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README.md

@@ -1,12 +1,11 @@
----
-title: Consistent Character
-emoji: 🤹
-colorFrom: indigo
-colorTo: blue
-sdk: docker
-pinned: false
-disable_embedding: true
-short_description: Create images of a given character in different poses
----
-
+---
+title: Consistent Character
+emoji: 🤹
+colorFrom: indigo
+colorTo: blue
+sdk: docker
+pinned: false
+short_description: Create images of a given character in different poses
+---
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,176 +1,173 @@
-import gradio as gr
-from urllib.parse import urlparse
-import requests
-import time
-import os
-import re
-
-hf_token = os.environ.get("HF_TOKEN")
-from gradio_client import Client
-
-
-def safety_check(user_prompt):
-    
-    client = Client("fffiloni/safety-checker-bot", hf_token=hf_token)
-    response = client.predict(
-    		source_space="consistent-character space",
-    		user_prompt=user_prompt,
-    		api_name="/infer"
-    )
-    print(response)
-    
-    return response
-
-from utils.gradio_helpers import parse_outputs, process_outputs
-
-names = ['prompt', 'negative_prompt', 'subject', 'number_of_outputs', 'number_of_images_per_pose', 'randomise_poses', 'output_format', 'output_quality', 'seed']
-
-def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
-    print(f"""
-        —/n
-        {args[0]}
-        """)
-    if args[0] == '' or args[0] is None:
-        raise gr.Error(f"You forgot to provide a prompt.")
-    
-    try:
-        
-        is_safe = safety_check(args[0])
-        print(is_safe)
-    
-        match = re.search(r'\bYes\b', is_safe)
-    
-        if match:
-            status = 'Yes'
-        else:
-            status = None
-    
-        if status == "Yes" :
-            raise gr.Error("Do not ask for such things.")
-        else:
-    
-            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}")
-
-    except Exception as e:
-        # Handle any other type of error
-        raise gr.Error(f"An error occurred: {e}")
-
-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.''',
-                    value="a person, darkblue suit, black tie, white pocket"
-                )
-        
-                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, show_error=True)
-
+import gradio as gr
+from urllib.parse import urlparse
+import requests
+import time
+import os
+import re
+
+hf_token = os.environ.get("HF_TOKEN")
+from gradio_client import Client
+
+
+def safety_check(user_prompt):
+    client = Client("fffiloni/safety-checker-bot", hf_token=hf_token)
+    response = client.predict(
+        "consistent-character space",    # str source space
+        user_prompt,	# str  in 'User sent this' Textbox component
+        api_name="/infer"
+    )
+    return response
+
+from utils.gradio_helpers import parse_outputs, process_outputs
+
+names = ['prompt', 'negative_prompt', 'subject', 'number_of_outputs', 'number_of_images_per_pose', 'randomise_poses', 'output_format', 'output_quality', 'seed']
+
+def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)):
+    print(f"""
+        —/n
+        {args[0]}
+        """)
+    if args[0] == '' or args[0] is None:
+        raise gr.Error(f"You forgot to provide a prompt.")
+    
+    try:
+        
+        is_safe = safety_check(args[0])
+        print(is_safe)
+    
+        match = re.search(r'\bYes\b', is_safe)
+    
+        if match:
+            status = 'Yes'
+        else:
+            status = None
+    
+        if status == "Yes" :
+            raise gr.Error("Do not ask for such things.")
+        else:
+    
+            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}")
+
+    except Exception as e:
+        # Handle any other type of error
+        raise gr.Error(f"An error occurred: {e}")
+
+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.''',
+                    value="a person, darkblue suit, black tie, white pocket"
+                )
+        
+                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, show_error=True)
+

requirements.txt

@@ -1,2 +1,2 @@
-gradio==5.0.1
+gradio==4.44.0
 prance