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import torch
import os
import gradio as gr
from PIL import Image
from diffusers import (
DiffusionPipeline,
StableDiffusionControlNetImg2ImgPipeline,
ControlNetModel,
)
# Initialize both pipelines
init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V2.0", torch_dtype=torch.float16).to("cuda")
controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)
main_pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"SG161222/Realistic_Vision_V2.0",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16,
).to("cuda")
# Sampler map
SAMPLER_MAP = {
"DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
"Euler": lambda config: EulerDiscreteScheduler.from_config(config),
}
# Inference function
def inference(
control_image: Image.Image,
prompt: str,
negative_prompt: str,
guidance_scale: float = 8.0,
controlnet_conditioning_scale: float = 1,
strength: float = 0.9,
seed: int = -1,
sampler = "DPM++ Karras SDE",
):
if prompt is None or prompt == "":
raise gr.Error("Prompt is required")
# Generate the initial image
init_image = init_pipe(prompt).images[0]
# Rest of your existing code
control_image = control_image.resize((512, 512))
main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
generator = torch.manual_seed(seed) if seed != -1 else torch.Generator()
out = main_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=init_image,
control_image=control_image,
guidance_scale=guidance_scale,
controlnet_conditioning_scale=controlnet_conditioning_scale,
generator=generator,
strength=strength,
num_inference_steps=30,
)
return out.images[0]
with gr.Blocks() as app:
gr.Markdown(
'''
# Illusion Diffusion 🌀
## Generate stunning illusion artwork with Stable Diffusion
**[Follow me on Twitter](https://twitter.com/angrypenguinPNG)**
'''
)
with gr.Row():
with gr.Column():
control_image = gr.Image(label="Input Illusion", type="pil")
prompt = gr.Textbox(label="Prompt")
negative_prompt = gr.Textbox(label="Negative Prompt", value="ugly, disfigured, low quality, blurry, nsfw")
with gr.Accordion(label="Advanced Options", open=False):
controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=1.1, label="Controlnet Conditioning Scale")
strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.9, label="Strength")
guidance_scale = gr.Slider(minimum=0.0, maximum=50.0, step=0.25, value=7.5, label="Guidance Scale")
sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="DPM++ Karras SDE")
seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=2313123, label="Seed", randomize=True)
run_btn = gr.Button("Run")
with gr.Column():
result_image = gr.Image(label="Illusion Diffusion Output")
run_btn.click(
inference,
inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, strength, seed, sampler],
outputs=[result_image]
)
app.queue(concurrency_count=4, max_size=20)
if __name__ == "__main__":
app.launch(debug=True) |