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Running
on
A10G
Running
on
A10G
| import gradio as gr | |
| from urllib.parse import urlparse | |
| import requests | |
| import time | |
| import os | |
| from utils.gradio_helpers import parse_outputs, process_outputs | |
| inputs = [] | |
| inputs.append(gr.Textbox( | |
| label="Prompt", info='''Describe the subject. Include clothes and hairstyle for more consistency.''' | |
| )) | |
| inputs.append(gr.Textbox( | |
| label="Negative Prompt", info='''Things you do not want to see in your image''' | |
| )) | |
| inputs.append(gr.Image( | |
| label="Subject", type="filepath" | |
| )) | |
| inputs.append(gr.Slider( | |
| label="Number Of Outputs", info='''The number of images to generate.''', value=3, | |
| minimum=1, maximum=20, step=1, | |
| )) | |
| inputs.append(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, | |
| )) | |
| inputs.append(gr.Checkbox( | |
| label="Randomise Poses", info='''Randomise the poses used.''', value=True | |
| )) | |
| inputs.append(gr.Dropdown( | |
| choices=['webp', 'jpg', 'png'], label="output_format", info='''Format of the output images''', value="webp" | |
| )) | |
| inputs.append(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 | |
| )) | |
| inputs.append(gr.Number( | |
| label="Seed", info='''Set a seed for reproducibility. Random by default.''', value=None | |
| )) | |
| names = ['prompt', 'negative_prompt', 'subject', 'number_of_outputs', 'number_of_images_per_pose', 'randomise_poses', 'output_format', 'output_quality', 'seed'] | |
| outputs = [] | |
| outputs.append(gr.Image()) | |
| outputs.append(gr.Image()) | |
| outputs.append(gr.Image()) | |
| outputs.append(gr.Image()) | |
| outputs.append(gr.Image()) | |
| expected_outputs = len(outputs) | |
| def predict(request: gr.Request, *args, progress=gr.Progress(track_tqdm=True)): | |
| 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) | |
| difference_outputs = expected_outputs - len(processed_outputs) | |
| # If less outputs than expected, hide the extra ones | |
| if difference_outputs > 0: | |
| extra_outputs = [gr.update(visible=False)] * difference_outputs | |
| processed_outputs.extend(extra_outputs) | |
| # If more outputs than expected, cap the outputs to the expected number | |
| elif difference_outputs < 0: | |
| processed_outputs = processed_outputs[:difference_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" | |
| model_description = "Create images of a given character in different poses" | |
| app = gr.Interface( | |
| fn=predict, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| description=model_description, | |
| allow_flagging="never", | |
| ) | |
| app.launch(share=True) | |