Update app.py

app.py

@@ -44,7 +44,7 @@ model = TSR.from_pretrained(
     config_name="config.yaml",
     weight_name="model.ckpt",
 )
-model.renderer.set_chunk_size(104218)
+model.renderer.set_chunk_size(131072) 
 model.to(device)
 
 rembg_session = rembg.new_session()
@@ -55,91 +55,6 @@ bedrock_runtime = boto3.client(service_name='bedrock-runtime', aws_access_key_id
 s3_client = boto3.client('s3',aws_access_key_id = ACCESS, aws_secret_access_key = SECRET, region_name='us-east-1')
 
 
-def gen_pos_prompt(text):
-  instruction = f'''Your task is to create a positive prompt for image generation.
-    Objective: Generate images that prioritize structural integrity and accurate shapes. The focus should be on the correct form and basic contours of objects, with minimal concern for colors.
-    
-    Guidelines:
-    
-    Complex Objects (e.g., animals, vehicles): For these, the image should resemble a toy object, emphasizing the correct shape and structure while minimizing details and color complexity.
-    
-    Example Input: A sports bike
-    Example Positive Prompt: Simple sports bike with accurate shape and structure, minimal details, digital painting, concept art style, basic contours, soft lighting, clean lines, neutral or muted colors, toy-like appearance, low contrast.
-    
-    Example Input: A lion
-    Example Positive Prompt: Toy-like depiction of a lion with a focus on structural accuracy, minimal details, digital painting, concept art style, basic contours, soft lighting, clean lines, neutral or muted colors, simplified features, low contrast.
-    
-    Simple Objects (e.g., a tennis ball): For these, the prompt should specify a realistic depiction, focusing on the accurate shape and structure.
-    
-    Example Input: A tennis ball
-    Example Positive Prompt: Realistic depiction of a tennis ball with accurate shape and texture, digital painting, clean lines, minimal additional details, soft lighting, neutral or muted colors, focus on structural integrity.
-    
-    Prompt Structure:
-    
-    Subject: Clearly describe the object and its essential shape and structure.
-    Medium: Specify the art style (e.g., digital painting, concept art).
-    Style: Include relevant style terms (e.g., simplified, toy-like for complex objects; realistic for simple objects).
-    Resolution: Mention resolution if necessary (e.g., basic resolution).
-    Lighting: Indicate the type of lighting (e.g., soft lighting).
-    Color: Use neutral or muted colors with minimal emphasis on color details.
-    Additional Details: Keep additional details minimal or specify if not desired.
-    Input: {text}
-    Positive Prompt: 
-    '''
-
-  body = json.dumps({'inputText': instruction,
-                     'textGenerationConfig': {'temperature': 0.1, 'topP': 0.01, 'maxTokenCount':512}})
-  response = bedrock_runtime.invoke_model(body=body, modelId='amazon.titan-text-express-v1')
-  pos_prompt = json.loads(response.get('body').read())['results'][0]['outputText']
-  return pos_prompt
-
-def encode_image_to_base64(image):
-    with io.BytesIO() as buffered:
-        image.save(buffered, format="PNG")
-        return base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-
-def generate_image_from_text(encoded_image, seed, pos_prompt=None):
-    neg_prompt = '''Detailed, complex textures, intricate patterns, realistic lighting, high contrast, reflections, fuzzy surface, realistic proportions, photographic quality, vibrant colors, detailed background, shadows, disfigured, deformed, ugly, multiple, duplicate.'''
-    encoded_str = encode_image_to_base64(encoded_image)
-    if pos_prompt:
-        parameters = {
-              'taskType': 'IMAGE_VARIATION',
-              'imageVariationParams': {
-                  'images': [encoded_str],
-                  'text': gen_pos_prompt(pos_prompt),
-                  'negativeText': neg_prompt,
-                  'similarityStrength': 0.7
-              },
-              'imageGenerationConfig': {
-                  "cfgScale": 8,
-                  "seed": seed,
-                  "width": 512,
-                  "height": 512,
-                  "numberOfImages": 1
-              }
-          }
-    else:
-        parameters = {
-          'taskType': 'IMAGE_VARIATION',
-          'imageVariationParams': {
-              'images': [encoded_str],
-              'negativeText': neg_prompt,
-              'similarityStrength': 0.7
-          },
-          'imageGenerationConfig': {
-              "cfgScale": 8,
-              "seed": seed,
-              "width": 512,
-              "height": 512,
-              "numberOfImages": 1
-          }
-      }  
-    request_body = json.dumps(parameters)
-    response = bedrock_runtime.invoke_model(body=request_body, modelId='amazon.titan-image-generator-v1')
-    response_body = json.loads(response.get('body').read())
-    base64_image_data = base64.b64decode(response_body['images'][0])
-    return Image.open(io.BytesIO(base64_image_data))
 
 
 def upload_file_to_s3(file_path, bucket_name, object_name=None):
@@ -217,12 +132,8 @@ async def process_image(
     
     if auth == os.getenv("AUTHORIZE"):
         image_bytes = await file.read()
-        input_image = Image.open(BytesIO(image_bytes))
-    
-        if enhance_image:
-            image_pil = generate_image_from_text(encoded_image=input_image, seed=seed, pos_prompt=text_prompt)
-        else:
-            image_pil = input_image
+        image_pil = Image.open(BytesIO(image_bytes))
+
     
         preprocessed = preprocess(image_pil, do_remove_background, foreground_ratio)
         mesh_name_obj, mesh_name_glb = generate(preprocessed, mc_resolution)