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Update app_januspro.py
Change visual and device types (issues in MacOS and Windows 11 Version)
This commit is contained in:
William S
GitHub
parent
a74a59f8a9
commit
e944b2ea94
@ -1,245 +1,263 @@
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import gradio as gr
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import gradio as gr
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import torch
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import torch
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import inspect
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from transformers import AutoConfig, AutoModelForCausalLM
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from transformers import AutoConfig, AutoModelForCausalLM
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from janus.models import MultiModalityCausalLM, VLChatProcessor
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from janus.models import MultiModalityCausalLM, VLChatProcessor
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from janus.utils.io import load_pil_images
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from PIL import Image
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from PIL import Image
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import numpy as np
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import numpy as np
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import os
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import logging
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import time
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# import spaces # Import spaces for ZeroGPU compatibility
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Load model and processor
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# Device configuration
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device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
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logger.info(f"Using device: {device}")
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def load_processor(model_path):
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"""Load and configure the VLChatProcessor with proper parameter filtering"""
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# Get valid initialization parameters
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init_params = inspect.getfullargspec(VLChatProcessor.__init__).args
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init_params.remove('self')
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# Load model config to find processor parameters
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model_config = AutoConfig.from_pretrained(model_path)
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processor_config = getattr(model_config, 'processor_config', {})
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# Filter valid parameters
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valid_config = {k: v for k, v in processor_config.items() if k in init_params}
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return VLChatProcessor.from_pretrained(
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model_path,
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**valid_config,
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legacy=False,
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use_fast=True
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)
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def load_model():
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"""Load the model with proper configuration and device management"""
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model_path = "deepseek-ai/Janus-Pro-7B"
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model_path = "deepseek-ai/Janus-Pro-7B"
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config = AutoConfig.from_pretrained(model_path)
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language_config = config.language_config
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language_config._attn_implementation = 'eager'
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vl_gpt = AutoModelForCausalLM.from_pretrained(model_path,
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language_config=language_config,
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trust_remote_code=True)
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if torch.cuda.is_available():
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vl_gpt = vl_gpt.to(torch.bfloat16).cuda()
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else:
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vl_gpt = vl_gpt.to(torch.float16)
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vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
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# Load model config
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config = AutoConfig.from_pretrained(model_path)
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config.language_config._attn_implementation = 'eager' if device.type == 'cpu' else 'flash_attention_2'
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# Load model with mixed precision
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torch_dtype = torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16
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vl_gpt = AutoModelForCausalLM.from_pretrained(
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model_path,
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config=config,
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trust_remote_code=True,
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torch_dtype=torch_dtype,
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device_map="auto" if device.type != 'cpu' else None
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)
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# Load processor and tokenizer
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vl_chat_processor = load_processor(model_path)
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tokenizer = vl_chat_processor.tokenizer
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tokenizer = vl_chat_processor.tokenizer
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cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'
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if device.type == 'cuda':
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vl_gpt = vl_gpt.to(device)
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return vl_gpt, vl_chat_processor, tokenizer
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try:
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vl_gpt, vl_chat_processor, tokenizer = load_model()
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except Exception as e:
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logger.error(f"Failed to initialize model: {str(e)}")
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raise
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@torch.inference_mode()
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@torch.inference_mode()
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# @spaces.GPU(duration=120)
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def multimodal_understanding(image, question, seed=42, top_p=0.95, temperature=0.1, max_new_tokens=1024):
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# Multimodal Understanding function
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"""Handle multimodal understanding requests"""
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def multimodal_understanding(image, question, seed, top_p, temperature):
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try:
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# Clear CUDA cache before generating
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# Input processing
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torch.cuda.empty_cache()
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conversation = [{
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# set seed
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torch.manual_seed(seed)
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np.random.seed(seed)
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torch.cuda.manual_seed(seed)
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conversation = [
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{
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"role": "<|User|>",
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"role": "<|User|>",
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"content": f"<image_placeholder>\n{question}",
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"content": f"<image_placeholder>\n{question}",
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"images": [image],
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"images": [image]
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},
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}, {"role": "<|Assistant|>", "content": ""}]
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{"role": "<|Assistant|>", "content": ""},
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]
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pil_images = [Image.fromarray(image)]
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# Process images and text
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pil_images = [Image.fromarray(image).convert('RGB')]
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prepare_inputs = vl_chat_processor(
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prepare_inputs = vl_chat_processor(
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conversations=conversation, images=pil_images, force_batchify=True
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conversations=conversation,
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).to(cuda_device, dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16)
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images=pil_images,
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force_batchify=True
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).to(device, dtype=vl_gpt.dtype)
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inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
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# Generate response
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outputs = vl_gpt.language_model.generate(
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outputs = vl_gpt.language_model.generate(
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inputs_embeds=inputs_embeds,
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inputs_embeds=vl_gpt.prepare_inputs_embeds(**prepare_inputs),
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attention_mask=prepare_inputs.attention_mask,
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attention_mask=prepare_inputs.attention_mask,
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pad_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id,
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bos_token_id=tokenizer.bos_token_id,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=512,
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max_new_tokens=max_new_tokens,
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do_sample=False if temperature == 0 else True,
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do_sample=temperature > 0,
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use_cache=True,
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temperature=temperature if temperature > 0 else None,
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temperature=temperature,
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top_p=top_p if temperature > 0 else None,
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top_p=top_p,
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use_cache=True
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)
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)
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answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
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return tokenizer.decode(outputs[0], skip_special_tokens=True)
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return answer
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except Exception as e:
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logger.error(f"Understanding error: {str(e)}")
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return f"Error processing request: {str(e)}"
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def generate(input_ids,
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@torch.inference_mode()
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width,
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def generate_image(prompt, seed=12345, guidance=5.0, temperature=1.0, parallel_size=4):
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height,
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"""Handle image generation requests"""
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temperature: float = 1,
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try:
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parallel_size: int = 5,
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# Text processing
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cfg_weight: float = 5,
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messages = [{'role': '<|User|>', 'content': prompt}, {'role': '<|Assistant|>', 'content': ''}]
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image_token_num_per_image: int = 576,
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text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
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patch_size: int = 16):
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conversations=messages,
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# Clear CUDA cache before generating
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sft_format=vl_chat_processor.sft_format,
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torch.cuda.empty_cache()
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system_prompt=''
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) + vl_chat_processor.image_start_tag
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tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).to(cuda_device)
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# Generate image tokens
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for i in range(parallel_size * 2):
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input_ids = torch.LongTensor(tokenizer.encode(text)).to(device)
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tokens[i, :] = input_ids
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generated_tokens, patches = generate(
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if i % 2 != 0:
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input_ids=input_ids,
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tokens[i, 1:-1] = vl_chat_processor.pad_id
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width=384,
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height=384,
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cfg_weight=guidance,
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parallel_size=parallel_size,
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temperature=temperature
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)
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# Process output images
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images = unpack(patches, 384, 384, parallel_size)
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return [Image.fromarray(img).resize((768, 768), Image.Resampling.LANCZOS) for img in images]
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except Exception as e:
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logger.error(f"Generation error: {str(e)}")
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return []
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def generate(input_ids, width, height, **kwargs):
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"""Core image generation function"""
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try:
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parallel_size = kwargs.get('parallel_size', 4)
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image_token_num_per_image = 576
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# Initialize tokens
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tokens = torch.stack([input_ids] * (parallel_size * 2), dim=0)
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generated = torch.zeros((parallel_size, image_token_num_per_image),
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dtype=torch.int, device=device)
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inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
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inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
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generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).to(cuda_device)
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pkv = None
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pkv = None
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for i in range(image_token_num_per_image):
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for i in range(image_token_num_per_image):
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with torch.no_grad():
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outputs = vl_gpt.language_model.model(
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outputs = vl_gpt.language_model.model(inputs_embeds=inputs_embeds,
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inputs_embeds=inputs_embeds,
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use_cache=True,
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past_key_values=pkv,
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past_key_values=pkv)
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use_cache=True
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)
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pkv = outputs.past_key_values
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pkv = outputs.past_key_values
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hidden_states = outputs.last_hidden_state
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logits = vl_gpt.gen_head(outputs.last_hidden_state[:, -1, :])
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logits = vl_gpt.gen_head(hidden_states[:, -1, :])
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logit_cond = logits[0::2, :]
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logit_uncond = logits[1::2, :]
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logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
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probs = torch.softmax(logits / temperature, dim=-1)
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next_token = torch.multinomial(probs, num_samples=1)
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generated_tokens[:, i] = next_token.squeeze(dim=-1)
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next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
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img_embeds = vl_gpt.prepare_gen_img_embeds(next_token)
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# Classifier-free guidance
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inputs_embeds = img_embeds.unsqueeze(dim=1)
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logit_cond, logit_uncond = logits[0::2], logits[1::2]
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logits = logit_uncond + kwargs['cfg_weight'] * (logit_cond - logit_uncond)
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# Sampling
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probs = torch.softmax(logits / kwargs['temperature'], dim=-1)
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next_token = torch.multinomial(probs, 1)
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generated[:, i] = next_token.squeeze()
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# Prepare next input
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inputs_embeds = vl_gpt.prepare_gen_img_embeds(
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next_token.repeat(1, 2).view(-1)
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).unsqueeze(1)
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patches = vl_gpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int),
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# Decode patches
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shape=[parallel_size, 8, width // patch_size, height // patch_size])
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return generated, vl_gpt.gen_vision_model.decode_code(
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generated.to(torch.int),
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return generated_tokens.to(dtype=torch.int), patches
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shape=[parallel_size, 8, width//16, height//16]
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def unpack(dec, width, height, parallel_size=5):
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dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
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dec = np.clip((dec + 1) / 2 * 255, 0, 255)
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visual_img = np.zeros((parallel_size, width, height, 3), dtype=np.uint8)
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visual_img[:, :, :] = dec
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return visual_img
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@torch.inference_mode()
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# @spaces.GPU(duration=120) # Specify a duration to avoid timeout
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def generate_image(prompt,
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seed=None,
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guidance=5,
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t2i_temperature=1.0):
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# Clear CUDA cache and avoid tracking gradients
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torch.cuda.empty_cache()
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# Set the seed for reproducible results
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if seed is not None:
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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np.random.seed(seed)
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width = 384
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height = 384
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parallel_size = 5
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with torch.no_grad():
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messages = [{'role': '<|User|>', 'content': prompt},
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{'role': '<|Assistant|>', 'content': ''}]
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text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(conversations=messages,
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sft_format=vl_chat_processor.sft_format,
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system_prompt='')
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text = text + vl_chat_processor.image_start_tag
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input_ids = torch.LongTensor(tokenizer.encode(text))
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output, patches = generate(input_ids,
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width // 16 * 16,
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height // 16 * 16,
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cfg_weight=guidance,
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parallel_size=parallel_size,
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temperature=t2i_temperature)
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images = unpack(patches,
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width // 16 * 16,
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height // 16 * 16,
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parallel_size=parallel_size)
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return [Image.fromarray(images[i]).resize((768, 768), Image.LANCZOS) for i in range(parallel_size)]
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# Gradio interface
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with gr.Blocks() as demo:
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gr.Markdown(value="# Multimodal Understanding")
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with gr.Row():
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image_input = gr.Image()
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with gr.Column():
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question_input = gr.Textbox(label="Question")
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und_seed_input = gr.Number(label="Seed", precision=0, value=42)
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top_p = gr.Slider(minimum=0, maximum=1, value=0.95, step=0.05, label="top_p")
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temperature = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.05, label="temperature")
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understanding_button = gr.Button("Chat")
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understanding_output = gr.Textbox(label="Response")
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examples_inpainting = gr.Examples(
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label="Multimodal Understanding examples",
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examples=[
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[
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"explain this meme",
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"images/doge.png",
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],
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[
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"Convert the formula into latex code.",
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"images/equation.png",
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],
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],
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inputs=[question_input, image_input],
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)
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)
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except Exception as e:
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logger.error(f"Generate core error: {str(e)}")
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raise
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gr.Markdown(value="# Text-to-Image Generation")
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def unpack(dec, width, height, parallel_size):
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"""Convert model output to images"""
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try:
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dec = dec.float().cpu().numpy().transpose(0, 2, 3, 1)
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dec = np.clip((dec + 1) * 127.5, 0, 255).astype(np.uint8)
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return [dec[i] for i in range(parallel_size)]
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except Exception as e:
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logger.error(f"Unpack error: {str(e)}")
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return [np.zeros((height, width, 3), dtype=np.uint8)] * parallel_size
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# Gradio Interface
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with gr.Blocks(title="Janus Pro 7B", theme=gr.themes.Soft()) as demo:
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gr.Markdown("## 🖼️ Janus Pro 7B - Multimodal AI Assistant")
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with gr.Tab("Image Understanding"):
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with gr.Row():
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with gr.Row():
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cfg_weight_input = gr.Slider(minimum=1, maximum=10, value=5, step=0.5, label="CFG Weight")
|
with gr.Column():
|
||||||
t2i_temperature = gr.Slider(minimum=0, maximum=1, value=1.0, step=0.05, label="temperature")
|
image_input = gr.Image(label="Upload Image", type="numpy")
|
||||||
|
# examples_und = gr.Examples(
|
||||||
prompt_input = gr.Textbox(label="Prompt. (Prompt in more detail can help produce better images!)")
|
# examples=[
|
||||||
seed_input = gr.Number(label="Seed (Optional)", precision=0, value=12345)
|
# ["explain this meme", "images/doge.png"],
|
||||||
|
# ["Convert the formula into latex code", "images/equation.png"]
|
||||||
generation_button = gr.Button("Generate Images")
|
# ],
|
||||||
|
# inputs=[gr.Textbox(), image_input], # Use component references
|
||||||
image_output = gr.Gallery(label="Generated Images", columns=2, rows=2, height=300)
|
# label="Example Queries"
|
||||||
|
# )
|
||||||
|
with gr.Column():
|
||||||
|
question_input = gr.Textbox(label="Question", placeholder="Ask about the image...")
|
||||||
|
with gr.Accordion("Advanced Settings", open=False):
|
||||||
|
und_seed = gr.Number(42, label="Seed", precision=0)
|
||||||
|
top_p = gr.Slider(0, 1, 0.95, label="Top-p Sampling")
|
||||||
|
temperature = gr.Slider(0, 1, 0.1, label="Temperature")
|
||||||
|
max_tokens = gr.Slider(128, 2048, 1024, step=128, label="Max Tokens")
|
||||||
|
understanding_button = gr.Button("Analyze", variant="primary")
|
||||||
|
understanding_output = gr.Textbox(label="Response", interactive=False)
|
||||||
|
|
||||||
|
with gr.Tab("Image Generation"):
|
||||||
|
with gr.Row():
|
||||||
|
with gr.Column():
|
||||||
|
prompt_input = gr.Textbox(label="Prompt", placeholder="Describe your image...", lines=3)
|
||||||
examples_t2i = gr.Examples(
|
examples_t2i = gr.Examples(
|
||||||
label="Text to image generation examples.",
|
|
||||||
examples=[
|
examples=[
|
||||||
"Master shifu racoon wearing drip attire as a street gangster.",
|
"Master shifu raccoon wearing streetwear",
|
||||||
"The face of a beautiful girl",
|
"Astronaut in a jungle, detailed 8k rendering"
|
||||||
"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
|
|
||||||
"A glass of red wine on a reflective surface.",
|
|
||||||
"A cute and adorable baby fox with big brown eyes, autumn leaves in the background enchanting,immortal,fluffy, shiny mane,Petals,fairyism,unreal engine 5 and Octane Render,highly detailed, photorealistic, cinematic, natural colors.",
|
|
||||||
"The image features an intricately designed eye set against a circular backdrop adorned with ornate swirl patterns that evoke both realism and surrealism. At the center of attention is a strikingly vivid blue iris surrounded by delicate veins radiating outward from the pupil to create depth and intensity. The eyelashes are long and dark, casting subtle shadows on the skin around them which appears smooth yet slightly textured as if aged or weathered over time.\n\nAbove the eye, there's a stone-like structure resembling part of classical architecture, adding layers of mystery and timeless elegance to the composition. This architectural element contrasts sharply but harmoniously with the organic curves surrounding it. Below the eye lies another decorative motif reminiscent of baroque artistry, further enhancing the overall sense of eternity encapsulated within each meticulously crafted detail. \n\nOverall, the atmosphere exudes a mysterious aura intertwined seamlessly with elements suggesting timelessness, achieved through the juxtaposition of realistic textures and surreal artistic flourishes. Each component\u2014from the intricate designs framing the eye to the ancient-looking stone piece above\u2014contributes uniquely towards creating a visually captivating tableau imbued with enigmatic allure.",
|
|
||||||
],
|
],
|
||||||
inputs=prompt_input,
|
inputs=prompt_input,
|
||||||
|
label="Example Prompts"
|
||||||
)
|
)
|
||||||
|
with gr.Accordion("Advanced Settings", open=False):
|
||||||
|
cfg_weight = gr.Slider(1, 10, 5.0, label="CFG Weight")
|
||||||
|
t2i_temp = gr.Slider(0, 2, 1.0, label="Temperature")
|
||||||
|
seed_input = gr.Number(12345, label="Seed", precision=0)
|
||||||
|
parallel_size = gr.Slider(1, 8, 4, step=1, label="Batch Size")
|
||||||
|
generation_button = gr.Button("Generate", variant="primary")
|
||||||
|
with gr.Column():
|
||||||
|
image_output = gr.Gallery(label="Generated Images", columns=2, height=600)
|
||||||
|
|
||||||
|
# Event handlers
|
||||||
understanding_button.click(
|
understanding_button.click(
|
||||||
multimodal_understanding,
|
multimodal_understanding,
|
||||||
inputs=[image_input, question_input, und_seed_input, top_p, temperature],
|
inputs=[image_input, question_input, und_seed, top_p, temperature, max_tokens],
|
||||||
outputs=understanding_output
|
outputs=understanding_output
|
||||||
)
|
)
|
||||||
|
|
||||||
generation_button.click(
|
generation_button.click(
|
||||||
fn=generate_image,
|
generate_image,
|
||||||
inputs=[prompt_input, seed_input, cfg_weight_input, t2i_temperature],
|
inputs=[prompt_input, seed_input, cfg_weight, t2i_temp, parallel_size],
|
||||||
outputs=image_output
|
outputs=image_output
|
||||||
)
|
)
|
||||||
|
|
||||||
demo.launch(share=True)
|
if __name__ == "__main__":
|
||||||
# demo.queue(concurrency_count=1, max_size=10).launch(server_name="0.0.0.0", server_port=37906, root_path="/path")
|
demo.queue(concurrency_count=2).launch(
|
||||||
|
server_name="127.0.0.1",
|
||||||
|
server_port=7920,
|
||||||
|
share=False
|
||||||
|
)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user