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import gradio as gr |
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import torch |
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import transformers |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from transformers import pipeline |
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from diffusers import StableDiffusionPipeline |
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READ_TOKEN = os.environ.get('HF_ACCESS_TOKEN', None) |
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model_id = "runwayml/stable-diffusion-v1-5" |
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has_cuda = torch.cuda.is_available() |
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device = "cpu" |
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if has_cuda: |
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pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=READ_TOKEN) |
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device = "cuda" |
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else: |
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pipe = StableDiffusionPipeline.from_pretrained(model_id, revision="fp16", use_auth_token=READ_TOKEN) |
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device = "cpu" |
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pipe.to(device) |
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def safety_checker(images, clip_input): |
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return images, False |
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pipe.safety_checker = safety_checker |
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SAVED_CHECKPOINT = 'mikegarts/distilgpt2-lotr' |
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model = AutoModelForCausalLM.from_pretrained(SAVED_CHECKPOINT) |
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tokenizer = AutoTokenizer.from_pretrained(SAVED_CHECKPOINT) |
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summarizer = pipeline("summarization") |
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def break_until_dot(txt): |
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return txt.rsplit('.', 1)[0] + '.' |
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def generate(prompt): |
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input_context = prompt |
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input_ids = tokenizer.encode(input_context, return_tensors="pt").to(model.device) |
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outputs = model.generate( |
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input_ids=input_ids, |
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max_length=180, |
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temperature=0.7, |
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num_return_sequences=3, |
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do_sample=True |
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) |
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decoded = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return break_until_dot(decoded) |
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def generate_image(prompt, inference_steps): |
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prompt = prompt + ', masterpiece charcoal pencil art lord of the rings illustration' |
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img = pipe(prompt, height=512, width=512, num_inference_steps=inference_steps) |
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return img.images[0] |
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def generate_story(prompt): |
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story = generate(prompt=prompt) |
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summary = summarizer(story, min_length=5, max_length=15)[0]['summary_text'] |
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summary = break_until_dot(summary) |
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return story, summary, gr.update(visible=True) |
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with gr.Blocks() as demo: |
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title = gr.Markdown('## Lord of the rings app') |
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description = gr.Markdown('### A Lord of the rings insired app that combines text and image generation') |
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prompt = gr.Textbox(label="Your prompt", value="And then the hobbit said") |
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story = gr.Textbox(label="Your story") |
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summary = gr.Textbox(label="Summary") |
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bt_make_text = gr.Button("Generate text") |
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bt_make_image = gr.Button("Generate and image (takes about 10-15 minutes on CPU)", visible=False) |
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image = gr.Image(label='Illustration for your story') |
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inference_steps = gr.Slider(5, 35, value=15, step=1, label="Num inference steps (more steps makes a better image but takes more time)") |
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bt_make_text.click(fn=generate_story, inputs=prompt, outputs=[story, summary, bt_make_image]) |
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bt_make_image.click(fn=generate_image, inputs=[summary, inference_steps], outputs=image) |
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demo.launch(share=True) |
