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Update app.py
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app.py
CHANGED
@@ -44,7 +44,7 @@ prompt_df = pd.read_csv("Stable-Diffusion-Prompts.csv")
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#DEFAULT_PROMPT = "1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt"
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DEFAULT_PROMPT = "X go to Istanbul"
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DEFAULT_ROLE = "Superman"
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DEFAULT_BOOK_COVER = "book_cover_dir/
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hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
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@@ -422,19 +422,20 @@ def style_transfer_func(content_img, style_img, style_transfer_client = style_tr
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os.remove(style_im_name)
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return Image.open(out)
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'''
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def style_transfer_func(content_img, style_img):
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assert hasattr(content_img, "save")
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assert hasattr(style_img, "save")
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content_image_input = np.asarray(content_img)
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style_image_input = np.asarray(style_img)
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out = perform_neural_transfer(content_image_input, style_image_input, super_resolution_type =
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assert hasattr(out, "save")
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return out
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def gen_images_from_event_fact(current_model, event_fact = DEFAULT_PROMPT, role_name = DEFAULT_ROLE,
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style_pic = None
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):
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event_reasoning_dict = produce_4_event(event_fact)
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caption_list ,event_reasoning_sd_list = transform_4_event_as_sd_prompts(event_fact ,
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event_reasoning_dict,
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@@ -451,9 +452,14 @@ def gen_images_from_event_fact(current_model, event_fact = DEFAULT_PROMPT, role_
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print("perform styling.....")
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img_list_ = []
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for x in tqdm(img_list):
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-
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-
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img_list = img_list_
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img_list = list(map(lambda t2: add_caption_on_image(t2[0], t2[1]) ,zip(*[img_list, caption_list])))
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img_mid = img_list[2]
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@@ -466,7 +472,9 @@ def gen_images_from_event_fact(current_model, event_fact = DEFAULT_PROMPT, role_
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def image_click(images, evt: gr.SelectData,
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):
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#print(img_selected)
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return img_selected
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@@ -475,10 +483,13 @@ def get_book_covers():
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list(pathlib.Path("book_cover_dir").rglob("*.jpg")) + \
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list(pathlib.Path("book_cover_dir").rglob("*.png")) + \
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list(pathlib.Path("book_cover_dir").rglob("*.jpeg"))
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).map(str).map(lambda x: np.nan if x.split("/")[-1].startswith("_") else x).dropna().
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return covers
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with gr.Blocks() as demo:
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favicon = '<img src="" width="48px" style="display: inline">'
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gr.Markdown(
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f"""<h1><center>🌻{favicon} AI Diffusion</center></h1>
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@@ -499,6 +510,9 @@ with gr.Blocks() as demo:
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value = DEFAULT_BOOK_COVER,
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interactive = True,
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)
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with gr.Row():
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text_prompt = gr.Textbox(label="Prompt", placeholder="a cute dog", lines=1, elem_id="prompt-text-input", value = DEFAULT_PROMPT)
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@@ -515,7 +529,7 @@ with gr.Blocks() as demo:
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image_click, style_reference_input_gallery, style_reference_input_image
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)
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text_button.click(gen_images_from_event_fact, inputs=[current_model, text_prompt, role_name, style_reference_input_image],
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outputs=video_output)
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#select_button.click(generate_txt2img, inputs=[current_model, select_prompt, negative_prompt, image_style], outputs=image_output)
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#DEFAULT_PROMPT = "1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewelry, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt"
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DEFAULT_PROMPT = "X go to Istanbul"
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DEFAULT_ROLE = "Superman"
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DEFAULT_BOOK_COVER = "book_cover_dir/Blank.jpg"
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hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
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os.remove(style_im_name)
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return Image.open(out)
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'''
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def style_transfer_func(content_img, style_img, super_resolution_type = "none"):
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assert hasattr(content_img, "save")
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assert hasattr(style_img, "save")
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content_image_input = np.asarray(content_img)
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style_image_input = np.asarray(style_img)
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out = perform_neural_transfer(content_image_input, style_image_input, super_resolution_type = super_resolution_type)
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assert hasattr(out, "save")
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return out
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def gen_images_from_event_fact(current_model, event_fact = DEFAULT_PROMPT, role_name = DEFAULT_ROLE,
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style_pic = None, super_resolution_type = "SD(Standard Definition)"
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):
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assert super_resolution_type in ["SD(Standard Definition)", "HD(High Definition)"]
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event_reasoning_dict = produce_4_event(event_fact)
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caption_list ,event_reasoning_sd_list = transform_4_event_as_sd_prompts(event_fact ,
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event_reasoning_dict,
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print("perform styling.....")
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img_list_ = []
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for x in tqdm(img_list):
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if super_resolution_type == "SD(Standard Definition)":
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img_list_.append(
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style_transfer_func(x, style_pic, super_resolution_type = "none")
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)
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else:
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img_list_.append(
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style_transfer_func(x, style_pic, super_resolution_type = "anime")
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)
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img_list = img_list_
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img_list = list(map(lambda t2: add_caption_on_image(t2[0], t2[1]) ,zip(*[img_list, caption_list])))
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img_mid = img_list[2]
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def image_click(images, evt: gr.SelectData,
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):
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#print(images)
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#print(evt.index)
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img_selected = images[evt.index][0]["name"]
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#print(img_selected)
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return img_selected
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list(pathlib.Path("book_cover_dir").rglob("*.jpg")) + \
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list(pathlib.Path("book_cover_dir").rglob("*.png")) + \
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list(pathlib.Path("book_cover_dir").rglob("*.jpeg"))
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).map(str).map(lambda x: np.nan if x.split("/")[-1].startswith("_") else x).dropna().map(
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lambda x: (x, "".join(x.split(".")[:-1]).split("/")[-1])
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).values.tolist()
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covers = sorted(covers, key = lambda t2: int(DEFAULT_BOOK_COVER in t2[0]), reverse = True)
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return covers
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with gr.Blocks(css=".caption-label {display:none}") as demo:
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favicon = '<img src="" width="48px" style="display: inline">'
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gr.Markdown(
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f"""<h1><center>🌻{favicon} AI Diffusion</center></h1>
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value = DEFAULT_BOOK_COVER,
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interactive = True,
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)
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super_resolution_type = gr.Radio(choices = ["SD(Standard Definition)" ,"HD(High Definition)"],
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value="SD(Standard Definition)", label="Story Video Quality",
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interactive = True)
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with gr.Row():
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text_prompt = gr.Textbox(label="Prompt", placeholder="a cute dog", lines=1, elem_id="prompt-text-input", value = DEFAULT_PROMPT)
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image_click, style_reference_input_gallery, style_reference_input_image
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)
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text_button.click(gen_images_from_event_fact, inputs=[current_model, text_prompt, role_name, style_reference_input_image, super_resolution_type],
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outputs=video_output)
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#select_button.click(generate_txt2img, inputs=[current_model, select_prompt, negative_prompt, image_style], outputs=image_output)
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