Update text_to_image.py

text_to_image.py

@@ -8,14 +8,14 @@ Original file is located at
 """
 
 # Commented out IPython magic to ensure Python compatibility.
-# %pip install -q "openvino>=2023.1.0"
-# %pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "diffusers[torch]>=0.9.0"
-# %pip install -q "huggingface-hub>=0.9.1"
-# %pip install -q gradio
-# %pip install -q transformers
-# %pip install kaleido cohere openai tiktoken
-# %pip install typing-extensions==3.10.0.2
-# %pip install diffusers transformers
+%pip install -q "openvino>=2023.1.0"
+%pip install -q --extra-index-url https://download.pytorch.org/whl/cpu "diffusers[torch]>=0.9.0"
+%pip install -q "huggingface-hub>=0.9.1"
+%pip install -q gradio
+%pip install -q transformers
+%pip install kaleido cohere openai tiktoken
+%pip install typing-extensions==3.10.0.2
+%pip install diffusers transformers
 
 from diffusers import StableDiffusionPipeline
 import gc
@@ -599,44 +599,6 @@ ov_pipe = OVStableDiffusionPipeline(
     scheduler=lms
 )
 
-"""### Text-to-Image generation
-[back to top ⬆️](#Table-of-contents:)
-
-Now, you can define a text prompt for image generation and run inference pipeline.
-Optionally, you can also change the random generator seed for latent state initialization and number of steps.
-
-> **Note**: Consider increasing `steps` to get more precise results. A suggested value is `50`, but it will take longer time to process.
-"""
-
-import ipywidgets as widgets
-sample_text = ('cyberpunk cityscape like Tokyo New York  with tall buildings at dusk golden hour cinematic lighting, epic composition. '
-               'A golden daylight, hyper-realistic environment. '
-               'Hyper and intricate detail, photo-realistic. '
-               'Cinematic and volumetric light. '
-               'Epic concept art. '
-               'Octane render and Unreal Engine, trending on artstation')
-text_prompt = widgets.Text(value=sample_text, description='your text')
-num_steps = widgets.IntSlider(min=1, max=50, value=20, description='steps:')
-seed = widgets.IntSlider(min=0, max=10000000, description='seed: ', value=42)
-widgets.VBox([text_prompt, seed, num_steps])
-
-print('Pipeline settings')
-print(f'Input text: {text_prompt.value}')
-print(f'Seed: {seed.value}')
-print(f'Number of steps: {num_steps.value}')
-
-result = ov_pipe(text_prompt.value, num_inference_steps=num_steps.value, seed=seed.value)
-
-"""Finally, let us save generation results.
-The pipeline returns several results: `sample` contains final generated image, `iterations` contains list of intermediate results for each step.
-"""
-
-final_image = result['sample'][0]
-if result['iterations']:
-    all_frames = result['iterations']
-    img = next(iter(all_frames))
-    img.save(fp='result.gif', format='GIF', append_images=iter(all_frames), save_all=True, duration=len(all_frames) * 5, loop=0)
-final_image.save('result.png')
 
 """Now is show time!"""
 
@@ -645,37 +607,4 @@ import ipywidgets as widgets
 text = '\n\t'.join(text_prompt.value.split('.'))
 print("Input text:")
 print("\t" + text)
-display(final_image)
-
-"""Nice. As you can see, the picture has quite a high definition 🔥."""
-
-import gradio as gr
-
-def generate_from_text(text, seed, num_steps, _=gr.Progress(track_tqdm=True)):
-    result = ov_pipe(text, num_inference_steps=num_steps, seed=seed)
-    return result["sample"][0]
-
-with gr.Blocks() as demo:
-    with gr.Tab("Text-to-Image generation"):
-        with gr.Row():
-            with gr.Column():
-                text_input = gr.Textbox(lines=3, label="Text")
-                seed_input = gr.Slider(0, 10000000, value=42, label="Seed")
-                steps_input = gr.Slider(1, 50, value=20, step=1, label="Steps")
-            out = gr.Image(label="Result", type="pil")
-        btn = gr.Button()
-        btn.click(generate_from_text, [text_input, seed_input, steps_input], out)
-
-    # Remove the "Image-to-Image generation" tab and its content
-
-try:
-    demo.launch(debug=True)
-except Exception:
-    demo.launch(share=True, debug=True)
-
-!ls  # List files in the current directory
-
-!echo "Hello, World!"  # Print a message
-
-!gradio deploy
-
+display(final_image)