bug fix

nexa/cli/entry.py

@@ -102,11 +102,11 @@ def main():
     image_group = run_parser.add_argument_group('Image generation options')
     image_group.add_argument("-i2i", "--img2img", action="store_true", help="Whether to run image-to-image generation")
     image_group.add_argument("-ns", "--num_inference_steps", type=int, help="Number of inference steps")
-    image_group.add_argument("-np", "--num_images_per_prompt", type=int, help="Number of images to generate per prompt")
+    image_group.add_argument("-np", "--num_images_per_prompt", type=int, deafult=1, help="Number of images to generate per prompt")
     image_group.add_argument("-H", "--height", type=int, help="Height of the output image")
     image_group.add_argument("-W", "--width", type=int, help="Width of the output image")
     image_group.add_argument("-g", "--guidance_scale", type=float, help="Guidance scale for diffusion")
-    image_group.add_argument("-o", "--output", type=str, help="Output path for the generated image")
+    image_group.add_argument("-o", "--output", type=str, default="generated_images/image.png", help="Output path for the generated image")
     image_group.add_argument("-s", "--random_seed", type=int, help="Random seed for image generation")
     image_group.add_argument("--lora_dir", type=str, help="Path to directory containing LoRA files")
     image_group.add_argument("--wtype", type=str, help="Weight type (f32, f16, q4_0, q4_1, q5_0, q5_1, q8_0)")

nexa/gguf/nexa_inference_image.py

@@ -218,7 +218,7 @@ def run_img2img(self):
                                       sample_steps=self.params["num_inference_steps"],
                                       seed=self.params["random_seed"],
                                       control_cond=self.params.get("control_image_path", ""),
-                                        control_strength=self.params.get("control_strength", 0.9),
+                                      control_strength=self.params.get("control_strength", 0.9),
                                     )
 
                 self._save_images(images)

nexa/gguf/streamlit/streamlit_image_chat.py

@@ -21,13 +21,16 @@ def generate_images(nexa_model: NexaImageInference, prompt: str, negative_prompt
     if not os.path.exists(output_dir):
         os.makedirs(output_dir)
 
-    nexa_model._txt2img(prompt, negative_prompt)
-
-    images = []
-    for file_name in os.listdir(output_dir):
-        if file_name.endswith(".png"):
-            image_path = os.path.join(output_dir, file_name)
-            images.append(Image.open(image_path))
+    images = nexa_model.txt2img(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        cfg_scale=nexa_model.params["guidance_scale"],
+        width=nexa_model.params["width"],
+        height=nexa_model.params["height"],
+        sample_steps=nexa_model.params["num_inference_steps"],
+        seed=nexa_model.params["random_seed"]
+    )
+
     return images
 
 

nexa/gguf/streamlit/streamlit_vlm.py

@@ -13,7 +13,7 @@
 @st.cache_resource
 def load_model(model_path):
     local_path, run_type = pull_model(model_path)
-    nexa_model = NexaVLMInference(model_pat=model_path, local_path=local_path)
+    nexa_model = NexaVLMInference(model_path=model_path, local_path=local_path)
     return nexa_model
 
 

pyproject.toml

@@ -24,6 +24,7 @@ dependencies = [
     "tqdm",                     # Shared dependencies
     "tabulate",
     "streamlit",
+    "streamlit-audiorec",
     "python-multipart",
 ]
 classifiers = [

requirements.txt

@@ -26,7 +26,7 @@ PyYAML
 requests
 setuptools
 soundfile
-streamlit_audiorec
+streamlit-audiorec
 transformers
 ttstokenizer