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| import os | |
| os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "False" | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| import matplotlib.pyplot as plt | |
| from fromage import models | |
| from fromage import utils | |
| import gradio as gr | |
| import huggingface_hub | |
| from share_btn import community_icon_html, loading_icon_html, share_js | |
| import tempfile | |
| css = """ | |
| #share-btn-container { | |
| display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem; | |
| margin-top: 10px; | |
| margin-left: auto; | |
| } | |
| #share-btn { | |
| all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0; | |
| } | |
| #share-btn * { | |
| all: unset; | |
| } | |
| #share-btn-container div:nth-child(-n+2){ | |
| width: auto !important; | |
| min-height: 0px !important; | |
| } | |
| #share-btn-container .wrap { | |
| display: none !important; | |
| } | |
| """ | |
| # Download model from HF Hub. | |
| ckpt_path = huggingface_hub.hf_hub_download(repo_id='jykoh/fromage', filename='pretrained_ckpt.pth.tar') | |
| args_path = huggingface_hub.hf_hub_download(repo_id='jykoh/fromage', filename='model_args.json') | |
| model = models.load_fromage('./', args_path, ckpt_path) | |
| def upload_image(state, image_input): | |
| conversation = state[0] | |
| chat_history = state[1] | |
| conversation += [(f"", "")] | |
| input_image = Image.open(image_input.name).resize((224, 224)).convert('RGB') | |
| return [conversation, chat_history, input_image], conversation | |
| def reset(): | |
| return [[], [], None], [] | |
| def save_image_to_local(image: Image.Image): | |
| # TODO(jykoh): Update so the url path is used, to prevent repeat saving. | |
| filename = next(tempfile._get_candidate_names()) + '.png' | |
| image.save(filename) | |
| return filename | |
| def generate_for_prompt(input_text, state, ret_scale_factor, max_nm_rets, num_words, temperature): | |
| input_prompt = 'Q: ' + input_text + '\nA:' | |
| conversation = state[0] | |
| chat_history = state[1] | |
| input_image = state[2] | |
| print('Generating for', chat_history, flush=True) | |
| # If an image was uploaded, prepend it to the model. | |
| model_inputs = None | |
| if input_image is not None: | |
| model_inputs = chat_history + [input_image] | |
| else: | |
| model_inputs = chat_history | |
| model_inputs.append(input_prompt) | |
| top_p = 1.0 | |
| if temperature != 0.0: | |
| top_p = 0.95 | |
| print('Running model.generate_for_images_and_texts with', model_inputs, flush=True) | |
| model_outputs = model.generate_for_images_and_texts(model_inputs, | |
| num_words=num_words, ret_scale_factor=ret_scale_factor, top_p=top_p, | |
| temperature=temperature, max_num_rets=max_nm_rets) | |
| print('model_outputs', model_outputs, flush=True) | |
| im_names = [] | |
| response = '' | |
| text_outputs = [] | |
| for output in model_outputs: | |
| if type(output) == str: | |
| text_outputs.append(output) | |
| response += output | |
| elif type(output) == list: | |
| for image in output: | |
| filename = save_image_to_local(image) | |
| response += f'<br/><img src="/file={filename}">' | |
| elif type(output) == Image.Image: | |
| filename = save_image_to_local(output) | |
| response += f'<br/><img src="/file={filename}">' | |
| # TODO(jykoh): Persist image inputs. | |
| chat_history = model_inputs + [' '.join([s for s in model_outputs if type(s) == str]) + '\n'] | |
| conversation.append((input_text, response.replace('[RET]', ''))) # Remove [RET] from outputs. | |
| # Set input image to None. | |
| print('state', state, flush=True) | |
| print('updated state', [conversation, chat_history, None], flush=True) | |
| return [conversation, chat_history, None], conversation | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown( | |
| '### Grounding Language Models to Images for Multimodal Generation' | |
| ) | |
| gr.HTML(""" | |
| For faster inference without waiting in queue, you may duplicate the space and use your own GPU. <a href="https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation?duplicate=true"><img style="margin-top: 0em; margin-bottom: 0em" src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a> | |
| """) | |
| chatbot = gr.Chatbot(elem_id="chatbot") | |
| gr_state = gr.State([[], [], None]) # chat_history, input_image | |
| with gr.Group(elem_id="share-btn-container", visible=False): | |
| community_icon = gr.HTML(community_icon_html) | |
| loading_icon = gr.HTML(loading_icon_html) | |
| share_button = gr.Button("Share to community", elem_id="share-btn") | |
| with gr.Row(): | |
| with gr.Column(scale=0.3, min_width=100): | |
| ret_scale_factor = gr.Slider(minimum=0.0, maximum=3.0, value=1.0, step=0.1, interactive=True, label="Multiplier for returning images (higher means more frequent)") | |
| max_ret_images = gr.Number(minimum=0, maximum=3, value=1, precision=1, interactive=True, label="Max images to return") | |
| gr_max_len = gr.Number(value=32, precision=1, label="Max # of words returned", interactive=True) | |
| gr_temperature = gr.Number(value=0.0, label="Temperature", interactive=True) | |
| with gr.Column(scale=0.7, min_width=400): | |
| image_btn = gr.UploadButton("🖼️ Image Input", file_types=["image"]) | |
| text_input = gr.Textbox(label="Chat Input", lines=1, placeholder="Upload an image above [optional]. Then enter a text prompt, and press enter!") | |
| with gr.Row(): | |
| with gr.Column(scale=0.5): | |
| submit_btn = gr.Button("Submit", interactive=True, variant="primary") | |
| with gr.Column(scale=0.5): | |
| clear_btn = gr.Button("Clear History") | |
| text_input.submit(generate_for_prompt, [text_input, gr_state, ret_scale_factor, max_ret_images, gr_max_len, gr_temperature], [gr_state, chatbot]) | |
| text_input.submit(lambda: "", None, text_input) # Reset chatbox. | |
| submit_btn.click(generate_for_prompt, [text_input, gr_state, ret_scale_factor, max_ret_images, gr_max_len, gr_temperature], [gr_state, chatbot]) | |
| submit_btn.click(lambda: "", None, text_input) # Reset chatbox. | |
| image_btn.upload(upload_image, [gr_state, image_btn], [gr_state, chatbot]) | |
| clear_btn.click(reset, [], [gr_state, chatbot]) | |
| share_button.click(None, [], [], _js=share_js) | |
| demo.queue(concurrency_count=1, api_open=False, max_size=16) | |
| demo.launch(debug=True, server_name="0.0.0.0") |