File size: 4,432 Bytes
e64cfbe f8f1485 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
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='''Prompt for the model'''
))
inputs.append(gr.Image(
label="Image", type="filepath"
))
inputs.append(gr.Dropdown(
choices=[2048, 2560], label="resolution", info='''Image resolution''', value="2048"
))
inputs.append(gr.Number(
label="Resemblance", info='''Conditioning scale for controlnet''', value=0.5
))
inputs.append(gr.Number(
label="Creativity", info='''Denoising strength. 1 means total destruction of the original image''', value=0.5
))
inputs.append(gr.Number(
label="Hdr", info='''HDR improvement over the original image''', value=0
))
inputs.append(gr.Dropdown(
choices=['DDIM', 'DPMSolverMultistep', 'K_EULER_ANCESTRAL', 'K_EULER'], label="scheduler", info='''Choose a scheduler.''', value="DDIM"
))
inputs.append(gr.Number(
label="Steps", info='''Steps''', value=20
))
inputs.append(gr.Slider(
label="Guidance Scale", info='''Scale for classifier-free guidance''', value=7,
minimum=0.1, maximum=30
))
inputs.append(gr.Number(
label="Seed", info='''Seed''', value=None
))
inputs.append(gr.Textbox(
label="Negative Prompt", info='''Negative prompt'''
))
inputs.append(gr.Checkbox(
label="Guess Mode", info='''In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.''', value=False
))
names = ['prompt', 'image', 'resolution', 'resemblance', 'creativity', 'hdr', 'scheduler', 'steps', 'guidance_scale', 'seed', 'negative_prompt', 'guess_mode']
outputs = []
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 high-resolution-controlnet-tile cog image by batouresearch"
model_description = "Fermat.app open-source implementation of an efficient ControlNet 1.1 tile for high-quality upscales. Increase the creativity to encourage hallucination."
app = gr.Interface(
fn=predict,
inputs=inputs,
outputs=outputs,
title=title,
description=model_description,
allow_flagging="never",
)
app.launch(share=True) |