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| import gradio as gr | |
| from gradio_client import Client | |
| import os | |
| hf_token = os.environ.get("HF_TKN") | |
| def get_instantID(portrait_in, condition_pose, prompt): | |
| client = Client("https://fffiloni-instantid.hf.space/", hf_token=hf_token) | |
| negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, (frame:1.2), deformed, ugly, deformed eyes, blur, out of focus, blurry, deformed cat, deformed, photo, anthropomorphic cat, monochrome, photo, pet collar, gun, weapon, blue, 3d, drones, drone, buildings in background, green" | |
| result = client.predict( | |
| portrait_in, # filepath in 'Upload a photo of your face' Image component | |
| condition_pose, # filepath in 'Upload a reference pose image (optional)' Image component | |
| prompt, # str in 'Prompt' Textbox component | |
| negative_prompt, # str in 'Negative Prompt' Textbox component | |
| "(No style)", # Literal['(No style)', 'Watercolor', 'Film Noir', 'Neon', 'Jungle', 'Mars', 'Vibrant Color', 'Snow', 'Line art'] in 'Style template' Dropdown component | |
| True, # bool in 'Enhance non-face region' Checkbox component | |
| 20, # float (numeric value between 20 and 100) in 'Number of sample steps' Slider component | |
| 0.8, # float (numeric value between 0 and 1.5) in 'IdentityNet strength (for fedility)' Slider component | |
| 0.8, # float (numeric value between 0 and 1.5) in 'Image adapter strength (for detail)' Slider component | |
| 5, # float (numeric value between 0.1 and 10.0) in 'Guidance scale' Slider component | |
| 0, # float (numeric value between 0 and 2147483647) in 'Seed' Slider component | |
| True, # bool in 'Randomize seed' Checkbox component | |
| api_name="/generate_image" | |
| ) | |
| print(result) | |
| return result[0] | |
| def get_video_i2vgen(image_in, prompt): | |
| client = Client("https://modelscope-i2vgen-xl.hf.space/") | |
| result = client.predict( | |
| image_in, | |
| prompt, | |
| fn_index=1 | |
| ) | |
| print(result) | |
| return result | |
| def get_video_svd(image_in): | |
| from gradio_client import Client | |
| client = Client("https://multimodalart-stable-video-diffusion.hf.space/--replicas/ej45m/") | |
| result = client.predict( | |
| image_in, # filepath in 'Upload your image' Image component | |
| 0, # float (numeric value between 0 and 9223372036854775807) in 'Seed' Slider component | |
| True, # bool in 'Randomize seed' Checkbox component | |
| 127, # float (numeric value between 1 and 255) in 'Motion bucket id' Slider component | |
| 6, # float (numeric value between 5 and 30) in 'Frames per second' Slider component | |
| api_name="/video" | |
| ) | |
| print(result) | |
| return result[0]["video"] | |
| def infer(image_in, camera_shot, conditional_pose, prompt, chosen_model): | |
| if camera_shot == "custom": | |
| if conditional_pose != None: | |
| conditional_pose = conditional_pose | |
| else : | |
| raise gr.Error("No custom conditional shot found !") | |
| elif camera_shot == "close-up": | |
| conditional_pose = "camera_shots/close_up_shot.jpeg" | |
| elif camera_shot == "medium close-up": | |
| conditional_pose = "camera_shots/medium_close_up.jpeg" | |
| elif camera_shot == "medium shot": | |
| conditional_pose = "camera_shots/medium_shot.png" | |
| elif camera_shot == "cowboy shot": | |
| conditional_pose = "camera_shots/cowboy_shot.jpeg" | |
| elif camera_shot == "medium full shot": | |
| conditional_pose = "camera_shots/medium_full_shot.png" | |
| elif camera_shot == "full shot": | |
| conditional_pose = "camera_shots/full_shot.jpeg" | |
| iid_img = get_instantID(image_in, conditional_pose, prompt) | |
| if chosen_model == "i2vgen-xl" : | |
| video_res = get_video_i2vgen(iid_img, prompt) | |
| elif chosen_model == "stable-video" : | |
| video_res = get_video_svd(image_in) | |
| print(video_res) | |
| return video_res | |
| with gr.Blocks as demo: | |
| with gr.Column(): | |
| gr.HTML(""" | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| face_in = gr.Image(type="filepath", label="Face to copy") | |
| camera_shot = gr.Dropdown( | |
| label = "Camera Shot", | |
| info = "Use standard camera shots vocabulary, or drop your custom shot as conditional pose (1280*720 ratio is recommended)" | |
| choices = [ | |
| "custom", "close-up", "medium close-up", "medium shot", "cowboy shot", "medium full shot", "full shot" | |
| ], | |
| value = "custom" | |
| ) | |
| condition_shot = gr.Image(type="filepath", label="Custom conditional shot (Optional)") | |
| prompt = gr.Textbox(label="Prompt") | |
| chosen_model = gr.Radio(label="Choose a model", choices=["i2vgen-xl", "stable-video"], value="i2vgen-xl", interactive=False, visible=False) | |
| submit_btn = gr.Button("Submit") | |
| with gr.Column(): | |
| video_out = gr.Video() | |
| submit_btn.click( | |
| fn = infer, | |
| inputs = [ | |
| face_in, | |
| camera_shot, | |
| condition_shot, | |
| prompt, | |
| chosen_model | |
| ], | |
| outputs = [ | |
| video_out | |
| ] | |
| ) | |
| demo.queue(max_size=6).launch(debug=True) |