mirror of
https://github.com/oobabooga/text-generation-webui.git
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103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
import base64
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import logging
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import re
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import time
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from functools import partial
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from io import BytesIO
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import gradio as gr
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import torch
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from extensions.multimodal.multimodal_embedder import MultimodalEmbedder
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from modules import shared
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params = {
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"add_all_images_to_prompt": False,
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# device to run vision encoder on
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"vision_device": None,
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# bits to load vision encoder in, either 16 or 32
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"vision_bits": 32,
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# device to run multimodal projector on
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"projector_device": None,
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# multimodal projector bits, either 32 or 16
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"projector_bits": 32
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}
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# If 'state' is True, will hijack the next chat generation
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input_hijack = {
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'state': False,
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'value': ["", ""]
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}
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# initialized in ui, so that params are loaded from settings
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multimodal_embedder: MultimodalEmbedder = None
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def add_chat_picture(picture, text, visible_text):
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# resize the image, so that shortest edge is at least 224 (size for CLIP), and at most 300 (to keep history manageable)
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max_hw, min_hw = max(picture.size), min(picture.size)
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aspect_ratio = max_hw / min_hw
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shortest_edge = int(max(300 / aspect_ratio, 224))
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longest_edge = int(shortest_edge * aspect_ratio)
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w = shortest_edge if picture.width < picture.height else longest_edge
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h = shortest_edge if picture.width >= picture.height else longest_edge
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picture = picture.resize((w, h))
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buffer = BytesIO()
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picture.save(buffer, format="JPEG")
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img_str = base64.b64encode(buffer.getvalue()).decode('utf-8')
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image = f'<img src="data:image/jpeg;base64,{img_str}">'
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if '<image>' in text:
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text = text.replace('<image>', image)
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else:
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text = text + '\n' + image
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if visible_text == '' or visible_text is None:
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visible_text = text
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elif '<image>' in visible_text:
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visible_text = visible_text.replace('<image>', image)
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else:
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visible_text = visible_text + '\n' + image
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return text, visible_text
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def custom_tokenized_length(prompt):
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return multimodal_embedder.len_in_tokens(prompt)
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def tokenizer_modifier(state, prompt, input_ids, input_embeds):
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global params
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start_ts = time.time()
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image_match = re.search(r'<img src="data:image/jpeg;base64,[A-Za-z0-9+/=]+">', prompt)
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if image_match is None:
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return prompt, input_ids, input_embeds
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prompt, input_ids, input_embeds, total_embedded = multimodal_embedder.forward(prompt, state, params)
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logging.info(f'Embedded {total_embedded} image(s) in {time.time()-start_ts:.2f}s')
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return (prompt,
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input_ids.unsqueeze(0).to(shared.model.device, dtype=torch.int64),
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input_embeds.unsqueeze(0).to(shared.model.device, dtype=shared.model.dtype))
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def ui():
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global multimodal_embedder
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multimodal_embedder = MultimodalEmbedder(params)
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with gr.Column():
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picture_select = gr.Image(label='Send a picture', type='pil')
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# The models don't seem to deal well with multiple images
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single_image_checkbox = gr.Checkbox(False, label='Embed all images, not only the last one')
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# Prepare the input hijack
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picture_select.upload(
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lambda picture: input_hijack.update({"state": True, "value": partial(add_chat_picture, picture)}),
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[picture_select],
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None
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)
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picture_select.clear(lambda: input_hijack.update({"state": False, "value": ["", ""]}), None, None)
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single_image_checkbox.change(lambda x: params.update({"add_all_images_to_prompt": x}), single_image_checkbox, None)
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shared.gradio['Generate'].click(lambda: None, None, picture_select)
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shared.gradio['textbox'].submit(lambda: None, None, picture_select)
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