Add Replicate Demos

README.md

@@ -21,6 +21,9 @@
   <a href="https://huggingface.co/deepseek-ai" target="_blank">
     <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" />
   </a>
+  <a href="https://replicate.com/deepseek-ai" target="_blank_">
+    <img src="https://replicate.com/deepseek-ai/janus-pro-7b/badge" alt="Replicate"/>
+  </a>
 
 </div>
 

demo/cog.yaml

@@ -0,0 +1,25 @@
+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+  gpu: true
+  cuda: "12.1"
+  python_version: "3.11"
+  python_packages:
+    - "torch==2.2"
+    - "torchvision"
+    - "transformers==4.36.2"
+    - "accelerate==1.3.0"
+    - "diffusers==0.32.2"
+    - "opencv-python==4.10.0.84"
+    - "attrdict==2.0.1"
+    - "timm==1.0.14"
+    - "sentencepiece==0.2.0"
+    - "einops==0.8.0"
+    - "pillow==10.2.0"
+    - "numpy==1.24.3"
+
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.8.2/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+
+predict: "predict.py:Predictor" 

demo/predict.py

@@ -0,0 +1,98 @@
+from cog import BasePredictor, Input, Path
+import os
+import time
+import torch
+import subprocess
+import numpy as np
+from PIL import Image
+from janus.models import VLChatProcessor
+from transformers import AutoConfig, AutoModelForCausalLM
+
+MODEL_CACHE = "checkpoints"
+# MODEL_URL = "https://weights.replicate.delivery/default/deepseek-ai/Janus-Pro-1B/model.tar"
+MODEL_URL = "https://weights.replicate.delivery/default/deepseek-ai/Janus-Pro-7B/model.tar"
+
+def download_weights(url, dest):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    subprocess.check_call(["pget", "-xf", url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+class Predictor(BasePredictor):
+    def setup(self):
+        """Load the model into memory to make running multiple predictions efficient"""
+        if not os.path.exists(MODEL_CACHE):
+            download_weights(MODEL_URL, MODEL_CACHE)
+
+        config = AutoConfig.from_pretrained(MODEL_CACHE)
+        language_config = config.language_config
+        language_config._attn_implementation = 'eager'
+
+        self.vl_gpt = AutoModelForCausalLM.from_pretrained(
+            MODEL_CACHE,
+            language_config=language_config,
+            trust_remote_code=True
+        )
+
+        if torch.cuda.is_available():
+            self.vl_gpt = self.vl_gpt.to(torch.bfloat16).cuda()
+        else:
+            self.vl_gpt = self.vl_gpt.to(torch.float16)
+
+        self.vl_chat_processor = VLChatProcessor.from_pretrained(MODEL_CACHE)
+        self.tokenizer = self.vl_chat_processor.tokenizer
+        self.cuda_device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+    @torch.inference_mode()
+    def predict(
+        self,
+        image: Path = Input(description="Input image for multimodal understanding"),
+        question: str = Input(description="Question about the image"),
+        seed: int = Input(description="Random seed for reproducibility", default=42),
+        top_p: float = Input(description="Top-p sampling value", default=0.95, ge=0, le=1),
+        temperature: float = Input(description="Temperature for text generation", default=0.1, ge=0, le=1),
+    ) -> str:
+        """Run a single prediction on the model"""
+        # Set seed
+        torch.manual_seed(seed)
+        np.random.seed(seed)
+        torch.cuda.manual_seed(seed)
+
+        # Load and process image
+        pil_image = Image.open(image)
+        image_array = np.array(pil_image)
+
+        conversation = [
+            {
+                "role": "<|User|>",
+                "content": f"<image_placeholder>\n{question}",
+                "images": [image_array],
+            },
+            {"role": "<|Assistant|>", "content": ""},
+        ]
+
+        pil_images = [pil_image]
+        prepare_inputs = self.vl_chat_processor(
+            conversations=conversation, 
+            images=pil_images, 
+            force_batchify=True
+        ).to(self.cuda_device, dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16)
+
+        inputs_embeds = self.vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+
+        outputs = self.vl_gpt.language_model.generate(
+            inputs_embeds=inputs_embeds,
+            attention_mask=prepare_inputs.attention_mask,
+            pad_token_id=self.tokenizer.eos_token_id,
+            bos_token_id=self.tokenizer.bos_token_id,
+            eos_token_id=self.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 = self.tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
+        return answer