test

main-copyy.py

@@ -0,0 +1,64 @@
+import os
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+from diffusers import AutoPipelineForImage2Image
+from diffusers.utils import load_image, make_image_grid
+from PIL import Image
+import torch
+import io
+
+# Set environment variable to avoid fragmentation
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+
+# Clear any unused GPU memory
+torch.cuda.empty_cache()
+
+app = Flask(__name__)
+CORS(app)
+
+# Load the image-to-image pipeline from Hugging Face
+pipe = AutoPipelineForImage2Image.from_pretrained("RunDiffusion/Juggernaut-X-v10", torch_dtype=torch.float16).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_vae_tiling()  # Improve performance on large images
+pipe.enable_vae_slicing()  # Improve performance on large batches
+
+@app.route('/')
+def hello():
+    return {"Goes Wrong": "Keeping it real"}
+
+@app.route('/generate', methods=['POST'])
+def generate():
+    if 'image' not in request.files:
+        return jsonify({"error": "No image file provided"}), 400
+
+    image_file = request.files['image']
+    prompt = request.form.get('prompt', 'fleece hoodie, front zip, abstract pattern, GAP logo, high quality, photo')
+    negative_prompt = request.form.get('negative_prompt', 'low quality, bad quality, sketches, hanger')
+    guidance_scale = float(request.form.get('guidance_scale', 7))
+    num_images = int(request.form.get('num_images', 2))
+
+    sketch = Image.open(image_file)
+    
+    with torch.inference_mode():
+        images = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=sketch,
+            num_inference_steps=35,
+            guidance_scale=guidance_scale,
+            strength=0.5,
+            generator=torch.manual_seed(69),
+            num_images_per_prompt=num_images,
+        ).images
+
+    grid = make_image_grid(images, rows=1, cols=num_images)
+    
+    # Save the generated grid to a BytesIO object
+    img_byte_arr = io.BytesIO()
+    grid.save(img_byte_arr, format='PNG')
+    img_byte_arr.seek(0)
+
+    return send_file(img_byte_arr, mimetype='image/png')
+
+if __name__ == '__main__':
+    app.run(debug=True)

main.py

@@ -1,10 +1,11 @@
+from diffusers import AutoPipelineForImage2Image
+import torch
 import os
+import numpy as np
+from PIL import Image
+from diffusers.utils import load_image, make_image_grid
 from flask import Flask, request, jsonify, send_file
 from flask_cors import CORS
-from diffusers import AutoPipelineForImage2Image
-from diffusers.utils import load_image, make_image_grid
-from PIL import Image
-import torch
 import io
 
 # Set environment variable to avoid fragmentation
@@ -16,29 +17,40 @@ torch.cuda.empty_cache()
 app = Flask(__name__)
 CORS(app)
 
+print('loading models...')
 # Load the image-to-image pipeline from Hugging Face
 pipe = AutoPipelineForImage2Image.from_pretrained("RunDiffusion/Juggernaut-X-v10", torch_dtype=torch.float16).to("cuda")
 pipe.enable_xformers_memory_efficient_attention()
 pipe.enable_vae_tiling()  # Improve performance on large images
 pipe.enable_vae_slicing()  # Improve performance on large batches
+print('loaded models...')
 
 @app.route('/')
 def hello():
     return {"Goes Wrong": "Keeping it real"}
 
-@app.route('/generate', methods=['POST'])
-def generate():
-    if 'image' not in request.files:
-        return jsonify({"error": "No image file provided"}), 400
+@app.route('/run_inference', methods=['POST'])
+def run_inference():
+    data = request.get_json()
+
+    if 'url' not in data:
+        return jsonify({"error": "No imageurl provided"}), 400
 
-    image_file = request.files['image']
-    prompt = request.form.get('prompt', 'fleece hoodie, front zip, abstract pattern, GAP logo, high quality, photo')
-    negative_prompt = request.form.get('negative_prompt', 'low quality, bad quality, sketches, hanger')
-    guidance_scale = float(request.form.get('guidance_scale', 7))
-    num_images = int(request.form.get('num_images', 2))
+    # base64_image = data['base64_image']
+    prompt = data.get('prompt', 'fleece hoodie, front zip, abstract pattern, GAP logo, high quality, photo')
+    negative_prompt = data.get('negative_prompt', 'low quality, bad quality, sketches, hanger')
+    guidance_scale = float(data.get('guidance_scale', 7))
+    num_images = int(data.get('num_images', 2))
+
+    url =  data.get('url', 'https://storage.googleapis.com/sketch-bucket/dresstest2.PNG')
+    sketch = load_image(url)
+    print(f'Loaded image URL: {url}')
+
+    # testing
+    # prompt = "long waist dress, puffed sleeves, fringes on sleeve and hem, high quality, photo"
+    # negative_prompt = "low quality, bad quality, sketches, hanger"
+    # guidance_scale = 7
 
-    sketch = Image.open(image_file)
-    
     with torch.inference_mode():
         images = pipe(
             prompt=prompt,
@@ -52,7 +64,8 @@ def generate():
         ).images
 
     grid = make_image_grid(images, rows=1, cols=num_images)
-    
+    # images[0].save('output.png')
+
     # Save the generated grid to a BytesIO object
     img_byte_arr = io.BytesIO()
     grid.save(img_byte_arr, format='PNG')

realify2.py

@@ -0,0 +1,70 @@
+import os
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+from diffusers import AutoPipelineForImage2Image
+from diffusers.utils import make_image_grid
+from PIL import Image
+import torch
+import io
+import base64
+
+# Set environment variable to avoid fragmentation
+os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
+
+# Clear any unused GPU memory
+torch.cuda.empty_cache()
+
+app = Flask(__name__)
+CORS(app)
+
+# Load the image-to-image pipeline from Hugging Face
+pipe = AutoPipelineForImage2Image.from_pretrained("RunDiffusion/Juggernaut-X-v10", torch_dtype=torch.float16).to("cuda")
+pipe.enable_xformers_memory_efficient_attention()
+pipe.enable_vae_tiling()  # Improve performance on large images
+pipe.enable_vae_slicing()  # Improve performance on large batches
+print('loaded models...')
+
+@app.route('/')
+def hello():
+    return {"Goes Wrong": "Keeping it real"}
+
+@app.route('/run_inference', methods=['POST'])
+def run_inference():
+    data = request.get_json()
+
+    if 'base64_image' not in data:
+        return jsonify({"error": "No base64 image data provided"}), 400
+
+    base64_image = data['base64_image']
+    prompt = data.get('prompt', 'fleece hoodie, front zip, abstract pattern, GAP logo, high quality, photo')
+    negative_prompt = data.get('negative_prompt', 'low quality, bad quality, sketches, hanger')
+    guidance_scale = float(data.get('guidance_scale', 7))
+    num_images = int(data.get('num_images', 2))
+
+    # Decode the base64 image
+    image_data = base64.b64decode(base64_image)
+    sketch = Image.open(io.BytesIO(image_data))
+
+    with torch.inference_mode():
+        images = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=sketch,
+            num_inference_steps=35,
+            guidance_scale=guidance_scale,
+            strength=0.5,
+            generator=torch.manual_seed(69),
+            num_images_per_prompt=num_images,
+        ).images
+
+    grid = make_image_grid(images, rows=1, cols=num_images)
+    
+    # Save the generated grid to a BytesIO object
+    img_byte_arr = io.BytesIO()
+    grid.save(img_byte_arr, format='PNG')
+    img_byte_arr.seek(0)
+
+    return send_file(img_byte_arr, mimetype='image/png')
+
+if __name__ == '__main__':
+    app.run(debug=True)