Help - Trying to connect n8n to fooocus via http request using fastapi

[surenjanath]

Hi I am trying and going in circles in trying to connect n8n http node that sends prompt and base model to my fastapi code and wait for the image to be generated and this gives me help.

i've attached the fastapi.py code below

from fastapi.responses import FileResponse
from pydantic import BaseModel
from gradio_client import Client
import os
import traceback
from typing import Optional
import requests

app = FastAPI()

# --- CONFIGURATION ---
FOOOCUS_URL = "http://127.0.0.1:7865/"
FN_INDEX_IMAGE_GENERATION = 67
# This count is correct. Do not change.
TARGET_ARGS_COUNT = 152

# Health check endpoint
@app.get("/")
async def root():
    return {"message": "Fooocus API is running", "endpoints": ["/generate", "/generate_simple", "/generate_robust", "/generate_auto", "/generate_working", "/generate_simple_web"]}

@app.get("/health")
async def health_check():
    return {"status": "healthy", "fooocus_url": FOOOCUS_URL}

# The ImageRequest now requires you to specify a base_model from n8n
class ImageRequest(BaseModel):
    prompt: str
    base_model: str  # e.g., "juggernautXL_v9Rundiffusion.safetensors"
    negative_prompt: Optional[str] = ""
    style: Optional[str] = "Fooocus V2"
    performance: Optional[str] = "Speed"
    aspect_ratio: Optional[str] = "1152*896"
    image_number: Optional[int] = 1
    output_format: Optional[str] = "png"
    seed: Optional[int] = -1
    sharpness: Optional[float] = 2.0
    guidance_scale: Optional[float] = 4.0

@app.post("/generate")
async def generate_image(request: ImageRequest):
    print(f"Received prompt: '{request.prompt}'")
    print(f"Using base model: '{request.base_model}'")
    try:
        client = Client(FOOOCUS_URL)
        
        # Get the function signature to understand the exact arguments
        api_info = client.view_api()
        print(f"Available functions: {list(api_info.keys())}")
        
        # Find the image generation function (usually the one with the most arguments)
        target_fn = None
        for fn_name, fn_info in api_info.items():
            if fn_name.startswith('fn_index_67') or 'generate' in fn_name.lower():
                target_fn = fn_name
                break
        
        if not target_fn:
            # Fallback: use the function with the most parameters
            target_fn = max(api_info.keys(), key=lambda k: len(api_info[k]['parameters']))
        
        print(f"Using function: {target_fn}")
        print(f"Function parameters: {api_info[target_fn]['parameters']}")
        
        # Create a dictionary of arguments based on the API signature
        args_dict = {}
        
        # Set the arguments we know
        args_dict['prompt'] = request.prompt
        args_dict['negative_prompt'] = request.negative_prompt
        args_dict['style_selections'] = [request.style]
        args_dict['performance_selection'] = request.performance
        args_dict['aspect_ratios_selection'] = request.aspect_ratio
        args_dict['image_number'] = request.image_number
        args_dict['output_format'] = request.output_format
        args_dict['image_seed'] = request.seed
        args_dict['sharpness'] = request.sharpness
        args_dict['guidance_scale'] = request.guidance_scale
        args_dict['base_model'] = request.base_model
        
        # Set default values for required parameters
        args_dict['metadata_scheme'] = 'fooocus'  # This should fix the MetadataScheme error
        
        print("Submitting job to Fooocus...")
        job = client.submit(target_fn, **args_dict)

        print("Waiting for job to complete...")
        result = job.result()
        print("Job result received.")

        if result is None:
             raise ValueError("Job completed but returned None. This usually means an incorrect model name or an out-of-memory error on the Fooocus server. Check the Fooocus console for details.")

        if isinstance(result, tuple) and len(result) > 0:
            gallery_output = result[-1]
            if isinstance(gallery_output, dict) and "files" in gallery_output:
                files = gallery_output["files"]
                if files and isinstance(files[0], dict) and "path" in files[0]:
                    image_path = files[0]["path"]
                    if image_path and os.path.exists(image_path):
                        print(f"SUCCESS! Image generated at: {image_path}")
                        return FileResponse(image_path, media_type="image/png", filename="generated_image.png")

        raise HTTPException(status_code=500, detail=f"Could not parse image path from result: {result}")

    except Exception as e:
        print("--- DETAILED ERROR ---")
        traceback.print_exc()
        print("----------------------")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")

# Alternative approach: Simple parameter passing
@app.post("/generate_simple")
async def generate_image_simple(request: ImageRequest):
    print(f"Received prompt: '{request.prompt}'")
    print(f"Using base model: '{request.base_model}'")
    try:
        client = Client(FOOOCUS_URL)
        
        # Use a simpler approach - just pass the essential parameters
        # and let Fooocus handle the rest with defaults
        result = client.submit(
            fn_index=FN_INDEX_IMAGE_GENERATION,
            _data=[
                False,                              # generate_image_grid
                request.prompt,                     # prompt
                request.negative_prompt,            # negative_prompt
                [request.style],                    # style_selections
                request.performance,                # performance_selection
                request.aspect_ratio,               # aspect_ratios_selection
                request.image_number,               # image_number
                request.output_format,              # output_format
                request.seed,                       # image_seed
                True,                               # read_wildcards_in_order
                request.sharpness,                  # sharpness
                request.guidance_scale,             # guidance_scale
                request.base_model,                 # base_model
                "None",                             # refiner_model
                0.8,                                # refiner_switch
            ]
        )
        
        print("Waiting for job to complete...")
        result = result.result()
        print("Job result received.")
        
        if result is None:
             raise ValueError("Job completed but returned None. This usually means an incorrect model name or an out-of-memory error on the Fooocus server. Check the Fooocus console for details.")

        if isinstance(result, tuple) and len(result) > 0:
            gallery_output = result[-1]
            if isinstance(gallery_output, dict) and "files" in gallery_output:
                files = gallery_output["files"]
                if files and isinstance(files[0], dict) and "path" in files[0]:
                    image_path = files[0]["path"]
                    if image_path and os.path.exists(image_path):
                        print(f"SUCCESS! Image generated at: {image_path}")
                        return FileResponse(image_path, media_type="image/png", filename="generated_image.png")

        raise HTTPException(status_code=500, detail=f"Could not parse image path from result: {result}")

    except Exception as e:
        print("--- DETAILED ERROR ---")
        traceback.print_exc()
        print("----------------------")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")

# Third approach: Original method with better error handling
@app.post("/generate_robust")
async def generate_image_robust(request: ImageRequest):
    print(f"Received prompt: '{request.prompt}'")
    print(f"Using base model: '{request.base_model}'")
    try:
        client = Client(FOOOCUS_URL)
        
        # Build the argument list with proper defaults
        args = [
            False,                              # generate_image_grid
            request.prompt,                     # prompt
            request.negative_prompt,            # negative_prompt
            [request.style],                    # style_selections
            request.performance,                # performance_selection
            request.aspect_ratio,               # aspect_ratios_selection
            request.image_number,               # image_number
            request.output_format,              # output_format
            request.seed,                       # image_seed
            True,                               # read_wildcards_in_order
            request.sharpness,                  # sharpness
            request.guidance_scale,             # guidance_scale
            request.base_model,                 # base_model
            "None",                             # refiner_model
            0.8,                                # refiner_switch
        ]
        
        # Add LoRA controls (5 LoRAs × 3 params each = 15 args)
        for _ in range(5):
            args.extend([False, "None", 1.0])  # enabled, model, weight
        
        # Add remaining controls with sensible defaults
        remaining_args = [
            False,                              # input_image_checkbox
            "uov_tab",                          # current_tab
            "Disabled",                         # uov_method
            None,                               # uov_input_image
            [],                                 # outpaint_selections
            None,                               # inpaint_input_image
            "",                                 # inpaint_additional_prompt
            None,                               # inpaint_mask_image_upload
            False,                              # disable_preview
            False,                              # disable_intermediate_results
            False,                              # disable_seed_increment
            False,                              # black_out_nsfw
            1.5,                                # adm_scaler_positive
            0.8,                                # adm_scaler_negative
            0.3,                                # adm_scaler_end
            7.0,                                # adaptive_cfg
            1,                                  # clip_skip
            "dpmpp_2m_sde_gpu",                # sampler_name
            "karras",                           # scheduler_name
            "Default (model)",                  # vae_name
            -1,                                 # overwrite_step
            -1,                                 # overwrite_switch
            -1,                                 # overwrite_width
            -1,                                 # overwrite_height
            -1,                                 # overwrite_vary_strength
            -1,                                 # overwrite_upscale_strength
            False,                              # mixing_image_prompt_and_vary_upscale
            False,                              # mixing_image_prompt_and_inpaint
            False,                              # debugging_cn_preprocessor
            False,                              # skipping_cn_preprocessor
            64,                                 # canny_low_threshold
            128,                                # canny_high_threshold
            "joint",                            # refiner_swap_method
            0.25,                               # controlnet_softness
            False,                              # freeu_enabled
            1.01,                               # freeu_b1
            1.02,                               # freeu_b2
            0.99,                               # freeu_s1
            0.95,                               # freeu_s2
            False,                              # debugging_inpaint_preprocessor
            False,                              # inpaint_disable_initial_latent
            "v2.6",                             # inpaint_engine
            1.0,                                # inpaint_strength
            0.618,                              # inpaint_respective_field
            False,                              # inpaint_advanced_masking_checkbox
            False,                              # invert_mask_checkbox
            0,                                  # inpaint_erode_or_dilate
            False,                              # save_final_enhanced_image_only
            True,                               # save_metadata_to_images
            "fooocus",                          # metadata_scheme - This should fix the error!
        ]
        
        args.extend(remaining_args)
        
        # Add ControlNet arguments (4 × 4 = 16 args)
        for _ in range(4):
            args.extend([None, 1.0, 1.0, "ImagePrompt"])  # img, stop, weight, type
        
        # Add remaining arguments
        args.extend([
            False,                              # debugging_dino
            0,                                  # dino_erode_or_dilate
            False,                              # debugging_enhance_masks_checkbox
            None,                               # enhance_input_image
            False,                              # enhance_checkbox
            "Disabled",                         # enhance_uov_method
            "Before First Enhancement",         # enhance_uov_processing_order
            "Original Prompts",                 # enhance_uov_prompt_type
        ])
        
        # Add enhance controls (2 tabs × 16 args each = 32 args)
        for _ in range(2):
            args.extend([
                False,                          # enhance_enabled
                "",                             # enhance_mask_dino_prompt_text
                "",                             # enhance_prompt
                "",                             # enhance_negative_prompt
                "u2net",                        # enhance_mask_model
                "full",                         # enhance_mask_cloth_category
                "vit_b",                        # enhance_mask_sam_model
                0.25,                           # enhance_mask_text_threshold
                0.3,                            # enhance_mask_box_threshold
                0,                              # enhance_mask_sam_max_detections
                False,                          # enhance_inpaint_disable_initial_latent
                "v2.6",                         # enhance_inpaint_engine
                1.0,                            # enhance_inpaint_strength
                0.0,                            # enhance_inpaint_respective_field
                0,                              # enhance_inpaint_erode_or_dilate
                False,                          # enhance_mask_invert
            ])
        
        # Add more arguments to reach exactly 153
        # We need 16 more arguments (153 - 137 = 16)
        additional_args = [
            False,                              # Additional parameter 1
            False,                              # Additional parameter 2
            False,                              # Additional parameter 3
            False,                              # Additional parameter 4
            False,                              # Additional parameter 5
            False,                              # Additional parameter 6
            False,                              # Additional parameter 7
            False,                              # Additional parameter 8
            False,                              # Additional parameter 9
            False,                              # Additional parameter 10
            False,                              # Additional parameter 11
            False,                              # Additional parameter 12
            False,                              # Additional parameter 13
            False,                              # Additional parameter 14
            False,                              # Additional parameter 15
            False,                              # Additional parameter 16
        ]
        
        args.extend(additional_args)
        
        print(f"Submitting job with {len(args)} arguments")
        print(f"Metadata scheme at position {len(args)-67}: {args[-67] if len(args) >= 67 else 'N/A'}")
        
        job = client.submit(*args, fn_index=FN_INDEX_IMAGE_GENERATION)

        print("Waiting for job to complete...")
        result = job.result()
        print("Job result received.")

        if result is None:
             raise ValueError("Job completed but returned None. This usually means an incorrect model name or an out-of-memory error on the Fooocus server. Check the Fooocus console for details.")

        if isinstance(result, tuple) and len(result) > 0:
            gallery_output = result[-1]
            if isinstance(gallery_output, dict) and "files" in gallery_output:
                files = gallery_output["files"]
                if files and isinstance(files[0], dict) and "path" in files[0]:
                    image_path = files[0]["path"]
                    if image_path and os.path.exists(image_path):
                        print(f"SUCCESS! Image generated at: {image_path}")
                        return FileResponse(image_path, media_type="image/png", filename="generated_image.png")

        raise HTTPException(status_code=500, detail=f"Could not parse image path from result: {result}")

    except Exception as e:
        print("--- DETAILED ERROR ---")
        traceback.print_exc()
        print("----------------------")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")

# Auto-detect endpoint that finds the correct function automatically
@app.post("/generate_auto")
async def generate_image_auto(request: ImageRequest):
    print(f"Auto-detecting function for prompt: '{request.prompt}'")
    try:
        client = Client(FOOOCUS_URL)
        
        # Check if we can connect to the Fooocus server
        try:
            api_info = client.view_api()
            if api_info is None:
                raise HTTPException(status_code=500, detail="Could not connect to Fooocus server. Make sure Fooocus is running on http://127.0.0.1:7865/")
        except Exception as e:
            raise HTTPException(status_code=500, detail=f"Failed to connect to Fooocus server: {e}. Make sure Fooocus is running on http://127.0.0.1:7865/")
        
        print(f"Available functions: {list(api_info.keys())}")
        
        # Find the function with the most parameters (likely the main generation function)
        target_fn = max(api_info.keys(), key=lambda k: len(api_info[k]['parameters']))
        print(f"Using function: {target_fn}")
        print(f"Function parameters: {api_info[target_fn]['parameters']}")
        
        # Create arguments based on the detected function
        fn_params = api_info[target_fn]['parameters']
        
        # Map our request to the function parameters
        args = []
        for param in fn_params:
            param_name = param.get('name', '').lower()
            
            if 'prompt' in param_name and 'negative' not in param_name:
                args.append(request.prompt)
            elif 'negative' in param_name:
                args.append(request.negative_prompt)
            elif 'style' in param_name:
                args.append([request.style])
            elif 'performance' in param_name:
                args.append(request.performance)
            elif 'aspect' in param_name:
                args.append(request.aspect_ratio)
            elif 'image_number' in param_name:
                args.append(request.image_number)
            elif 'output_format' in param_name:
                args.append(request.output_format)
            elif 'seed' in param_name:
                args.append(request.seed)
            elif 'sharpness' in param_name:
                args.append(request.sharpness)
            elif 'guidance' in param_name or 'cfg' in param_name:
                args.append(request.guidance_scale)
            elif 'base_model' in param_name or 'model' in param_name:
                args.append(request.base_model)
            elif 'metadata' in param_name:
                args.append('fooocus')
            else:
                # Use default values for unknown parameters
                if param.get('type') == 'boolean':
                    args.append(False)
                elif param.get('type') == 'number':
                    args.append(0.0)
                elif param.get('type') == 'string':
                    args.append("")
                else:
                    args.append(None)
        
        print(f"Submitting with {len(args)} arguments to function {target_fn}")
        
        # Submit the job
        job = client.submit(target_fn, *args)
        result = job.result()
        
        if result is None:
            raise ValueError("Job completed but returned None. Check the Fooocus console for details.")

        if isinstance(result, tuple) and len(result) > 0:
            gallery_output = result[-1]
            if isinstance(gallery_output, dict) and "files" in gallery_output:
                files = gallery_output["files"]
                if files and isinstance(files[0], dict) and "path" in files[0]:
                    image_path = files[0]["path"]
                    if image_path and os.path.exists(image_path):
                        print(f"SUCCESS! Image generated at: {image_path}")
                        return FileResponse(image_path, media_type="image/png", filename="generated_image.png")

        raise HTTPException(status_code=500, detail=f"Could not parse image path from result: {result}")

    except Exception as e:
        print("--- DETAILED ERROR ---")
        traceback.print_exc()
        print("----------------------")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")

# Simple test endpoint with minimal arguments
@app.post("/test")
async def test_generation(request: ImageRequest):
    print(f"Testing with prompt: '{request.prompt}'")
    try:
        client = Client(FOOOCUS_URL)
        
        # First, let's check what functions are available
        try:
            api_info = client.view_api()
            if api_info is None:
                return {"status": "error", "message": "Could not connect to Fooocus server. Make sure Fooocus is running on http://127.0.0.1:7865/"}
        except Exception as e:
            return {"status": "error", "message": f"Failed to connect to Fooocus server: {e}. Make sure Fooocus is running on http://127.0.0.1:7865/"}
        
        print(f"Available functions: {list(api_info.keys())}")
        
        # Find the function with the most parameters (likely the main generation function)
        target_fn = max(api_info.keys(), key=lambda k: len(api_info[k]['parameters']))
        print(f"Using function: {target_fn}")
        print(f"Function parameters: {api_info[target_fn]['parameters']}")
        
        # Test with just the basic arguments
        args = [
            False,                              # generate_image_grid
            request.prompt,                     # prompt
            request.negative_prompt,            # negative_prompt
            [request.style],                    # style_selections
            request.performance,                # performance_selection
            request.aspect_ratio,               # aspect_ratios_selection
            request.image_number,               # image_number
            request.output_format,              # output_format
            request.seed,                       # image_seed
            True,                               # read_wildcards_in_order
            request.sharpness,                  # sharpness
            request.guidance_scale,             # guidance_scale
            request.base_model,                 # base_model
            "None",                             # refiner_model
            0.8,                                # refiner_switch
        ]
        
        # Try different argument counts
        for arg_count in [152, 153, 154, 155]:
            test_args = args.copy()
            while len(test_args) < arg_count:
                test_args.append(None)
            
            # Set metadata_scheme to fooocus at different positions
            if len(test_args) >= 67:
                test_args[-67] = "fooocus"
            
            print(f"Testing with {len(test_args)} arguments...")
            
            try:
                job = client.submit(*test_args, fn_index=FN_INDEX_IMAGE_GENERATION)
                result = job.result()
                
                if result is not None:
                    return {
                        "status": "success", 
                        "message": f"Found working configuration with {len(test_args)} arguments",
                        "result_type": str(type(result)),
                        "working_arg_count": len(test_args)
                    }
                else:
                    print(f"Result was None with {len(test_args)} arguments")
                    
            except Exception as e:
                print(f"Error with {len(test_args)} arguments: {e}")
                continue
        
        return {"status": "error", "message": "No working configuration found"}
        
    except Exception as e:
        print(f"Test failed: {e}")
        return {"status": "error", "message": str(e)}

# Test connection to Fooocus
@app.get("/test_connection")
async def test_connection():
    try:
        client = Client(FOOOCUS_URL)
        api_info = client.view_api()
        
        if api_info is None:
            return {"status": "error", "message": "Could not connect to Fooocus server"}
        
        return {
            "status": "success", 
            "message": "Successfully connected to Fooocus",
            "available_functions": list(api_info.keys()),
            "function_count": len(api_info.keys())
        }
        
    except Exception as e:
        return {"status": "error", "message": f"Connection failed: {e}"}

# Test connection using basic HTTP
@app.get("/test_http")
async def test_http_connection():
    try:
        # Try to connect to Fooocus using basic HTTP
        response = requests.get(FOOOCUS_URL, timeout=5)
        if response.status_code == 200:
            return {
                "status": "success", 
                "message": "HTTP connection successful",
                "status_code": response.status_code,
                "content_length": len(response.content)
            }
        else:
            return {
                "status": "error", 
                "message": f"HTTP connection failed with status code: {response.status_code}"
            }
    except requests.exceptions.ConnectionError:
        return {"status": "error", "message": "Connection refused - Fooocus is not running or not accessible"}
    except requests.exceptions.Timeout:
        return {"status": "error", "message": "Connection timeout - Fooocus is not responding"}
    except Exception as e:
        return {"status": "error", "message": f"HTTP test failed: {e}"}

# Simple connection test without view_api
@app.get("/test_simple")
async def test_simple_connection():
    try:
        client = Client(FOOOCUS_URL)
        
        # Try a simple operation without view_api
        # This will help us see if the basic connection works
        return {
            "status": "success", 
            "message": "Gradio client created successfully",
            "client_type": str(type(client)),
            "url": FOOOCUS_URL
        }
        
    except Exception as e:
        return {"status": "error", "message": f"Simple connection failed: {e}"}

# Working endpoint that doesn't use view_api
@app.post("/generate_working")
async def generate_image_working(request: ImageRequest):
    print(f"Using working method for prompt: '{request.prompt}'")
    try:
        client = Client(FOOOCUS_URL)
        
        # We know function index 67 expects 153 arguments
        fn_index = 67
        print(f"Using function index: {fn_index} with 153 arguments")
        
        # Create the argument list with exactly 153 arguments
        args = [
            False,                              # generate_image_grid
            request.prompt,                     # prompt
            request.negative_prompt,            # negative_prompt
            [request.style],                    # style_selections
            request.performance,                # performance_selection
            request.aspect_ratio,               # aspect_ratios_selection
            request.image_number,               # image_number
            request.output_format,              # output_format
            request.seed,                       # image_seed
            True,                               # read_wildcards_in_order
            request.sharpness,                  # sharpness
            request.guidance_scale,             # guidance_scale
            request.base_model,                 # base_model
            "None",                             # refiner_model
            0.8,                                # refiner_switch
        ]
        
        # Add LoRA controls (5 LoRAs × 3 params each = 15 args)
        for _ in range(5):
            args.extend([False, "None", 1.0])  # enabled, model, weight
        
        # Add remaining controls with sensible defaults
        remaining_args = [
            False,                              # input_image_checkbox
            "uov_tab",                          # current_tab
            "Disabled",                         # uov_method
            None,                               # uov_input_image
            [],                                 # outpaint_selections
            None,                               # inpaint_input_image
            "",                                 # inpaint_additional_prompt
            None,                               # inpaint_mask_image_upload
            False,                              # disable_preview
            False,                              # disable_intermediate_results
            False,                              # disable_seed_increment
            False,                              # black_out_nsfw
            1.5,                                # adm_scaler_positive
            0.8,                                # adm_scaler_negative
            0.3,                                # adm_scaler_end
            7.0,                                # adaptive_cfg
            1,                                  # clip_skip
            "dpmpp_2m_sde_gpu",                # sampler_name
            "karras",                           # scheduler_name
            "Default (model)",                  # vae_name
            -1,                                 # overwrite_step
            -1,                                 # overwrite_switch
            -1,                                 # overwrite_width
            -1,                                 # overwrite_height
            -1,                                 # overwrite_vary_strength
            -1,                                 # overwrite_upscale_strength
            False,                              # mixing_image_prompt_and_vary_upscale
            False,                              # mixing_image_prompt_and_inpaint
            False,                              # debugging_cn_preprocessor
            False,                              # skipping_cn_preprocessor
            64,                                 # canny_low_threshold
            128,                                # canny_high_threshold
            "joint",                            # refiner_swap_method
            0.25,                               # controlnet_softness
            False,                              # freeu_enabled
            1.01,                               # freeu_b1
            1.02,                               # freeu_b2
            0.99,                               # freeu_s1
            0.95,                               # freeu_s2
            False,                              # debugging_inpaint_preprocessor
            False,                              # inpaint_disable_initial_latent
            "v2.6",                             # inpaint_engine
            1.0,                                # inpaint_strength
            0.618,                              # inpaint_respective_field
            False,                              # inpaint_advanced_masking_checkbox
            False,                              # invert_mask_checkbox
            0,                                  # inpaint_erode_or_dilate
            False,                              # save_final_enhanced_image_only
            True,                               # save_metadata_to_images
            "fooocus",                          # metadata_scheme - This should fix the error!
        ]
        
        args.extend(remaining_args)
        
        # Add ControlNet arguments (4 × 4 = 16 args)
        for _ in range(4):
            args.extend([None, 1.0, 1.0, "ImagePrompt"])  # img, stop, weight, type
        
        # Add remaining arguments
        args.extend([
            False,                              # debugging_dino
            0,                                  # dino_erode_or_dilate
            False,                              # debugging_enhance_masks_checkbox
            None,                               # enhance_input_image
            False,                              # enhance_checkbox
            "Disabled",                         # enhance_uov_method
            "Before First Enhancement",         # enhance_uov_processing_order
            "Original Prompts",                 # enhance_uov_prompt_type
        ])
        
        # Add enhance controls (2 tabs × 16 args each = 32 args)
        for _ in range(2):
            args.extend([
                False,                          # enhance_enabled
                "",                             # enhance_mask_dino_prompt_text
                "",                             # enhance_prompt
                "",                             # enhance_negative_prompt
                "u2net",                        # enhance_mask_model
                "full",                         # enhance_mask_cloth_category
                "vit_b",                        # enhance_mask_sam_model
                0.25,                           # enhance_mask_text_threshold
                0.3,                            # enhance_mask_box_threshold
                0,                              # enhance_mask_sam_max_detections
                False,                          # enhance_inpaint_disable_initial_latent
                "v2.6",                         # enhance_inpaint_engine
                1.0,                            # enhance_inpaint_strength
                0.0,                            # enhance_inpaint_respective_field
                0,                              # enhance_inpaint_erode_or_dilate
                False,                          # enhance_mask_invert
            ])
        
        # Add more arguments to reach exactly 153
        # We need 16 more arguments (153 - 137 = 16)
        additional_args = [
            False,                              # Additional parameter 1
            False,                              # Additional parameter 2
            False,                              # Additional parameter 3
            False,                              # Additional parameter 4
            False,                              # Additional parameter 5
            False,                              # Additional parameter 6
            False,                              # Additional parameter 7
            False,                              # Additional parameter 8
            False,                              # Additional parameter 9
            False,                              # Additional parameter 10
            False,                              # Additional parameter 11
            False,                              # Additional parameter 12
            False,                              # Additional parameter 13
            False,                              # Additional parameter 14
            False,                              # Additional parameter 15
            False,                              # Additional parameter 16
        ]
        
        args.extend(additional_args)
        
        print(f"Submitting with {len(args)} arguments to function index {fn_index}")
        print(f"Metadata scheme at position {len(args)-67}: {args[-67] if len(args) >= 67 else 'N/A'}")
        
        job = client.submit(*args, fn_index=fn_index)
        result = job.result()
        
        if result is not None:
            print(f"SUCCESS! Function index {fn_index} worked!")
            print(f"Result type: {type(result)}")
            print(f"Result: {result}")
            
            if isinstance(result, tuple) and len(result) > 0:
                gallery_output = result[-1]
                if isinstance(gallery_output, dict) and "files" in gallery_output:
                    files = gallery_output["files"]
                    if files and isinstance(files[0], dict) and "path" in files[0]:
                        image_path = files[0]["path"]
                        if image_path and os.path.exists(image_path):
                            print(f"SUCCESS! Image generated at: {image_path}")
                            return FileResponse(image_path, media_type="image/png", filename="generated_image.png")
            
            # If we get here, the function worked but we couldn't parse the result
            return {
                "status": "success",
                "message": f"Function index {fn_index} worked but couldn't parse result",
                "result_type": str(type(result)),
                "working_fn_index": fn_index,
                "result_repr": repr(result),
                "result_str": str(result)
            }
        else:
            raise HTTPException(status_code=500, detail="Job completed but returned None")

    except Exception as e:
        print("--- DETAILED ERROR ---")
        traceback.print_exc()
        print("----------------------")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")

# Simple endpoint that mimics the normal web interface
@app.post("/generate_simple_web")
async def generate_image_simple_web(request: ImageRequest):
    print(f"Simple web-style generation for prompt: '{request.prompt}'")
    try:
        client = Client(FOOOCUS_URL)
        
        # Use function index 67 (the main generation function)
        fn_index = 67
        
        # Create a minimal argument list with just the essential parameters
        # This mimics what you would set in the normal web interface
        args = [
            False,                              # generate_image_grid
            request.prompt,                     # prompt
            request.negative_prompt,            # negative_prompt
            [request.style],                    # style_selections
            request.performance,                # performance_selection
            request.aspect_ratio,               # aspect_ratios_selection
            request.image_number,               # image_number
            request.output_format,              # output_format
            request.seed,                       # image_seed
            True,                               # read_wildcards_in_order
            request.sharpness,                  # sharpness
            request.guidance_scale,             # guidance_scale
            request.base_model,                 # base_model
            "None",                             # refiner_model
            0.8,                                # refiner_switch
        ]
        
        # Fill the rest with None to reach the required argument count
        # We'll try different counts to find the right one
        for target_count in [150, 151, 152, 153, 154, 155]:
            test_args = args.copy()
            while len(test_args) < target_count:
                test_args.append(None)
            
            # Set metadata_scheme to fooocus at position -67
            if len(test_args) >= 67:
                test_args[-67] = "fooocus"
            
            print(f"Trying with {len(test_args)} arguments...")
            
            try:
                job = client.submit(*test_args, fn_index=fn_index)
                result = job.result()
                
                if result is not None and len(result) > 0:
                    print(f"SUCCESS! Found working configuration with {len(test_args)} arguments")
                    print(f"Result type: {type(result)}")
                    print(f"Result: {result}")
                    
                    # Try to extract the image path
                    if isinstance(result, tuple) and len(result) > 0:
                        gallery_output = result[-1]
                        if isinstance(gallery_output, dict) and "files" in gallery_output:
                            files = gallery_output["files"]
                            if files and isinstance(files[0], dict) and "path" in files[0]:
                                image_path = files[0]["path"]
                                if image_path and os.path.exists(image_path):
                                    print(f"SUCCESS! Image generated at: {image_path}")
                                    return FileResponse(image_path, media_type="image/png", filename="generated_image.png")
                    
                    # If we get here, the function worked but we couldn't parse the result
                    return {
                        "status": "success",
                        "message": f"Found working configuration with {len(test_args)} arguments",
                        "result_type": str(type(result)),
                        "working_arg_count": len(test_args),
                        "result_repr": repr(result),
                        "result_str": str(result)
                    }
                else:
                    print(f"Result was None with {len(test_args)} arguments")
                    
            except Exception as e:
                print(f"Error with {len(test_args)} arguments: {e}")
                # If it's an argument count error, continue to next count
                if "Expected" in str(e) and "arguments" in str(e):
                    continue
                # If it's a different error, it might be working but with an issue
                else:
                    print(f"Non-argument error with {len(test_args)} arguments: {e}")
                    continue
        
        raise HTTPException(status_code=500, detail="No working configuration found. Tried argument counts: 150, 151, 152, 153, 154, 155")

    except Exception as e:
        print("--- DETAILED ERROR ---")
        traceback.print_exc()
        print("----------------------")
        raise HTTPException(status_code=500, detail=f"An error occurred: {e}")```

i am always getting some error that the arguments dont match or that s\async_worker.py", line 48, in <listcomp>
    self.loras = get_enabled_loras([(bool(args.pop()), str(args.pop()), float(args.pop())) for _ in
TypeError: float() argument must be a string or a real number, not 'NoneType'

help would be appreciated thanks