Fix projector name (David)

nexa/cli/entry.py

@@ -15,27 +15,32 @@ def run_ggml_inference(args):
 
     stop_words = kwargs.pop("stop_words", [])
 
-    if run_type == "NLP":
-        from nexa.gguf.nexa_inference_text import NexaTextInference
-        inference = NexaTextInference(model_path=model_path, local_path=local_path, stop_words=stop_words, **kwargs)
-    elif run_type == "Computer Vision":
-        from nexa.gguf.nexa_inference_image import NexaImageInference
-        inference = NexaImageInference(model_path=model_path, local_path=local_path, **kwargs)
-        if hasattr(args, 'streamlit') and args.streamlit:
-            inference.run_streamlit(model_path)
-        elif args.img2img:
-            inference.run_img2img()
+    try:
+        if run_type == "NLP":
+            from nexa.gguf.nexa_inference_text import NexaTextInference
+            inference = NexaTextInference(model_path=model_path, local_path=local_path, stop_words=stop_words, **kwargs)
+        elif run_type == "Computer Vision":
+            from nexa.gguf.nexa_inference_image import NexaImageInference
+            inference = NexaImageInference(model_path=model_path, local_path=local_path, **kwargs)
+            if hasattr(args, 'streamlit') and args.streamlit:
+                inference.run_streamlit(model_path)
+            elif args.img2img:
+                inference.run_img2img()
+            else:
+                inference.run_txt2img()
+            return
+        elif run_type == "Multimodal":
+            from nexa.gguf.nexa_inference_vlm import NexaVLMInference
+            inference = NexaVLMInference(model_path=model_path, local_path=local_path, stop_words=stop_words, **kwargs)
+        elif run_type == "Audio":
+            from nexa.gguf.nexa_inference_voice import NexaVoiceInference
+            inference = NexaVoiceInference(model_path=model_path, local_path=local_path, **kwargs)
         else:
-            inference.run_txt2img()
+            print(f"Unknown task: {run_type}. Skipping inference.")
+            return
+    except Exception as e:
+        print(f"Error loading GGUF models, please refer to our docs to install nexaai package: https://docs.nexaai.com/getting-started/installation ")
         return
-    elif run_type == "Multimodal":
-        from nexa.gguf.nexa_inference_vlm import NexaVLMInference
-        inference = NexaVLMInference(model_path=model_path, local_path=local_path, stop_words=stop_words, **kwargs)
-    elif run_type == "Audio":
-        from nexa.gguf.nexa_inference_voice import NexaVoiceInference
-        inference = NexaVoiceInference(model_path=model_path, local_path=local_path, **kwargs)
-    else:
-        raise ValueError(f"Unknown task: {run_type}")
 
     if hasattr(args, 'streamlit') and args.streamlit:
         inference.run_streamlit(model_path)
@@ -49,20 +54,25 @@ def run_onnx_inference(args):
     from nexa.general import pull_model
     local_path, run_type = pull_model(model_path)
 
-    if run_type == "NLP":
-        from nexa.onnx.nexa_inference_text import NexaTextInference as NexaTextOnnxInference
-        inference = NexaTextOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
-    elif run_type == "Computer Vision":
-        from nexa.onnx.nexa_inference_image import NexaImageInference as NexaImageOnnxInference
-        inference = NexaImageOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
-    elif run_type == "Audio":
-        from nexa.onnx.nexa_inference_voice import NexaVoiceInference as NexaVoiceOnnxInference
-        inference = NexaVoiceOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
-    elif run_type == "TTS":
-        from nexa.onnx.nexa_inference_tts import NexaTTSInference as NexaTTSOnnxInference
-        inference = NexaTTSOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
-    else:
-        raise ValueError(f"Unknown task: {run_type}")
+    try:
+        if run_type == "NLP":
+            from nexa.onnx.nexa_inference_text import NexaTextInference as NexaTextOnnxInference
+            inference = NexaTextOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
+        elif run_type == "Computer Vision":
+            from nexa.onnx.nexa_inference_image import NexaImageInference as NexaImageOnnxInference
+            inference = NexaImageOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
+        elif run_type == "Audio":
+            from nexa.onnx.nexa_inference_voice import NexaVoiceInference as NexaVoiceOnnxInference
+            inference = NexaVoiceOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
+        elif run_type == "TTS":
+            from nexa.onnx.nexa_inference_tts import NexaTTSInference as NexaTTSOnnxInference
+            inference = NexaTTSOnnxInference(model_path=model_path, local_path=local_path, **kwargs)
+        else:
+            print(f"Unknown task: {run_type}. Skipping inference.")
+            return
+    except Exception as e:
+        print(f"Error loading ONNX models, please refer to our docs to install nexaai[onnx] package: https://docs.nexaai.com/getting-started/installation ")
+        return
 
     if hasattr(args, 'streamlit') and args.streamlit:
         inference.run_streamlit(model_path)

nexa/constants.py

@@ -120,7 +120,7 @@
 
 NEXA_RUN_PROJECTOR_MAP = {
     "nanollava": "nanoLLaVA:projector-fp16",
-    "nanoLLaVA:fp16": "nanoLLaVA:project-fp16",
+    "nanoLLaVA:fp16": "nanoLLaVA:projector-fp16",
     "llava-phi3": "llava-phi-3-mini:projector-q4_0",
     "llava-phi-3-mini:q4_0": "llava-phi-3-mini:projector-q4_0",
     "llava-phi-3-mini:fp16": "llava-phi-3-mini:projector-fp16",

nexa/gguf/nexa_inference_image.py

@@ -115,7 +115,7 @@ def _retry(self, func, *args, **kwargs):
             except Exception as e:
                 logging.error(f"Attempt {attempt + 1} failed with error: {e}")
                 time.sleep(1)
-        logging.error("All retry attempts failed.")
+        print("All retry attempts failed becase of Out of Memory error, Try to use smaller models...")
         return None
 
     def txt2img(

nexa/gguf/streamlit/streamlit_image_chat.py

@@ -4,6 +4,7 @@
 from nexa.general import pull_model
 import streamlit as st
 from nexa.gguf.nexa_inference_image import NexaImageInference
+import io
 
 default_model = sys.argv[1]
 
@@ -106,5 +107,16 @@ def generate_images(nexa_model: NexaImageInference, prompt: str, negative_prompt
                 st.session_state.nexa_model, prompt, negative_prompt
             )
             st.success("Images generated successfully!")
-            for image in images:
-                st.image(image, caption="Generated Image", use_column_width=True)
+            for i, image in enumerate(images):
+                st.image(image, caption=f"Generated Image", use_column_width=True)
+
+                img_byte_arr = io.BytesIO()
+                image.save(img_byte_arr, format='PNG')
+                img_byte_arr = img_byte_arr.getvalue()
+
+                st.download_button(
+                    label=f"Download Image",
+                    data=img_byte_arr,
+                    file_name=f"generated_image.png",
+                    mime="image/png"
+                )

nexa/onnx/streamlit/streamlit_image_chat.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 import streamlit as st
-
+from optimum.onnxruntime import ORTLatentConsistencyModelPipeline
 from nexa.general import pull_model
 from nexa.onnx.nexa_inference_image import NexaImageInference
 
@@ -15,11 +15,11 @@ def load_model(model_path):
     local_path, run_type = pull_model(model_path)
     nexa_model = NexaImageInference(model_path=model_path, local_path=local_path)
 
-    if nexa_model.downloaded_onnx_folder is None:
+    if nexa_model.download_onnx_folder is None:
         st.error("Failed to download the model. Please check the model path.")
         return None
 
-    nexa_model._load_model(nexa_model.downloaded_onnx_folder)
+    nexa_model._load_model(nexa_model.download_onnx_folder)
     return nexa_model
 
 
@@ -30,17 +30,21 @@ def generate_images(nexa_model: NexaImageInference, prompt, negative_prompt):
 
     generator = np.random.RandomState(nexa_model.params["random_seed"])
 
-    images = nexa_model.pipeline(
-        prompt=prompt,
-        negative_prompt=negative_prompt if negative_prompt else None,
-        num_inference_steps=nexa_model.params["num_inference_steps"],
-        num_images_per_prompt=nexa_model.params["num_images_per_prompt"],
-        height=nexa_model.params["height"],
-        width=nexa_model.params["width"],
-        generator=generator,
-        guidance_scale=nexa_model.params["guidance_scale"],
-    ).images
+    is_lcm_pipeline = isinstance(nexa_model.pipeline, ORTLatentConsistencyModelPipeline)
+
+    pipeline_kwargs = {
+        "prompt": prompt,
+        "num_inference_steps": nexa_model.params["num_inference_steps"],
+        "num_images_per_prompt": nexa_model.params["num_images_per_prompt"],
+        "height": nexa_model.params["height"],
+        "width": nexa_model.params["width"],
+        "generator": generator,
+        "guidance_scale": nexa_model.params["guidance_scale"],
+    }
+    if not is_lcm_pipeline and negative_prompt:
+        pipeline_kwargs["negative_prompt"] = negative_prompt
 
+    images = nexa_model.pipeline(**pipeline_kwargs).images
     return images
 
 

pyproject.toml

@@ -24,10 +24,9 @@ dependencies = [
     "prompt_toolkit",
     "tqdm",                     # Shared dependencies
     "tabulate",
-    "streamlit",
+    "streamlit>=1.37.1",
     "streamlit-audiorec",
     "python-multipart",
-    "streamlit-audiorec",
     "cmake",
 ]
 classifiers = [