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Running
on
Zero
File size: 5,444 Bytes
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import torch
import spaces
import gradio as gr
from diffusers import FluxInpaintPipeline
import random
import numpy as np
import google.generativeai as genai
MARKDOWN = """
# Prompt Canvas🎨
Thanks to [Black Forest Labs](https://huggingface.co/black-forest-labs) team for creating this amazing model,
and a big thanks to [Gothos](https://github.com/Gothos) for taking it to the next level by enabling inpainting with the FLUX.
"""
#Gemini Setup
genai.configure(api_key = os.environ['Gemini_API'])
gemini_flash = genai.GenerativeModel(model_name='gemini-1.5-flash-002')
def gemini_predict(prompt):
system_message = f"""You are the best text analyser.
You have to analyse a user query and identify what the user wants to change, from a given user query.
Examples:
Query: Change Lipstick colour to blue
Response: Lips
Query: Add a nose stud
Response: Nose
Query: Add a wallpaper to the right wall
Response: Right wall
Query: Change the Sofa's colour to Purple
Response: Sofa
Your response should be in 1 or 2-3 words
Query : {prompt}
"""
response = gemini_flash.generate_content(system_message)
return(response.text)
MAX_SEED = np.iinfo(np.int32).max
DEVICE = "cuda" #if torch.cuda.is_available() else "cpu"
inpaint_pipe = FluxInpaintPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
inpaint_pipe.load_lora_weights('hugovntr/flux-schnell-realism', weight_name='schnell-realism_v1')
@spaces.GPU()
def process(input_image_editor, mask_image, input_text, strength, seed, randomize_seed, num_inference_steps, guidance_scale=3.5, progress=gr.Progress(track_tqdm=True)):
if not input_text:
raise gr.Error("Please enter a text prompt.")
image = input_image_editor['background']
if not image:
raise gr.Error("Please upload an image.")
width, height = image.size
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=DEVICE).manual_seed(seed)
result = inpaint_pipe(prompt=input_text, image=image, mask_image=mask_image, width=width, height=height,
strength=strength, num_inference_steps=num_inference_steps, generator=generator,
guidance_scale=guidance_scale).images[0]
return result, mask_image, seed
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown(MARKDOWN)
with gr.Row():
with gr.Column(scale=1):
input_image_component = gr.ImageEditor(
label='Image',
type='pil',
sources=["upload", "webcam"],
image_mode='RGB',
layers=False,
brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
input_text_component = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
with gr.Accordion("Advanced Settings", open=False):
strength_slider = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.7,
step=0.01,
label="Strength"
)
num_inference_steps = gr.Slider(
minimum=1,
maximum=100,
value=30,
step=1,
label="Number of inference steps"
)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=15,
step=0.1,
value=3.5,
)
seed_number = gr.Number(
label="Seed",
value=42,
precision=0
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Accordion("Upload a mask", open=False):
uploaded_mask_component = gr.Image(label="Already made mask (black pixels will be preserved, white pixels will be redrawn)", sources=["upload"], type="pil")
submit_button_component = gr.Button(
value='Inpaint', variant='primary')
with gr.Column(scale=1):
output_image_component = gr.Image(
type='pil', image_mode='RGB', label='Generated Image')
output_mask_component = gr.Image(
type='pil', image_mode='RGB', label='Generated Mask')
with gr.Accordion("Debug Info", open=False):
output_seed = gr.Number(label="Used Seed")
submit_button_component.click(
fn=process,
inputs=[input_image_component, uploaded_mask_component, input_text_component, strength_slider, seed_number, randomize_seed, num_inference_steps, guidance_scale],
outputs=[output_image_component, output_mask_component, output_seed]
)
demo.launch(debug=False, show_error=True) |