Update app.py

app.py

@@ -1,3 +1,266 @@
+# import logging
+# import random
+# import warnings
+# import gradio as gr
+# import os
+# import shutil
+# import subprocess
+# import spaces
+# import torch
+# import numpy as np
+# from diffusers import FluxControlNetModel
+# from diffusers.pipelines import FluxControlNetPipeline
+# from PIL import Image
+# from huggingface_hub import snapshot_download, login
+# import io
+# import base64
+# from flask import Flask, request, jsonify
+# from concurrent.futures import ThreadPoolExecutor
+# from flask_cors import CORS
+# import threading
+
+# # Configure logging
+# logging.basicConfig(level=logging.INFO)
+# logger = logging.getLogger(__name__)
+
+# app = Flask(__name__)
+# CORS(app)
+
+# # Function to check disk usage
+# def check_disk_space():
+#     result = subprocess.run(['df', '-h'], capture_output=True, text=True)
+#     logger.info("Disk space usage:\n%s", result.stdout)
+
+# # Function to clear Hugging Face cache
+# def clear_huggingface_cache():
+#     cache_dir = os.path.expanduser('~/.cache/huggingface')
+#     if os.path.exists(cache_dir):
+#         shutil.rmtree(cache_dir)  # Removes the entire cache directory
+#         logger.info("Cleared Hugging Face cache at: %s", cache_dir)
+#     else:
+#         logger.info("No Hugging Face cache found.")
+
+# # Check disk space
+# check_disk_space()
+
+# # Clear Hugging Face cache
+# clear_huggingface_cache()
+
+# # Add config to store base64 images
+# app.config['image_outputs'] = {}
+
+# # ThreadPoolExecutor for managing image processing threads
+# executor = ThreadPoolExecutor()
+
+# # Determine the device (GPU or CPU)
+# if torch.cuda.is_available():
+#     device = "cuda"
+#     logger.info("CUDA is available. Using GPU.")
+# else:
+#     device = "cpu"
+#     logger.info("CUDA is not available. Using CPU.")
+
+# # Load model from Huggingface Hub
+# huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+# if huggingface_token:
+#     login(token=huggingface_token)
+#     logger.info("Hugging Face token found and logged in.")
+# else:
+#     logger.warning("Hugging Face token not found in environment variables.")
+
+# logger.info("Hugging Face token: %s", huggingface_token)
+
+# # Download model using snapshot_download
+
+# model_path = snapshot_download(
+#         repo_id="black-forest-labs/FLUX.1-dev", 
+#         repo_type="model", 
+#         ignore_patterns=["*.md", "*..gitattributes"],
+#         local_dir="FLUX.1-dev",
+#         token=huggingface_token)
+# logger.info("Model downloaded to: %s", model_path)
+
+# # Load pipeline
+# logger.info('Loading ControlNet model.')
+
+# controlnet = FluxControlNetModel.from_pretrained(
+#         "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
+#     ).to(device)
+# logger.info("ControlNet model loaded successfully.")
+
+# logger.info('Loading pipeline.')
+
+# pipe = FluxControlNetPipeline.from_pretrained(
+#         model_path, controlnet=controlnet, torch_dtype=torch.bfloat16
+#     ).to(device)
+# logger.info("Pipeline loaded successfully.")
+
+# MAX_SEED = 1000000
+# MAX_PIXEL_BUDGET = 1024 * 1024
+
+
+# @spaces.GPU
+# def process_input(input_image, upscale_factor):
+#     w, h = input_image.size
+#     aspect_ratio = w / h
+#     was_resized = False
+
+#     # Resize if input size exceeds the maximum pixel budget
+#     if w * h * upscale_factor**2 > MAX_PIXEL_BUDGET:
+#         warnings.warn(f"Requested output image is too large. Resizing to fit within pixel budget.")
+#         input_image = input_image.resize(
+#             (
+#                 int(aspect_ratio * MAX_PIXEL_BUDGET**0.5 // upscale_factor),
+#                 int(MAX_PIXEL_BUDGET**0.5 // aspect_ratio // upscale_factor),
+#             )
+#         )
+#         was_resized = True
+
+#     # Adjust dimensions to be a multiple of 8
+#     w, h = input_image.size
+#     w = w - w % 8
+#     h = h - h % 8
+
+#     return input_image.resize((w, h)), was_resized
+
+# @spaces.GPU
+# def run_inference(process_id, input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale):
+#     logger.info("Processing inference for process_id: %s", process_id)
+#     input_image, was_resized = process_input(input_image, upscale_factor)
+
+#     # Rescale image for ControlNet processing
+#     w, h = input_image.size
+#     control_image = input_image.resize((w * upscale_factor, h * upscale_factor))
+
+#     # Set the random generator for inference
+#     generator = torch.Generator().manual_seed(seed)
+
+#     # Perform inference using the pipeline
+#     logger.info("Running pipeline for process_id: %s", process_id)
+#     image = pipe(
+#         prompt="",
+#         control_image=control_image,
+#         controlnet_conditioning_scale=controlnet_conditioning_scale,
+#         num_inference_steps=num_inference_steps,
+#         guidance_scale=3.5,
+#         height=control_image.size[1],
+#         width=control_image.size[0],
+#         generator=generator,
+#     ).images[0]
+
+#     # Resize output image back to the original dimensions if needed
+#     if was_resized:
+#         original_size = (input_image.width * upscale_factor, input_image.height * upscale_factor)
+#         image = image.resize(original_size)
+
+#     # Convert the output image to base64
+#     buffered = io.BytesIO()
+#     image.save(buffered, format="JPEG")
+#     image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+#     # Store the result in the shared dictionary
+#     app.config['image_outputs'][process_id] = image_base64
+#     logger.info("Inference completed for process_id: %s", process_id)
+
+# @app.route('/infer', methods=['POST'])
+# def infer():
+#     # Check if the file was provided in the form-data
+#     if 'input_image' not in request.files:
+#         logger.error("No image file provided in request.")
+#         return jsonify({
+#             "status": "error",
+#             "message": "No input_image file provided"
+#         }), 400
+
+#     # Get the uploaded image file from the request
+#     file = request.files['input_image']
+
+#     # Check if a file was uploaded
+#     if file.filename == '':
+#         logger.error("No selected file in form-data.")
+#         return jsonify({
+#             "status": "error",
+#             "message": "No selected file"
+#         }), 400
+
+#     # Convert the image to Base64 for internal processing
+#     input_image = Image.open(file)
+#     buffered = io.BytesIO()
+#     input_image.save(buffered, format="JPEG")
+
+#     # Retrieve additional parameters from the request (if any)
+#     seed = request.form.get("seed", 42, type=int)
+#     randomize_seed = request.form.get("randomize_seed", 'true').lower() == 'true'
+#     num_inference_steps = request.form.get("num_inference_steps", 28, type=int)
+#     upscale_factor = request.form.get("upscale_factor", 4, type=int)
+#     controlnet_conditioning_scale = request.form.get("controlnet_conditioning_scale", 0.6, type=float)
+
+#     # Randomize seed if specified
+#     if randomize_seed:
+#         seed = random.randint(0, MAX_SEED)
+#         logger.info("Seed randomized to: %d", seed)
+
+#     # Create a unique process ID for this request
+#     process_id = str(random.randint(1000, 9999))
+#     logger.info("Process started with process_id: %s", process_id)
+
+#     # Set the status to 'in_progress'
+#     app.config['image_outputs'][process_id] = None
+
+#     # Run the inference in a separate thread
+#     executor.submit(run_inference, process_id, input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)
+
+#     # Return the process ID
+#     return jsonify({
+#         "process_id": process_id,
+#         "message": "Processing started"
+#     })
+
+
+# # Modify status endpoint to receive process_id in request body
+# @app.route('/status', methods=['GET'])
+# def status():
+#     # Get the process_id from the query parameters
+#     process_id = request.args.get('process_id')
+
+#     # Check if process_id was provided
+#     if not process_id:
+#         logger.error("Process ID not provided in request.")
+#         return jsonify({
+#             "status": "error",
+#             "message": "Process ID is required"
+#         }), 400
+
+#     # Check if the process_id exists in the dictionary
+#     if process_id not in app.config['image_outputs']:
+#         logger.error("Invalid process ID: %s", process_id)
+#         return jsonify({
+#             "status": "error",
+#             "message": "Invalid process ID"
+#         }), 404
+
+#     # Check the status of the image processing
+#     image_base64 = app.config['image_outputs'][process_id]
+#     if image_base64 is None:
+#         logger.info("Process ID %s is still in progress.", process_id)
+#         return jsonify({
+#             "status": "in_progress"
+#         })
+#     else:
+#         logger.info("Process ID %s completed successfully.", process_id)
+#         return jsonify({
+#             "status": "completed",
+#             "output_image": image_base64
+#         })
+
+
+# if __name__ == '__main__':
+#     app.run(debug=True,host='0.0.0.0')
+
+
+
+
+
 import logging
 import random
 import warnings
@@ -5,7 +268,6 @@ import gradio as gr
 import os
 import shutil
 import subprocess
-import spaces
 import torch
 import numpy as np
 from diffusers import FluxControlNetModel
@@ -14,18 +276,12 @@ from PIL import Image
 from huggingface_hub import snapshot_download, login
 import io
 import base64
-from flask import Flask, request, jsonify
-from concurrent.futures import ThreadPoolExecutor
-from flask_cors import CORS
 import threading
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-app = Flask(__name__)
-CORS(app)
-
 # Function to check disk usage
 def check_disk_space():
     result = subprocess.run(['df', '-h'], capture_output=True, text=True)
@@ -47,10 +303,7 @@ check_disk_space()
 clear_huggingface_cache()
 
 # Add config to store base64 images
-app.config['image_outputs'] = {}
-
-# ThreadPoolExecutor for managing image processing threads
-executor = ThreadPoolExecutor()
+image_outputs = {}
 
 # Determine the device (GPU or CPU)
 if torch.cuda.is_available():
@@ -68,38 +321,32 @@ if huggingface_token:
 else:
     logger.warning("Hugging Face token not found in environment variables.")
 
-logger.info("Hugging Face token: %s", huggingface_token)
-
 # Download model using snapshot_download
-
 model_path = snapshot_download(
-        repo_id="black-forest-labs/FLUX.1-dev", 
-        repo_type="model", 
-        ignore_patterns=["*.md", "*..gitattributes"],
-        local_dir="FLUX.1-dev",
-        token=huggingface_token)
+    repo_id="black-forest-labs/FLUX.1-dev", 
+    repo_type="model", 
+    ignore_patterns=["*.md", "*..gitattributes"],
+    local_dir="FLUX.1-dev",
+    token=huggingface_token
+)
 logger.info("Model downloaded to: %s", model_path)
 
 # Load pipeline
 logger.info('Loading ControlNet model.')
-
 controlnet = FluxControlNetModel.from_pretrained(
-        "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
-    ).to(device)
+    "jasperai/Flux.1-dev-Controlnet-Upscaler", torch_dtype=torch.bfloat16
+).to(device)
 logger.info("ControlNet model loaded successfully.")
 
 logger.info('Loading pipeline.')
-
 pipe = FluxControlNetPipeline.from_pretrained(
-        model_path, controlnet=controlnet, torch_dtype=torch.bfloat16
-    ).to(device)
+    model_path, controlnet=controlnet, torch_dtype=torch.bfloat16
+).to(device)
 logger.info("Pipeline loaded successfully.")
 
 MAX_SEED = 1000000
 MAX_PIXEL_BUDGET = 1024 * 1024
 
-
-@spaces.GPU
 def process_input(input_image, upscale_factor):
     w, h = input_image.size
     aspect_ratio = w / h
@@ -123,9 +370,8 @@ def process_input(input_image, upscale_factor):
 
     return input_image.resize((w, h)), was_resized
 
-@spaces.GPU
-def run_inference(process_id, input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale):
-    logger.info("Processing inference for process_id: %s", process_id)
+def run_inference(input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale):
+    logger.info("Running inference")
     input_image, was_resized = process_input(input_image, upscale_factor)
 
     # Rescale image for ControlNet processing
@@ -136,7 +382,7 @@ def run_inference(process_id, input_image, upscale_factor, seed, num_inference_s
     generator = torch.Generator().manual_seed(seed)
 
     # Perform inference using the pipeline
-    logger.info("Running pipeline for process_id: %s", process_id)
+    logger.info("Running pipeline")
     image = pipe(
         prompt="",
         control_image=control_image,
@@ -158,176 +404,38 @@ def run_inference(process_id, input_image, upscale_factor, seed, num_inference_s
     image.save(buffered, format="JPEG")
     image_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
 
-    # Store the result in the shared dictionary
-    app.config['image_outputs'][process_id] = image_base64
-    logger.info("Inference completed for process_id: %s", process_id)
-
-@app.route('/infer', methods=['POST'])
-def infer():
-    # Check if the file was provided in the form-data
-    if 'input_image' not in request.files:
-        logger.error("No image file provided in request.")
-        return jsonify({
-            "status": "error",
-            "message": "No input_image file provided"
-        }), 400
-
-    # Get the uploaded image file from the request
-    file = request.files['input_image']
-
-    # Check if a file was uploaded
-    if file.filename == '':
-        logger.error("No selected file in form-data.")
-        return jsonify({
-            "status": "error",
-            "message": "No selected file"
-        }), 400
-
-    # Convert the image to Base64 for internal processing
-    input_image = Image.open(file)
-    buffered = io.BytesIO()
-    input_image.save(buffered, format="JPEG")
-
-    # Retrieve additional parameters from the request (if any)
-    seed = request.form.get("seed", 42, type=int)
-    randomize_seed = request.form.get("randomize_seed", 'true').lower() == 'true'
-    num_inference_steps = request.form.get("num_inference_steps", 28, type=int)
-    upscale_factor = request.form.get("upscale_factor", 4, type=int)
-    controlnet_conditioning_scale = request.form.get("controlnet_conditioning_scale", 0.6, type=float)
+    logger.info("Inference completed")
+    return image_base64
 
-    # Randomize seed if specified
+# Define Gradio interface
+def gradio_interface(input_image, upscale_factor=4, seed=42, num_inference_steps=28, controlnet_conditioning_scale=0.6):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         logger.info("Seed randomized to: %d", seed)
 
-    # Create a unique process ID for this request
-    process_id = str(random.randint(1000, 9999))
-    logger.info("Process started with process_id: %s", process_id)
-
-    # Set the status to 'in_progress'
-    app.config['image_outputs'][process_id] = None
-
-    # Run the inference in a separate thread
-    executor.submit(run_inference, process_id, input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)
-
-    # Return the process ID
-    return jsonify({
-        "process_id": process_id,
-        "message": "Processing started"
-    })
-
-
-# Modify status endpoint to receive process_id in request body
-@app.route('/status', methods=['GET'])
-def status():
-    # Get the process_id from the query parameters
-    process_id = request.args.get('process_id')
-
-    # Check if process_id was provided
-    if not process_id:
-        logger.error("Process ID not provided in request.")
-        return jsonify({
-            "status": "error",
-            "message": "Process ID is required"
-        }), 400
-
-    # Check if the process_id exists in the dictionary
-    if process_id not in app.config['image_outputs']:
-        logger.error("Invalid process ID: %s", process_id)
-        return jsonify({
-            "status": "error",
-            "message": "Invalid process ID"
-        }), 404
-
-    # Check the status of the image processing
-    image_base64 = app.config['image_outputs'][process_id]
-    if image_base64 is None:
-        logger.info("Process ID %s is still in progress.", process_id)
-        return jsonify({
-            "status": "in_progress"
-        })
-    else:
-        logger.info("Process ID %s completed successfully.", process_id)
-        return jsonify({
-            "status": "completed",
-            "output_image": image_base64
-        })
-
-
-# Gradio Blocks setup
-css = """
-#col-container {
-    max-width: 800px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    gr.Markdown(
-        """
-        # ⚡ Flux.1-dev Upscaler ControlNet ⚡
-        This is an interactive demo of [Flux.1-dev Upscaler ControlNet](https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler) taking as input a low resolution image to generate a high resolution image.
-        Currently running on {device}.
-        *Note*: Even though the model can handle higher resolution images, due to GPU memory constraints, this demo was limited to a generated output not exceeding a pixel budget of 1024x1024. If the requested size exceeds that limit, the input will be first resized keeping the aspect ratio such that the output of the controlNet model does not exceed the allocated pixel budget. The output is then resized to the targeted shape using a simple resizing. This may explain some artifacts for high resolution input. To address this, run the demo locally or consider implementing a tiling strategy. Happy upscaling! 🚀
-        """.format(device=device)
-    )
-
-    with gr.Row():
-        run_button = gr.Button(value="Run")
-
-    with gr.Row():
-        with gr.Column(scale=4):
-            input_im = gr.Image(label="Input Image", type="pil")
-        with gr.Column(scale=1):
-            num_inference_steps = gr.Slider(
-                label="Number of Inference Steps",
-                minimum=8,
-                maximum=50,
-                step=1,
-                value=28,
-            )
-            upscale_factor = gr.Slider(
-                label="Upscale Factor",
-                minimum=1,
-                maximum=4,
-                step=1,
-                value=4,
-            )
-            controlnet_conditioning_scale = gr.Slider(
-                label="Controlnet Conditioning Scale",
-                minimum=0.1,
-                maximum=1.5,
-                step=0.1,
-                value=0.6,
-            )
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=42,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-    with gr.Row():
-        result = gr.Image(label="Output Image", type="pil", interactive=False)
-
-    gr.Button("Run").click(
-        fn=gr_infer,
-        inputs=[seed, randomize_seed, input_im, num_inference_steps, upscale_factor, controlnet_conditioning_scale],
-        outputs=result
-    )
-
-    gr.Markdown("**Disclaimer:**")
-    gr.Markdown(
-        "This demo is only for research purposes. Jasper cannot be held responsible for the generation of NSFW (Not Safe For Work) content through the use of this demo. Users are solely responsible for any content they create, and it is their obligation to ensure that it adheres to appropriate and ethical standards."
-    )
-
+    # Run inference
+    output_image_base64 = run_inference(input_image, upscale_factor, seed, num_inference_steps, controlnet_conditioning_scale)
+    
+    return Image.open(io.BytesIO(base64.b64decode(output_image_base64)))
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=gradio_interface,
+    inputs=[
+        gr.Image(type="pil", label="Input Image"),
+        gr.Slider(min=1, max=8, step=1, label="Upscale Factor"),
+        gr.Slider(min=0, max=MAX_SEED, step=1, label="Seed"),
+        gr.Slider(min=1, max=100, step=1, label="Inference Steps"),
+        gr.Slider(min=0.0, max=1.0, step=0.1, label="ControlNet Conditioning Scale")
+    ],
+    outputs=gr.Image(label="Output Image"),
+    title="ControlNet Image Upscaling",
+    description="Upload an image to upscale using the ControlNet model."
+)
+
+# Launch Gradio app
 if __name__ == '__main__':
-    demo.queue().launch(share=False, show_api=False)
-    app.run(host='0.0.0.0', port=5000)
+    iface.launch()
 
-# if __name__ == '__main__':
-#     app.run(debug=True,host='0.0.0.0')