Spaces:
Running
on
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Running
on
Zero
File size: 4,009 Bytes
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import spaces
import gradio as gr
import os
from PIL import Image
import torch
from diffusers.utils import load_image, check_min_version
from controlnet_flux import FluxControlNetModel
from transformer_flux import FluxTransformer2DModel
from pipeline_flux_controlnet_inpaint import FluxControlNetInpaintingPipeline
import huggingface_hub
huggingface_hub.login(os.getenv('HF_TOKEN_FLUX'))
check_min_version("0.30.2")
transformer = FluxTransformer2DModel.from_pretrained(
"black-forest-labs/FLUX.1-dev", subfolder='transformer', torch_dytpe=torch.bfloat16
)
cuda_device =torch.device("cuda")
# Build pipeline
controlnet = FluxControlNetModel.from_pretrained("alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Beta", torch_dtype=torch.bfloat16)
pipe = FluxControlNetInpaintingPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
controlnet=controlnet,
transformer=transformer,
torch_dtype=torch.bfloat16
).to(cuda_device)
pipe.transformer.to(torch.bfloat16)
pipe.controlnet.to(torch.bfloat16)
MARKDOWN = """
# Nativ Inpainting
Model by Nativ
"""
@spaces.GPU()
def process(input_image_editor,
prompt,
negative_prompt,
controlnet_conditioning_scale,
guidance_scale,
seed,
num_inference_steps,
true_guidance_scale
):
image = input_image_editor['background']
mask = input_image_editor['layers'][0]
size = (768, 768)
image_or = image.copy()
image = image.convert("RGB").resize(size)
mask = mask.convert("RGB").resize(size)
generator = torch.Generator(device="cuda").manual_seed(seed)
result = pipe(
prompt=prompt,
height=size[1],
width=size[0],
control_image=image,
control_mask=mask,
num_inference_steps=num_inference_steps,
generator=generator,
controlnet_conditioning_scale=controlnet_conditioning_scale,
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
true_guidance_scale=true_guidance_scale
).images[0]
return result.resize((image_or.size[:2]))
with gr.Blocks() as demo:
gr.Markdown(MARKDOWN)
with gr.Row():
with gr.Column():
input_image_editor_component = gr.ImageEditor(
label='Image',
type='pil',
sources=["upload", "webcam"],
image_mode='RGB',
layers=False,
brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed"))
prompt = gr.Textbox(lines=2, placeholder="Enter prompt here...")
negative_prompt = gr.Textbox(lines=2, placeholder="Enter negative_prompt here...")
controlnet_conditioning_scale = gr.Slider(minimum=0, step=0.01, maximum=1, value=0.9, label="controlnet_conditioning_scale")
guidance_scale = gr.Slider(minimum=1, step=0.5, maximum=10, value=3.5, label="Image to generate")
seed = gr.Slider(minimum=0, step=1, maximum=10000000, value=124, label="Seed Value")
num_inference_steps = gr.Slider(minimum=1, step=1, maximum=30, value=10, label="num_inference_steps")
true_guidance_scale = gr.Slider(minimum=1, step=1, maximum=10, value=3.5, label="true_guidance_scale")
submit_button_component = gr.Button(
value='Submit', variant='primary', scale=0)
with gr.Column():
output_image_component = gr.Image(
type='pil', image_mode='RGB', label='Generated image', format="png")
submit_button_component.click(
fn=process,
inputs=[
input_image_editor_component,
prompt,
negative_prompt,
controlnet_conditioning_scale,
guidance_scale,
seed,
num_inference_steps,
true_guidance_scale
],
outputs=[
output_image_component,
]
)
demo.launch(debug=False, show_error=True,share=True) |