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| import gradio as gr | |
| import os | |
| import requests | |
| import time | |
| import re | |
| from PIL import Image | |
| from gradio_client import Client | |
| # Ensure HF_TOKEN is set | |
| hf_token = os.environ.get("HF_TOKEN") | |
| if not hf_token: | |
| raise ValueError("HF_TOKEN environment variable is not set. Please set it to your Hugging Face token.") | |
| # Verify the repository access | |
| repo_id = "fffiloni/safety-checker-bot" | |
| try: | |
| client = Client(repo_id, hf_token=hf_token) | |
| except Exception as e: | |
| raise ValueError(f"Failed to access repository {repo_id}: {e}") | |
| def safety_check(user_prompt): | |
| try: | |
| response = client.predict( | |
| "consistent-character space", # str source space | |
| user_prompt, # str in 'User sent this' Textbox component | |
| api_name="/infer" | |
| ) | |
| return response | |
| except Exception as e: | |
| raise ValueError(f"Safety check failed: {e}") | |
| def load_images_from_folder(folder): | |
| images = [] | |
| for filename in os.listdir(folder): | |
| if filename.endswith((".png", ".jpg", ".jpeg")): | |
| images.append(os.path.join(folder, filename)) | |
| return images | |
| def generate_variation(original_image_path): | |
| original_image = Image.open(original_image_path) | |
| variation = original_image.transpose(Image.FLIP_LEFT_RIGHT) # Example: Flip the image | |
| output_path = os.path.join("output", os.path.basename(original_image_path)) | |
| os.makedirs(os.path.dirname(output_path), exist_ok=True) | |
| variation.save(output_path) | |
| return output_path | |
| def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)): | |
| prompt = args[0] | |
| if not prompt: | |
| raise gr.Error("You forgot to provide a prompt.") | |
| try: | |
| is_safe = safety_check(prompt) | |
| if "Yes" in is_safe: | |
| 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(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: | |
| 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; | |
| } | |
| """ | |
| images = load_images_from_folder("uploaded_images") | |
| 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" | |
| ) | |
| image_selection = gr.Dropdown( | |
| label="Select an image from directory", choices=images, type="index" | |
| ) | |
| 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) | |