeroScope, and its performance may be influenced by the model's characteristics. We plan to conduct experiments on different models in the future." | |
| gr.HTML(text) | |
| with gr.Row(): | |
| trailing_length = gr.Slider( | |
| minimum=0, | |
| maximum=30, | |
| step=1, | |
| value=13, | |
| interactive=True, | |
| label="#Trailing", | |
| ) | |
| n_spatial_steps = gr.Slider( | |
| minimum=0, | |
| maximum=30, | |
| step=1, | |
| value=5, | |
| interactive=True, | |
| label="#Spatial edits", | |
| ) | |
| n_temporal_steps = gr.Slider( | |
| minimum=0, | |
| maximum=30, | |
| step=1, | |
| value=5, | |
| interactive=True, | |
| label="#Temporal edits", | |
| ) | |
| with gr.Row(): | |
| spatial_strengthen_scale = gr.Slider( | |
| minimum=0, | |
| maximum=2, | |
| step=0.01, | |
| value=0.15, | |
| interactive=True, | |
| label="Spatial Strengthen Scale", | |
| ) | |
| spatial_weaken_scale = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| step=0.01, | |
| value=0.001, | |
| interactive=True, | |
| label="Spatial Weaken Scale", | |
| ) | |
| temporal_strengthen_scale = gr.Slider( | |
| minimum=0, | |
| maximum=2, | |
| step=0.01, | |
| value=0.15, | |
| interactive=True, | |
| label="Temporal Strengthen Scale", | |
| ) | |
| temporal_weaken_scale = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| step=0.01, | |
| value=0.001, | |
| interactive=True, | |
| label="Temporal Weaken Scale", | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| minimum=0, | |
| maximum=50, | |
| step=0.5, | |
| value=7.5, | |
| interactive=True, | |
| label="Guidance Scale", | |
| ) | |
| rand_seed = gr.Slider( | |
| minimum=0, | |
| maximum=523451232531, | |
| step=1, | |
| value=0, | |
| interactive=True, | |
| label="Seed", | |
| ) | |
| with gr.Tab("SketchPadHelper"): | |
| with gr.Row(): | |
| user_board = gr.ImageMask(type="pil", label="Draw me") | |
| out_board = gr.Image(type="pil", label="Processed bbox") | |
| user_board.change( | |
| out_board_cb, inputs=[user_board], outputs=[out_board] | |
| ) | |
| with gr.Row(): | |
| text = "Hint: Utilize a black pen with the Draw Button to create a ``rough'' bbox. When you press the green ``Save Changes'' Button, the app calculates the minimum and maximum boundaries. Each ``Layer'', located at the bottom left of the pad, corresponds to one bounding box. Copy the returned value to the bbox textfield in the main tab." | |
| gr.HTML(text) | |
| with gr.Row(): | |
| out_label = gr.Label(label="Converted bboxes string") | |
| user_board.change( | |
| out_label_cb, inputs=[user_board], outputs=[out_label] | |
| ) | |
| with gr.Column(scale=1): | |
| gr.HTML( | |
| '<span style="font-size: 20px; font-weight: bold">Generated Images</span>' | |
| ) | |
| with gr.Row(): | |
| out_gen_1 = gr.Video(visible=True, show_label=False) | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| "A clown fish swimming in a coral reef", | |
| "0.5,0.35,1.0,0.65; 0.0,0.35,0.5,0.65;", | |
| "0; 24;", | |
| "1,2,3", | |
| "123451232531", | |
| "assets/gradio/fish-RL.mp4", | |
| ], | |
| [ | |
| "A cat is running on the grass", | |
| "0.0,0.35,0.4,0.65; 0.6,0.35,1.0,0.65; 0.0,0.35,0.4,0.65;" | |
| "0.6,0.35,1.0,0.65; 0.0,0.35,0.4,0.65;", | |
| "0; 6; 12; 18; 24;", | |
| "1,2", | |
| "123451232530", | |
| "assets/gradio/cat-LRLR.mp4", | |
| ], | |
| [ | |
| "A fish swimming in the ocean", | |
| "0.0,0.0,0.1,0.1; 0.5,0.5,1.0,1.0;", | |
| "0; 24;", | |
| "1, 2", | |
| "0", | |
| "assets/gradio/fish-TL2BR.mp4" | |
| ], | |
| [ | |
| "A tiger walking alone down the street", | |
| "0.0,0.0,0.1,0.1; 0.5,0.5,1.0,1.0;", | |
| "0; 24;", | |
| "1, 2", | |
| "0", | |
| "assets/gradio/tiger-TL2BR.mp4" | |
| ], | |
| [ | |
| "A white cat walking on the grass; A yellow dog walking on the grass;", | |
| "0.7,0.4,1.0,0.65; 0.0,0.4,0.3,0.65;", | |
| "0; 24;", | |
| "1,2,3", | |
| "123451232531", | |
| "assets/gradio/Cat2Dog.mp4", | |
| ], | |
| ], | |
| inputs=[text_prompt_tb, bboxes_tb, frame_tb, word_prompt_indices_tb, rand_seed,out_gen_1], | |
| outputs=None, | |
| fn=None, | |
| cache_examples=False, | |
| ) | |
| clear_btn.click( | |
| clear_btn_fn, | |
| inputs=[], | |
| outputs=[text_prompt_tb, bboxes_tb, frame_tb, word_prompt_indices_tb], | |
| queue=False, | |
| ) | |
| gen_btn.click( | |
| gen_btn_fn, | |
| inputs=[ | |
| text_prompt_tb, | |
| bboxes_tb, | |
| frame_tb, | |
| word_prompt_indices_tb, | |
| trailing_length, | |
| n_spatial_steps, | |
| n_temporal_steps, | |
| spatial_strengthen_scale, | |
| spatial_weaken_scale, | |
| temporal_strengthen_scale, | |
| temporal_weaken_scale, | |
| rand_seed, | |
| ], | |
| outputs=[out_gen_1], | |
| queue=False, | |
| ) | |
| main.queue(max_size=40, api_open=False) | |
| main.launch(max_threads=400) | |