Create app.py

app.py

@@ -0,0 +1,135 @@
+import gradio as gr
+from urllib.parse import urlparse
+import requests
+import time
+import os
+import spaces
+import torch
+
+zero = torch.Tensor([0]).cuda()
+print(zero.device)  # <-- 'cpu' 🤔
+
+names = ['prompt', 'negative_prompt', 'subject', 'number_of_outputs', 'number_of_images_per_pose', 'randomise_poses', 'output_format', 'output_quality', 'seed']
+
+@spaces.GPU
+def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
+    print(zero.device)  # <-- 'cuda:0' 🤗
+    headers = {'Content-Type': 'application/json'}
+
+    payload = {"input": {}}
+    
+    
+    base_url = "http://0.0.0.0:7860"
+    for i, key in enumerate(names):
+        value = args[i]
+        if value and (os.path.exists(str(value))):
+            value = f"{base_url}/file=" + value
+        if value is not None and value != "":
+            payload["input"][key] = value
+
+    response = requests.post("http://0.0.0.0:5000/predictions", headers=headers, json=payload)
+
+    
+    if response.status_code == 201:
+        follow_up_url = response.json()["urls"]["get"]
+        response = requests.get(follow_up_url, headers=headers)
+        while response.json()["status"] != "succeeded":
+            if response.json()["status"] == "failed":
+                raise gr.Error("The submission failed!")
+            response = requests.get(follow_up_url, headers=headers)
+            time.sleep(1)
+    if response.status_code == 200:
+        json_response = response.json()
+        #If the output component is JSON return the entire output response 
+        if(outputs[0].get_config()["name"] == "json"):
+            return json_response["output"]
+        predict_outputs = parse_outputs(json_response["output"])
+        processed_outputs = process_outputs(predict_outputs)        
+        return tuple(processed_outputs) if len(processed_outputs) > 1 else processed_outputs[0]
+    else:
+        if(response.status_code == 409):
+            raise gr.Error(f"Sorry, the Cog image is still processing. Try again in a bit.")
+        raise gr.Error(f"The submission failed! Error: {response.status_code}")
+
+title = "Demo for consistent-character cog image by fofr"
+description = "Create images of a given character in different poses • running cog image by fofr"
+
+css="""
+#col-container{
+    margin: 0 auto;
+    max-width: 1400px;
+    text-align: left;
+}
+"""
+with gr.Blocks(css=css) as app:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(f"""
+        <h2 style="text-align: center;">Consistent Character Workflow</h2>
+        <p style="text-align: center;">{description}</p>
+        """)
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                prompt = gr.Textbox(
+                    label="Prompt", info='''Describe the subject. Include clothes and hairstyle for more consistency.'''
+                )
+        
+                subject = gr.Image(
+                    label="Subject", type="filepath"
+                )
+
+                submit_btn = gr.Button("Submit")
+
+                with gr.Accordion(label="Advanced Settings", open=False):
+                    
+                    negative_prompt = gr.Textbox(
+                        label="Negative Prompt", info='''Things you do not want to see in your image''',
+                        value="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry"
+                    )
+
+                    with gr.Row():
+
+                        number_of_outputs = gr.Slider(
+                            label="Number Of Outputs", info='''The number of images to generate.''', value=2,
+                            minimum=1, maximum=4, step=1,
+                        )
+                        
+                        number_of_images_per_pose = gr.Slider(
+                            label="Number Of Images Per Pose", info='''The number of images to generate for each pose.''', value=1,
+                            minimum=1, maximum=4, step=1,
+                        )
+
+                    with gr.Row():
+                        
+                        randomise_poses = gr.Checkbox(
+                            label="Randomise Poses", info='''Randomise the poses used.''', value=True
+                        )
+                        
+                        output_format = gr.Dropdown(
+                            choices=['webp', 'jpg', 'png'], label="output_format", info='''Format of the output images''', value="webp"
+                        )
+                    
+                    with gr.Row():
+                        
+                        output_quality = gr.Number(
+                            label="Output Quality", info='''Quality of the output images, from 0 to 100. 100 is best quality, 0 is lowest quality.''', value=80
+                        )
+                        
+                        seed = gr.Number(
+                            label="Seed", info='''Set a seed for reproducibility. Random by default.''', value=None
+                        )
+
+            with gr.Column(scale=1.5):
+                consistent_results = gr.Gallery(label="Consistent Results")
+
+    inputs = [prompt, negative_prompt, subject, number_of_outputs, number_of_images_per_pose, randomise_poses, output_format, output_quality, seed]
+    outputs = [consistent_results]
+
+    submit_btn.click(
+        fn = predict,
+        inputs = inputs,
+        outputs = outputs,
+        show_api = False
+    )
+
+app.queue(max_size=12, api_open=False).launch(share=False, show_api=False)