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Product-Shot-Generationns/.gitattributes

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Product-Shot-Generationns/README.md

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+---
+title: Product Shot Generation
+emoji: 📉
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.40.0
+app_file: app.py
+pinned: false
+license: other
+---
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Product-Shot-Generationns/app.py

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+import gradio as gr
+import numpy as np
+
+import os
+from PIL import Image
+import requests
+from io import BytesIO
+import io
+import base64
+
+hf_token = os.environ.get("HF_TOKEN_API_DEMO") # we get it from a secret env variable, such that it's private
+auth_headers = {"api_token": hf_token}
+
+def convert_image_to_base64_string(mask_image):
+    buffer = io.BytesIO()
+    mask_image.save(buffer, format="PNG")  # You can choose the format (e.g., "JPEG", "PNG")
+    # Encode the buffer in base64
+    image_base64_string = base64.b64encode(buffer.getvalue()).decode('utf-8')
+    return f",{image_base64_string}" # for some reason the funciton which downloads image from base64 expects prefix of "," which is redundant in the url
+
+def download_image(url):
+    response = requests.get(url)
+    img_bytes = BytesIO(response.content)
+    return Image.open(img_bytes).convert("RGB")
+
+def lifestyle_shot_by_text_api_call(image_base64_file, prompt):
+
+    url = "http://engine.prod.bria-api.com/v1/product/lifestyle_shot_by_text"
+    
+    payload = {
+    "file": image_base64_file,
+    "scene_description": prompt,
+    "num_results": 1,
+    "sync": True,
+    "original_quality": True,
+    "optimize_description": True,
+    }
+    response = requests.post(url, json=payload, headers=auth_headers)
+    response = response.json()
+    res_image = download_image(response['result'][0][0])
+    
+    return res_image
+
+
+def predict_ref_by_text(input_image, prompt):
+
+    # init_image = Image.fromarray(dict['background'][:, :, :3], 'RGB') #dict['background'].convert("RGB")#.resize((1024, 1024))
+    # mask = Image.fromarray(dict['layers'][0][:,:,3], 'L') #dict['layers'].convert("RGB")#.resize((1024, 1024))
+    
+    image_base64_file = convert_image_to_base64_string(input_image)
+    
+    gen_img = lifestyle_shot_by_text_api_call(image_base64_file, prompt)
+    
+    return gen_img
+
+
+def lifestyle_shot_by_image_api_call(image_base64_file, ref_image_base64_file):
+
+    url = "http://engine.prod.bria-api.com/v1/product/lifestyle_shot_by_image"
+    
+    payload = {
+    "file": image_base64_file,
+    "ref_image_file": ref_image_base64_file,
+    "num_results": 1,
+    "sync": True,
+    "original_quality": True,
+    "optimize_description": True,
+    }
+    response = requests.post(url, json=payload, headers=auth_headers)
+    response = response.json()
+    res_image = download_image(response['result'][0][0])
+    
+    return res_image
+
+
+def predict_ref_by_image(init_image, ref_image):
+    
+    image_base64_file = convert_image_to_base64_string(init_image)
+    ref_base64_file = convert_image_to_base64_string(ref_image)
+    
+    gen_img = lifestyle_shot_by_image_api_call(image_base64_file, ref_base64_file)
+    
+    return gen_img
+
+def on_change_prompt(img: Image.Image | None, prompt: str | None):
+    return gr.update(interactive=bool(img and prompt))
+
+css = '''
+.gradio-container{max-width: 1100px !important}
+#image_upload{min-height:400px}
+#image_upload [data-testid="image"], #image_upload [data-testid="image"] > div{min-height: 400px}
+#mask_radio .gr-form{background:transparent; border: none}
+#word_mask{margin-top: .75em !important}
+#word_mask textarea:disabled{opacity: 0.3}
+.footer {margin-bottom: 45px;margin-top: 35px;text-align: center;border-bottom: 1px solid #e5e5e5}
+.footer>p {font-size: .8rem; display: inline-block; padding: 0 10px;transform: translateY(10px);background: white}
+.dark .footer {border-color: #303030}
+.dark .footer>p {background: #0b0f19}
+.acknowledgments h4{margin: 1.25em 0 .25em 0;font-weight: bold;font-size: 115%}
+#image_upload .touch-none{display: flex}
+@keyframes spin {
+    from {
+        transform: rotate(0deg);
+    }
+    to {
+        transform: rotate(360deg);
+    }
+}
+#share-btn-container {padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; max-width: 13rem; margin-left: auto;}
+div#share-btn-container > div {flex-direction: row;background: black;align-items: center}
+#share-btn-container:hover {background-color: #060606}
+#share-btn {all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.5rem !important; padding-bottom: 0.5rem !important;right:0;}
+#share-btn * {all: unset}
+#share-btn-container div:nth-child(-n+2){width: auto !important;min-height: 0px !important;}
+#share-btn-container .wrap {display: none !important}
+#share-btn-container.hidden {display: none!important}
+#prompt input{width: calc(100% - 160px);border-top-right-radius: 0px;border-bottom-right-radius: 0px;}
+#run_button {
+    width: 100%;
+    height: 50px;  /* Set a fixed height for the button */
+    display: flex;
+    align-items: center;
+    justify-content: center;
+}
+#output-img img, #image_upload img {
+    object-fit: contain; /* Ensure aspect ratio is preserved */
+    width: 100%;
+    height: auto; /* Let height adjust automatically */
+}
+#prompt-container{margin-top:-18px;}
+#prompt-container .form{border-top-left-radius: 0;border-top-right-radius: 0}
+#image_upload{border-bottom-left-radius: 0px;border-bottom-right-radius: 0px}
+'''
+
+image_blocks = gr.Blocks(css=css, elem_id="total-container")
+with image_blocks as demo:
+    # with gr.Column(elem_id="col-container"):
+    gr.Markdown("## Product Shot Generation")
+    gr.HTML('''
+          <p style="margin-bottom: 10px; font-size: 94%">
+            This demo showcases the <strong>Lifestyle Product Shot by Text</strong> and <strong>Lifestyle Product Shot by Image</strong> feature, enabling users to generate product backgrounds effortlessly.<br>
+            With <strong>Lifestyle Product Shot by Text</strong>, users can create backgrounds using descriptive textual prompts, 
+            while <strong>Lifestyle Product Shot by Image</strong> allows backgrounds to be generated based on a reference image for inspiration.<br>
+            The pipeline comprises multiple components, including <a href="https://huggingface.co/briaai/BRIA-2.3" target="_blank">briaai/BRIA-2.3</a>, 
+            <a href="https://huggingface.co/briaai/RMBG-2.0" target="_blank">briaai/RMBG-2.0</a>, <a href="https://huggingface.co/briaai/BRIA-2.3-ControlNet-BG-Gen" target="_blank">briaai/BRIA-2.3-ControlNet-BG-Gen</a> and 
+            <a href="https://huggingface.co/briaai/Image-Prompt" target="_blank">briaai/Image-Prompt</a>, all trained on licensed data.<br>
+            This ensures full legal liability coverage for copyright and privacy infringement.<br>
+            Notes:<br>
+            - High-resolution images may take longer to process.<br>
+            - For best results in reference by image: make sure the foreground in the image is already located in the wanted position and scale, relative to the elements in the reference image.<br>
+          </p>
+        ''')
+    with gr.Tab(label="By scene description", id="tab_prompt"):
+
+        with gr.Row():
+            with gr.Column():
+                # image = gr.ImageEditor(sources=["upload"], layers=False, transforms=[], 
+                                    # brush=gr.Brush(colors=["#000000"], color_mode="fixed"),
+                                    # )
+                image = gr.Image(type="pil", label="Input")
+                prompt = gr.Textbox(label="scene description", placeholder="Enter your scene description here...")
+                with gr.Row(elem_id="prompt-container", equal_height=True):
+                    with gr.Column():
+                        btn = gr.Button("Generate Product Shot!", elem_id="run_button")
+            
+            with gr.Column():
+                image_out = gr.Image(label="Output", elem_id="output-img")
+
+        # Button click will trigger the inpainting function (now with prompt included)
+        for inp in [image, prompt]:
+            inp.change(
+                fn=on_change_prompt,
+                inputs=[image, prompt],
+                outputs=[btn],
+            )
+        btn.click(fn=predict_ref_by_text, inputs=[image, prompt], outputs=[image_out], api_name='run')
+    
+    with gr.Tab(label="By reference image", id="tab_ref_image"):
+
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="pil", label="Input")
+                ref_image = gr.Image(type="pil", label="Reference Image")
+                with gr.Row(elem_id="prompt-container", equal_height=True):
+                    with gr.Column():
+                        btn = gr.Button("Generate Product Shot!", elem_id="run_button")
+            
+            with gr.Column():
+                image_out = gr.Image(label="Output", elem_id="output-img")
+
+        # Button click will trigger the inpainting function (now with prompt included)
+        btn.click(fn=predict_ref_by_image, inputs=[image, ref_image], outputs=[image_out], api_name='run')
+
+    gr.HTML(
+        """
+            <div class="footer">
+                <p>Model by <a href="https://huggingface.co/diffusers" style="text-decoration: underline;" target="_blank">Diffusers</a> - Gradio Demo by 🤗 Hugging Face
+                </p>
+            </div>
+        """
+    )
+
+image_blocks.queue(max_size=25, api_open=False).launch(show_api=False)

Product-Shot-Generationns/requirements.txt

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+pillow==10.3.0