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| import io, os, base64 | |
| from PIL import Image | |
| import gradio as gr | |
| import shortuuid | |
| from transformers import pipeline | |
| text_generation_model = "pranavpsv/gpt2-genre-story-generator" | |
| text_generation = pipeline("text-generation", text_generation_model) | |
| latent = gr.Interface.load("spaces/multimodalart/latentdiffusion") | |
| def get_story(user_input, genre="sci_fi"): | |
| prompt = f"<BOS> <{genre}> " | |
| stories = text_generation(f"{prompt}{user_input}", max_length=32, num_return_sequences=1) | |
| story = stories[0]["generated_text"] | |
| story_without_prompt = story[len(prompt):] | |
| return story_without_prompt | |
| def text2image_latent(text, steps, width, height, images, diversity): | |
| print(text) | |
| results = latent(text, steps, width, height, images, diversity) | |
| image_paths = [] | |
| for image in results[1]: | |
| image_str = image[0] | |
| image_str = image_str.replace("data:image/png;base64,","") | |
| decoded_bytes = base64.decodebytes(bytes(image_str, "utf-8")) | |
| img = Image.open(io.BytesIO(decoded_bytes)) | |
| url = shortuuid.uuid() | |
| temp_dir = './tmp' | |
| if not os.path.exists(temp_dir): | |
| os.makedirs(temp_dir, exist_ok=True) | |
| image_path = f'{temp_dir}/{url}.png' | |
| img.save(f'{temp_dir}/{url}.png') | |
| image_paths.append(image_path) | |
| return(image_paths) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| user_input = gr.inputs.Textbox(placeholder="Type your prompt to generate an image", label="Prompt - try adding increments to your prompt such as 'a painting of', 'in the style of Picasso'", default="A giant mecha robot in Rio de Janeiro, oil on canvas") | |
| genre_input = gr.Dropdown(["superhero","action","drama","horror","thriller","sci_fi",]) | |
| generated_story = gr.Textbox() | |
| with gr.Row(): | |
| button_generate_story = gr.Button("Generate Story") | |
| with gr.Column(): | |
| steps = gr.inputs.Slider(label="Steps - more steps can increase quality but will take longer to generate",default=50,maximum=50,minimum=1,step=1) | |
| width = gr.inputs.Slider(label="Width", default=256, step=32, maximum=256, minimum=32) | |
| height = gr.inputs.Slider(label="Height", default=256, step=32, maximum = 256, minimum=32) | |
| images = gr.inputs.Slider(label="Images - How many images you wish to generate", default=4, step=1, minimum=1, maximum=4) | |
| diversity = gr.inputs.Slider(label="Diversity scale - How different from one another you wish the images to be",default=15.0, minimum=1.0, maximum=15.0) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Individual images") | |
| with gr.Row(): | |
| get_image_latent = gr.Button("Generate Image", css={"margin-top": "1em"}) | |
| with gr.Row(): | |
| gr.Markdown("<a href='https://huggingface.co/spaces/merve/GPT-2-story-gen' target='_blank'>Story generation with GPT-2</a>, and text to image by <a href='https://huggingface.co/spaces/multimodalart/latentdiffusion' target='_blank'>Latent Diffusion</a>.") | |
| with gr.Row(): | |
| gr.Markdown("") | |
| button_generate_story.click(get_story, inputs=[user_input, genre_input], outputs=generated_story) | |
| get_image_latent.click(text2image_latent, inputs=[generated_story,steps,width,height,images,diversity], outputs=gallery) | |
| demo.launch(enable_queue=False) |