Spaces:
Runtime error
Runtime error
File size: 4,844 Bytes
4b8ee81 d030f4c 4b8ee81 47b9b86 4b8ee81 |
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 |
import gradio as gr
import requests
from pydantic import BaseModel
from diffusers.utils import load_image
from io import BytesIO
sdxl_inference_endpoint = 'http://127.0.0.1:7860/api/v1/product-diffusion/sdxl_v0_lora_inference'
sdxl_batch_inference_endpoint = 'http://127.0.0.1:7860/api/v1/product-diffusion/sdxl_v0_lora_inference/batch'
kandinsky_inpainting_inference = 'http://127.0.0.1:7860/api/v1/product-diffusion/kandinskyv2.2_inpainting'
# Define the InpaintingRequest model
class InputRequest(BaseModel):
prompt: str
num_inference_steps: int
guidance_scale: float
negative_prompt: str
num_images: int
mode: str
class InpaintingRequest(BaseModel):
prompt: str
negative_prompt: str
num_inference_steps: int
strength: float
guidance_scale: float
mode: str
async def generate_sdxl_lora_image(prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode):
# Prepare the payload for SDXL LORA API
payload = InputRequest(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
num_images=num_images,
mode=mode
).dict()
response = requests.post(sdxl_inference_endpoint, json=payload)
response = response.json()
url = response['url']
image = load_image(url)
return image
def generate_outpainting(prompt, negative_prompt, num_inference_steps, strength, guidance_scale, mode, image):
# Convert the image to bytes
img_byte_arr = BytesIO()
image.save(img_byte_arr, format='PNG')
img_byte_arr = img_byte_arr.getvalue()
# Prepare the payload for multipart/form-data
files = {
'image': ('image.png', img_byte_arr, 'image/png'),
'prompt': (None, prompt),
'negative_prompt': (None, negative_prompt),
'num_inference_steps': (None, str(num_inference_steps)),
'strength': (None, str(strength)),
'guidance_scale': (None, str(guidance_scale)),
'mode': (None, mode)
}
response = requests.post(kandinsky_inpainting_inference, files=files)
response.raise_for_status()
response = response.json()
url = response['url']
image = load_image(url)
return image
with gr.Blocks(theme='VikramSingh178/Webui-Theme') as demo:
with gr.Tab("SdxL-Lora"):
with gr.Row():
with gr.Column():
with gr.Group():
prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
num_inference_steps = gr.Slider(minimum=1, maximum=1000, step=1, value=20, label="Inference Steps")
guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale")
num_images = gr.Slider(minimum=1, maximum=10, step=1, value=1, label="Number of Images")
mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode")
generate_button = gr.Button("Generate Image",variant='primary')
with gr.Column(scale=1):
image_preview = gr.Image(label="Generated Image",show_download_button=True,show_share_button=True,container=True)
generate_button.click(generate_sdxl_lora_image, inputs=[prompt, negative_prompt, num_inference_steps, guidance_scale, num_images, mode], outputs=[image_preview])
with gr.Tab("Generate AI Background"):
with gr.Row():
with gr.Column():
with gr.Group():
image_input = gr.Image(type="pil", label="Upload Image")
prompt = gr.Textbox(label="Prompt", placeholder="Enter your prompt here")
negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt here")
num_inference_steps = gr.Slider(minimum=1, maximum=500, step=1, value=20, label="Inference Steps")
guidance_scale = gr.Slider(minimum=1.0, maximum=10.0, step=0.1, value=7.5, label="Guidance Scale")
strength = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=1, label="Strength")
mode = gr.Dropdown(choices=["s3_json", "b64_json"], value="s3_json", label="Mode")
generate_button = gr.Button("Generate Background", variant='primary')
with gr.Column(scale=1):
image_preview = gr.Image(label="Image", show_download_button=True, show_share_button=True, container=True)
generate_button.click(generate_outpainting, inputs=[prompt, negative_prompt, num_inference_steps, strength, guidance_scale, mode, image_input], outputs=[image_preview])
demo.launch()
|