Update app_januspro.py

Change visual and device types (issues in MacOS and Windows 11 Version)

demo/app_januspro.py

@@ -1,245 +1,263 @@
 import gradio as gr
 import torch
+import inspect
 from transformers import AutoConfig, AutoModelForCausalLM
 from janus.models import MultiModalityCausalLM, VLChatProcessor
-from janus.utils.io import load_pil_images
 from PIL import Image
-
 import numpy as np
-import os
+import logging
-import time
+
-# import spaces  # Import spaces for ZeroGPU compatibility
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Device configuration
+device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
+logger.info(f"Using device: {device}")
+
+def load_processor(model_path):
+    """Load and configure the VLChatProcessor with proper parameter filtering"""
+    # Get valid initialization parameters
+    init_params = inspect.getfullargspec(VLChatProcessor.__init__).args
+    init_params.remove('self')
+    
+    # Load model config to find processor parameters
+    model_config = AutoConfig.from_pretrained(model_path)
+    processor_config = getattr(model_config, 'processor_config', {})
+    
+    # Filter valid parameters
+    valid_config = {k: v for k, v in processor_config.items() if k in init_params}
+    
+    return VLChatProcessor.from_pretrained(
+        model_path,
+        **valid_config,
+        legacy=False,
+        use_fast=True
+    )
+
+def load_model():
+    """Load the model with proper configuration and device management"""
+    model_path = "deepseek-ai/Janus-Pro-7B"
+    
+    # Load model config
+    config = AutoConfig.from_pretrained(model_path)
+    config.language_config._attn_implementation = 'eager' if device.type == 'cpu' else 'flash_attention_2'
+    
+    # Load model with mixed precision
+    torch_dtype = torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16
+    vl_gpt = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        config=config,
+        trust_remote_code=True,
+        torch_dtype=torch_dtype,
+        device_map="auto" if device.type != 'cpu' else None
+    )
     
     
-# Load model and processor
+    # Load processor and tokenizer
-model_path = "deepseek-ai/Janus-Pro-7B"
+    vl_chat_processor = load_processor(model_path)
-config = AutoConfig.from_pretrained(model_path)
+    tokenizer = vl_chat_processor.tokenizer
-language_config = config.language_config
-language_config._attn_implementation = 'eager'
-vl_gpt = AutoModelForCausalLM.from_pretrained(model_path,
-                                             language_config=language_config,
-                                             trust_remote_code=True)
-if torch.cuda.is_available():
-    vl_gpt = vl_gpt.to(torch.bfloat16).cuda()
-else:
-    vl_gpt = vl_gpt.to(torch.float16)
     
-vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
+    if device.type == 'cuda':
-tokenizer = vl_chat_processor.tokenizer
+        vl_gpt = vl_gpt.to(device)
-cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    
+    return vl_gpt, vl_chat_processor, tokenizer
+
+try:
+    vl_gpt, vl_chat_processor, tokenizer = load_model()
+except Exception as e:
+    logger.error(f"Failed to initialize model: {str(e)}")
+    raise
 
 @torch.inference_mode()
-# @spaces.GPU(duration=120) 
+def multimodal_understanding(image, question, seed=42, top_p=0.95, temperature=0.1, max_new_tokens=1024):
-# Multimodal Understanding function
+    """Handle multimodal understanding requests"""
-def multimodal_understanding(image, question, seed, top_p, temperature):
+    try:
-    # Clear CUDA cache before generating
+        # Input processing
-    torch.cuda.empty_cache()
+        conversation = [{
-    
-    # set seed
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    torch.cuda.manual_seed(seed)
-    
-    conversation = [
-        {
             "role": "<|User|>",
             "content": f"<image_placeholder>\n{question}",
-            "images": [image],
+            "images": [image]
-        },
+        }, {"role": "<|Assistant|>", "content": ""}]
-        {"role": "<|Assistant|>", "content": ""},
-    ]
         
-    pil_images = [Image.fromarray(image)]
+        # Process images and text
-    prepare_inputs = vl_chat_processor(
+        pil_images = [Image.fromarray(image).convert('RGB')]
-        conversations=conversation, images=pil_images, force_batchify=True
+        prepare_inputs = vl_chat_processor(
-    ).to(cuda_device, dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16)
+            conversations=conversation,
+            images=pil_images,
+            force_batchify=True
+        ).to(device, dtype=vl_gpt.dtype)
         
+        # Generate response
+        outputs = vl_gpt.language_model.generate(
+            inputs_embeds=vl_gpt.prepare_inputs_embeds(**prepare_inputs),
+            attention_mask=prepare_inputs.attention_mask,
+            pad_token_id=tokenizer.eos_token_id,
+            bos_token_id=tokenizer.bos_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+            max_new_tokens=max_new_tokens,
+            do_sample=temperature > 0,
+            temperature=temperature if temperature > 0 else None,
+            top_p=top_p if temperature > 0 else None,
+            use_cache=True
+        )
         
-    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
-    
-    outputs = vl_gpt.language_model.generate(
-        inputs_embeds=inputs_embeds,
-        attention_mask=prepare_inputs.attention_mask,
-        pad_token_id=tokenizer.eos_token_id,
-        bos_token_id=tokenizer.bos_token_id,
-        eos_token_id=tokenizer.eos_token_id,
-        max_new_tokens=512,
-        do_sample=False if temperature == 0 else True,
-        use_cache=True,
-        temperature=temperature,
-        top_p=top_p,
-    )
-    
-    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
-    return answer
-
-
-def generate(input_ids,
-             width,
-             height,
-             temperature: float = 1,
-             parallel_size: int = 5,
-             cfg_weight: float = 5,
-             image_token_num_per_image: int = 576,
-             patch_size: int = 16):
-    # Clear CUDA cache before generating
-    torch.cuda.empty_cache()
-    
-    tokens = torch.zeros((parallel_size * 2, len(input_ids)), dtype=torch.int).to(cuda_device)
-    for i in range(parallel_size * 2):
-        tokens[i, :] = input_ids
-        if i % 2 != 0:
-            tokens[i, 1:-1] = vl_chat_processor.pad_id
-    inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
-    generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).to(cuda_device)
-
-    pkv = None
-    for i in range(image_token_num_per_image):
-        with torch.no_grad():
-            outputs = vl_gpt.language_model.model(inputs_embeds=inputs_embeds,
-                                                use_cache=True,
-                                                past_key_values=pkv)
-            pkv = outputs.past_key_values
-            hidden_states = outputs.last_hidden_state
-            logits = vl_gpt.gen_head(hidden_states[:, -1, :])
-            logit_cond = logits[0::2, :]
-            logit_uncond = logits[1::2, :]
-            logits = logit_uncond + cfg_weight * (logit_cond - logit_uncond)
-            probs = torch.softmax(logits / temperature, dim=-1)
-            next_token = torch.multinomial(probs, num_samples=1)
-            generated_tokens[:, i] = next_token.squeeze(dim=-1)
-            next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
-
-            img_embeds = vl_gpt.prepare_gen_img_embeds(next_token)
-            inputs_embeds = img_embeds.unsqueeze(dim=1)
-
-    
-
-    patches = vl_gpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int),
-                                                 shape=[parallel_size, 8, width // patch_size, height // patch_size])
-
-    return generated_tokens.to(dtype=torch.int), patches
-
-def unpack(dec, width, height, parallel_size=5):
-    dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
-    dec = np.clip((dec + 1) / 2 * 255, 0, 255)
-
-    visual_img = np.zeros((parallel_size, width, height, 3), dtype=np.uint8)
-    visual_img[:, :, :] = dec
-
-    return visual_img
-
     
+    except Exception as e:
+        logger.error(f"Understanding error: {str(e)}")
+        return f"Error processing request: {str(e)}"
 
 @torch.inference_mode()
-# @spaces.GPU(duration=120)  # Specify a duration to avoid timeout
+def generate_image(prompt, seed=12345, guidance=5.0, temperature=1.0, parallel_size=4):
-def generate_image(prompt,
+    """Handle image generation requests"""
-                   seed=None,
+    try:
-                   guidance=5,
+        # Text processing
-                   t2i_temperature=1.0):
+        messages = [{'role': '<|User|>', 'content': prompt}, {'role': '<|Assistant|>', 'content': ''}]
-    # Clear CUDA cache and avoid tracking gradients
+        text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(
-    torch.cuda.empty_cache()
+            conversations=messages,
-    # Set the seed for reproducible results
+            sft_format=vl_chat_processor.sft_format,
-    if seed is not None:
+            system_prompt=''
-        torch.manual_seed(seed)
+        ) + vl_chat_processor.image_start_tag
-        torch.cuda.manual_seed(seed)
-        np.random.seed(seed)
-    width = 384
-    height = 384
-    parallel_size = 5
         
-    with torch.no_grad():
+        # Generate image tokens
-        messages = [{'role': '<|User|>', 'content': prompt},
+        input_ids = torch.LongTensor(tokenizer.encode(text)).to(device)
-                    {'role': '<|Assistant|>', 'content': ''}]
+        generated_tokens, patches = generate(
-        text = vl_chat_processor.apply_sft_template_for_multi_turn_prompts(conversations=messages,
+            input_ids=input_ids,
-                                                                   sft_format=vl_chat_processor.sft_format,
+            width=384,
-                                                                   system_prompt='')
+            height=384,
-        text = text + vl_chat_processor.image_start_tag
+            cfg_weight=guidance,
+            parallel_size=parallel_size,
+            temperature=temperature
+        )
         
-        input_ids = torch.LongTensor(tokenizer.encode(text))
+        # Process output images
-        output, patches = generate(input_ids,
+        images = unpack(patches, 384, 384, parallel_size)
-                                   width // 16 * 16,
+        return [Image.fromarray(img).resize((768, 768), Image.Resampling.LANCZOS) for img in images]
-                                   height // 16 * 16,
-                                   cfg_weight=guidance,
-                                   parallel_size=parallel_size,
-                                   temperature=t2i_temperature)
-        images = unpack(patches,
-                        width // 16 * 16,
-                        height // 16 * 16,
-                        parallel_size=parallel_size)
     
-        return [Image.fromarray(images[i]).resize((768, 768), Image.LANCZOS) for i in range(parallel_size)]
+    except Exception as e:
+        logger.error(f"Generation error: {str(e)}")
+        return []
 
+def generate(input_ids, width, height, **kwargs):
+    """Core image generation function"""
+    try:
+        parallel_size = kwargs.get('parallel_size', 4)
+        image_token_num_per_image = 576
         
-# Gradio interface
+        # Initialize tokens
-with gr.Blocks() as demo:
+        tokens = torch.stack([input_ids] * (parallel_size * 2), dim=0)
-    gr.Markdown(value="# Multimodal Understanding")
+        generated = torch.zeros((parallel_size, image_token_num_per_image), 
-    with gr.Row():
+                              dtype=torch.int, device=device)
-        image_input = gr.Image()
+        inputs_embeds = vl_gpt.language_model.get_input_embeddings()(tokens)
-        with gr.Column():
-            question_input = gr.Textbox(label="Question")
-            und_seed_input = gr.Number(label="Seed", precision=0, value=42)
-            top_p = gr.Slider(minimum=0, maximum=1, value=0.95, step=0.05, label="top_p")
-            temperature = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.05, label="temperature")
         
-    understanding_button = gr.Button("Chat")
+        pkv = None
-    understanding_output = gr.Textbox(label="Response")
+        for i in range(image_token_num_per_image):
+            outputs = vl_gpt.language_model.model(
+                inputs_embeds=inputs_embeds,
+                past_key_values=pkv,
+                use_cache=True
+            )
+            pkv = outputs.past_key_values
+            logits = vl_gpt.gen_head(outputs.last_hidden_state[:, -1, :])
             
-    examples_inpainting = gr.Examples(
+            # Classifier-free guidance
-        label="Multimodal Understanding examples",
+            logit_cond, logit_uncond = logits[0::2], logits[1::2]
-        examples=[
+            logits = logit_uncond + kwargs['cfg_weight'] * (logit_cond - logit_uncond)
-            [
-                "explain this meme",
-                "images/doge.png",
-            ],
-            [
-                "Convert the formula into latex code.",
-                "images/equation.png",
-            ],
-        ],
-        inputs=[question_input, image_input],
-    )
             
+            # Sampling
+            probs = torch.softmax(logits / kwargs['temperature'], dim=-1)
+            next_token = torch.multinomial(probs, 1)
+            generated[:, i] = next_token.squeeze()
             
-    gr.Markdown(value="# Text-to-Image Generation")
+            # Prepare next input
+            inputs_embeds = vl_gpt.prepare_gen_img_embeds(
+                next_token.repeat(1, 2).view(-1)
+            ).unsqueeze(1)
 
+        # Decode patches
+        return generated, vl_gpt.gen_vision_model.decode_code(
+            generated.to(torch.int),
+            shape=[parallel_size, 8, width//16, height//16]
+        )
     
+    except Exception as e:
+        logger.error(f"Generate core error: {str(e)}")
+        raise
 
-    with gr.Row():
+def unpack(dec, width, height, parallel_size):
-        cfg_weight_input = gr.Slider(minimum=1, maximum=10, value=5, step=0.5, label="CFG Weight")
+    """Convert model output to images"""
-        t2i_temperature = gr.Slider(minimum=0, maximum=1, value=1.0, step=0.05, label="temperature")
+    try:
+        dec = dec.float().cpu().numpy().transpose(0, 2, 3, 1)
+        dec = np.clip((dec + 1) * 127.5, 0, 255).astype(np.uint8)
+        return [dec[i] for i in range(parallel_size)]
+    except Exception as e:
+        logger.error(f"Unpack error: {str(e)}")
+        return [np.zeros((height, width, 3), dtype=np.uint8)] * parallel_size
 
-    prompt_input = gr.Textbox(label="Prompt. (Prompt in more detail can help produce better images!)")
+# Gradio Interface
-    seed_input = gr.Number(label="Seed (Optional)", precision=0, value=12345)
+with gr.Blocks(title="Janus Pro 7B", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 🖼️ Janus Pro 7B - Multimodal AI Assistant")
     
-    generation_button = gr.Button("Generate Images")
+    with gr.Tab("Image Understanding"):
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(label="Upload Image", type="numpy")
+                # examples_und = gr.Examples(
+                #     examples=[
+                #         ["explain this meme", "images/doge.png"],
+                #         ["Convert the formula into latex code", "images/equation.png"]
+                #     ],
+                #     inputs=[gr.Textbox(), image_input],  # Use component references
+                #     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)
 
-    image_output = gr.Gallery(label="Generated Images", columns=2, rows=2, height=300)
+    with gr.Tab("Image Generation"):
-
+        with gr.Row():
-    examples_t2i = gr.Examples(
+            with gr.Column():
-        label="Text to image generation examples.",
+                prompt_input = gr.Textbox(label="Prompt", placeholder="Describe your image...", lines=3)
-        examples=[
+                examples_t2i = gr.Examples(
-            "Master shifu racoon wearing drip attire as a street gangster.",
+                    examples=[
-            "The face of a beautiful girl",
+                        "Master shifu raccoon wearing streetwear",
-            "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+                        "Astronaut in a jungle, detailed 8k rendering"
-            "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.",
+                    inputs=prompt_input,
-            "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.",
+                    label="Example Prompts"
-        ],
+                )
-        inputs=prompt_input,
+                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(
         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
     )
     
     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
     )
 
-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
+    )