inital deployment1

.gitignore

@@ -0,0 +1 @@
+Real-Time-Latent-Consistency-Model/

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import cv2
 import torch
 import numpy as np
-from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline,AutoPipelineForImage2Image,AutoencoderTiny
 from transformers import AutoProcessor, AutoModel, AutoTokenizer
 from PIL import Image
 
@@ -10,24 +10,64 @@ from PIL import Image
 device = "cuda" if torch.cuda.is_available() else "cpu"
 ##realtime_pipe = StableDiffusionPipeline.from_pretrained("radames/Real-Time-Latent-Consistency-Model").to(device)
 
-# Load the model (optimized for inference)
-model_id = "radames/Real-Time-Latent-Consistency-Model"
+# Load the model (optimized for inference)#
+#model_id = "radames/Real-Time-Latent-Consistency-Model"
 
-tokenizer = AutoTokenizer.from_pretrained(model_id)
+# model_id = "stabilityai/sd-turbo"
+# AutoPipelineForImage2Image.from_pretrained(base_model)
+# 
+# tokenizer = AutoTokenizer.from_pretrained(model_id)
+# 
+# realtime_pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+# realtime_pipe.to("cuda")  # Use GPU for faster inference
 
-realtime_pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-realtime_pipe.to("cuda")  # Use GPU for faster inference
 
-
-def process_frame(frame, prompt="A futuristic landscape"):
-    """Process a single frame using the real-time latent consistency model."""
-    
-    # Convert frame to PIL image
-    image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)).resize((512, 512))
-    
-    # Apply Real-Time Latent Consistency Model
-    result = realtime_pipe(prompt=prompt, image=image, strength=0.5, guidance_scale=7.5).images[0]
-    return np.array(result)
+# 
+# 
+# def predict(prompt, frame):
+#     generator = torch.manual_seed(params.seed)
+#     steps = params.steps
+#     strength = params.strength
+#     if int(steps * strength) < 1:
+#         steps = math.ceil(1 / max(0.10, strength))
+# 
+#     prompt = params.prompt
+#     prompt_embeds = None
+#     
+#     results = self.pipe(
+#         image=frame,
+#         prompt_embeds=prompt_embeds,
+#         prompt=prompt,
+#         negative_prompt=params.negative_prompt,
+#         generator=generator,
+#         strength=strength,
+#         num_inference_steps=steps,
+#         guidance_scale=1.1,
+#         width=params.width,
+#         height=params.height,
+#         output_type="pil",
+#         )
+# 
+#     nsfw_content_detected = (
+#         results.nsfw_content_detected[0]
+#         if "nsfw_content_detected" in results
+#         else False
+#     )
+#     if nsfw_content_detected:
+#         return None
+#     result_image = results.images[0]
+# 
+#     return result_image
+# 
+# def process_frame(frame, prompt="A futuristic landscape"):
+#     """Process a single frame using the real-time latent consistency model."""
+#     
+#     # Convert frame to PIL image
+#     image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)).resize((512, 512))
+#     
+#     # Apply Real-Time Latent Consistency Model
+#     result = realtime_pipe(prompt=prompt, image=image, strength=0.5, guidance_scale=7.5).images[0]
+#     return np.array(result)
 
 def video_stream(prompt):
     """Captures video feed from webcam and sends to the AI model."""
@@ -56,7 +96,7 @@ with gr.Blocks() as demo:
     prompt_input = gr.Textbox(label="Real-Time LCM Prompt", value="A futuristic landscape")
     start_button = gr.Button("Start Real-Time AI Enhancement")
     
-    start_button.click(fn=video_stream, inputs=[prompt_input], outputs=[processed_image, canvas_output])
+    #start_button.click(fn=video_stream, inputs=[prompt_input], outputs=[processed_image, canvas_output])
 
 demo.launch(share=True)