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| #!/usr/bin/env python | |
| import os | |
| import random | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| import torchvision.transforms.functional as TF | |
| from diffusers import ( | |
| AutoencoderKL, | |
| EulerAncestralDiscreteScheduler, | |
| StableDiffusionXLAdapterPipeline, | |
| T2IAdapter, | |
| ) | |
| from modelscope.pipelines import pipeline | |
| from modelscope.outputs import OutputKeys | |
| DESCRIPTION = '''# doodle2vid | |
| Combining T2I-Adapter-SDXL with MS-Image2Video to create a doodle to video pipeline. | |
| Shout-out to [fffiloni](https://huggingface.co/fffiloni) & [ARC Lab, Tencent PCG](https://huggingface.co/TencentARC) 🗣️ | |
| How to use: Draw a doodle in the canvas, and click "Run" to generate a video. | |
| You can also provide a prompt with more details and choose a style. | |
| ''' | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" | |
| style_list = [ | |
| { | |
| "name": "(No style)", | |
| "prompt": "{prompt}", | |
| "negative_prompt": "", | |
| }, | |
| { | |
| "name": "Cinematic", | |
| "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", | |
| "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured", | |
| }, | |
| { | |
| "name": "3D Model", | |
| "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", | |
| "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting", | |
| }, | |
| { | |
| "name": "Anime", | |
| "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", | |
| "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast", | |
| }, | |
| { | |
| "name": "Digital Art", | |
| "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", | |
| "negative_prompt": "photo, photorealistic, realism, ugly", | |
| }, | |
| { | |
| "name": "Photographic", | |
| "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", | |
| "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly", | |
| }, | |
| { | |
| "name": "Pixel art", | |
| "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", | |
| "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic", | |
| }, | |
| { | |
| "name": "Fantasy art", | |
| "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", | |
| "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white", | |
| }, | |
| { | |
| "name": "Neonpunk", | |
| "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", | |
| "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured", | |
| }, | |
| { | |
| "name": "Manga", | |
| "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", | |
| "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style", | |
| }, | |
| ] | |
| styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
| STYLE_NAMES = list(styles.keys()) | |
| DEFAULT_STYLE_NAME = "(No style)" | |
| def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]: | |
| p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) | |
| return p.replace("{prompt}", positive), n + negative | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| if torch.cuda.is_available(): | |
| model_id = "stabilityai/stable-diffusion-xl-base-1.0" | |
| adapter = T2IAdapter.from_pretrained( | |
| "TencentARC/t2i-adapter-sketch-sdxl-1.0", torch_dtype=torch.float16, variant="fp16" | |
| ) | |
| scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") | |
| pipe = StableDiffusionXLAdapterPipeline.from_pretrained( | |
| model_id, | |
| vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16), | |
| adapter=adapter, | |
| scheduler=scheduler, | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| ) | |
| pipe.to(device) | |
| else: | |
| pipe = None | |
| MAX_SEED = np.iinfo(np.int32).max | |
| video_pipe = pipeline(task='image-to-video', model='damo/Image-to-Video', model_revision='v1.1.0') | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def inferVideo(image: PIL.Image.Image) -> str: | |
| # Save the passed image to a temp file | |
| temp_path = "temp_input_image.png" | |
| image.save(temp_path) | |
| output_video_path = video_pipe(temp_path, output_video='output.mp4')[OutputKeys.OUTPUT_VIDEO] | |
| print(output_video_path) | |
| return output_video_path | |
| def inferImage( | |
| image: PIL.Image.Image, | |
| prompt: str, | |
| negative_prompt: str, | |
| style_name: str = DEFAULT_STYLE_NAME, | |
| num_steps: int = 25, | |
| guidance_scale: float = 5, | |
| adapter_conditioning_scale: float = 0.8, | |
| adapter_conditioning_factor: float = 0.8, | |
| seed: int = 0, | |
| progress=gr.Progress(track_tqdm=True), | |
| ) -> PIL.Image.Image: | |
| image = image.convert("RGB") | |
| image = TF.to_tensor(image) > 0.5 | |
| image = TF.to_pil_image(image.to(torch.float32)) | |
| prompt, negative_prompt = apply_style(style_name, prompt, negative_prompt) | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| out = pipe( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| image=image, | |
| num_inference_steps=num_steps, | |
| generator=generator, | |
| guidance_scale=guidance_scale, | |
| adapter_conditioning_scale=adapter_conditioning_scale, | |
| adapter_conditioning_factor=adapter_conditioning_factor, | |
| ).images[0] | |
| return out | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(DESCRIPTION, elem_id="description") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| image = gr.Image( | |
| source="canvas", | |
| tool="sketch", | |
| type="pil", | |
| image_mode="L", | |
| invert_colors=True, | |
| shape=(1024, 1024), | |
| brush_radius=4, | |
| height=440, | |
| ) | |
| prompt = gr.Textbox(label="Prompt") | |
| style = gr.Dropdown(label="Style", choices=STYLE_NAMES, value=DEFAULT_STYLE_NAME) | |
| run_button = gr.Button("Run") | |
| with gr.Accordion("Advanced options", open=False): | |
| negative_prompt = gr.Textbox( | |
| label="Negative prompt", | |
| value=" extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured", | |
| ) | |
| num_steps = gr.Slider( | |
| label="Number of steps", | |
| minimum=1, | |
| maximum=50, | |
| step=1, | |
| value=25, | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance scale", | |
| minimum=0.1, | |
| maximum=10.0, | |
| step=0.1, | |
| value=5, | |
| ) | |
| adapter_conditioning_scale = gr.Slider( | |
| label="Adapter conditioning scale", | |
| minimum=0.5, | |
| maximum=1, | |
| step=0.1, | |
| value=0.8, | |
| ) | |
| adapter_conditioning_factor = gr.Slider( | |
| label="Adapter conditioning factor", | |
| info="Fraction of timesteps for which adapter should be applied", | |
| minimum=0.5, | |
| maximum=1, | |
| step=0.1, | |
| value=0.8, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Column(): | |
| result_image = gr.Image(label="Intermediate Image Output", height=400) | |
| result_video = gr.Video(label="Final Video Output", height=400) | |
| inputs = [ | |
| image, | |
| prompt, | |
| negative_prompt, | |
| style, | |
| num_steps, | |
| guidance_scale, | |
| adapter_conditioning_scale, | |
| adapter_conditioning_factor, | |
| seed, | |
| ] | |
| prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=inferImage, | |
| inputs=inputs, | |
| outputs=result_image, | |
| api_name=False, | |
| ).then( | |
| fn=inferVideo, | |
| inputs=[result_image], | |
| outputs=result_video, | |
| api_name=False, | |
| ) | |
| negative_prompt.submit( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=inferImage, | |
| inputs=inputs, | |
| outputs=result_image, | |
| api_name=False, | |
| ).then( | |
| fn=inferVideo, | |
| inputs=[result_image], | |
| outputs=result_video, | |
| api_name=False, | |
| ) | |
| run_button.click( | |
| fn=randomize_seed_fn, | |
| inputs=[seed, randomize_seed], | |
| outputs=seed, | |
| queue=False, | |
| api_name=False, | |
| ).then( | |
| fn=inferImage, | |
| inputs=inputs, | |
| outputs=result_image, | |
| api_name=False, | |
| ).then( | |
| fn=inferVideo, | |
| inputs=[result_image], | |
| outputs=result_video, | |
| api_name=False, | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() | |