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Style changes
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@ -46,12 +46,12 @@ class MultimodalEmbedder:
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break
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# found an image, append image start token to the text
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if match.start() > 0:
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parts.append(PromptPart(text=prompt[curr:curr+match.start()]+self.pipeline.image_start()))
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parts.append(PromptPart(text=prompt[curr:curr + match.start()] + self.pipeline.image_start()))
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else:
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parts.append(PromptPart(text=self.pipeline.image_start()))
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# append the image
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parts.append(PromptPart(
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text=match.group(0),
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text=match.group(0),
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image=Image.open(BytesIO(base64.b64decode(match.group(1)))) if load_images else None,
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is_image=True
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))
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@ -94,14 +94,14 @@ class MultimodalEmbedder:
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def _encode_text(self, state, parts: List[PromptPart]) -> List[PromptPart]:
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"""Encode text to token_ids, also truncate the prompt, if necessary.
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The chat/instruct mode should make prompts that fit in get_max_prompt_length, but if max_new_tokens are set
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such that the context + min_rows don't fit, we can get a prompt which is too long.
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We can't truncate image embeddings, as it leads to broken generation, so remove the images instead and warn the user
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"""
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encoded: List[PromptPart] = []
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for i, part in enumerate(parts):
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encoded.append(self._encode_single_text(part, i==0 and state['add_bos_token']))
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encoded.append(self._encode_single_text(part, i == 0 and state['add_bos_token']))
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# truncation:
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max_len = get_max_prompt_length(state)
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@ -26,7 +26,7 @@ def load_pipeline(params: dict) -> Tuple[AbstractMultimodalPipeline, str]:
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if shared.args.multimodal_pipeline is not None:
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for k in pipeline_modules:
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if hasattr(pipeline_modules[k], 'get_pipeline'):
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if hasattr(pipeline_modules[k], 'get_pipeline'):
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pipeline = getattr(pipeline_modules[k], 'get_pipeline')(shared.args.multimodal_pipeline, params)
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if pipeline is not None:
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return (pipeline, k)
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@ -42,14 +42,13 @@ def add_chat_picture(picture, text, visible_text):
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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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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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@ -80,8 +79,8 @@ def tokenizer_modifier(state, 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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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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@ -97,7 +96,7 @@ def ui():
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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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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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