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Running
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import spaces
import torch
import gradio as gr
from gradio import processing_utils, utils
from PIL import Image
import random
from diffusers import (
DiffusionPipeline,
AutoencoderKL,
StableDiffusionControlNetPipeline,
ControlNetModel,
StableDiffusionLatentUpscalePipeline,
StableDiffusionImg2ImgPipeline,
StableDiffusionControlNetImg2ImgPipeline,
DPMSolverMultistepScheduler, # <-- Added import
EulerDiscreteScheduler # <-- Added import
)
import tempfile
import time
from share_btn import community_icon_html, loading_icon_html, share_js
import user_history
from illusion_style import css
BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
# Initialize both pipelines
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
#init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", torch_dtype=torch.float16)
controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)#, torch_dtype=torch.float16)
main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
BASE_MODEL,
controlnet=controlnet,
vae=vae,
safety_checker=None,
torch_dtype=torch.float16,
).to("cuda")
#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
#main_pipe.unet.to(memory_format=torch.channels_last)
#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
#model_id = "stabilityai/sd-x2-latent-upscaler"
image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
#image_pipe.unet = torch.compile(image_pipe.unet, mode="reduce-overhead", fullgraph=True)
#upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
#upscaler.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),
}
def center_crop_resize(img, output_size=(512, 512)):
width, height = img.size
# Calculate dimensions to crop to the center
new_dimension = min(width, height)
left = (width - new_dimension)/2
top = (height - new_dimension)/2
right = (width + new_dimension)/2
bottom = (height + new_dimension)/2
# Crop and resize
img = img.crop((left, top, right, bottom))
img = img.resize(output_size)
return img
def common_upscale(samples, width, height, upscale_method, crop=False):
if crop == "center":
old_width = samples.shape[3]
old_height = samples.shape[2]
old_aspect = old_width / old_height
new_aspect = width / height
x = 0
y = 0
if old_aspect > new_aspect:
x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
elif old_aspect < new_aspect:
y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
s = samples[:,:,y:old_height-y,x:old_width-x]
else:
s = samples
return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
def upscale(samples, upscale_method, scale_by):
#s = samples.copy()
width = round(samples["images"].shape[3] * scale_by)
height = round(samples["images"].shape[2] * scale_by)
s = common_upscale(samples["images"], width, height, upscale_method, "disabled")
return (s)
def check_inputs(prompt: str, control_image: Image.Image):
if control_image is None:
raise gr.Error("Please select or upload an Input Illusion")
if prompt is None or prompt == "":
raise gr.Error("Prompt is required")
def convert_to_pil(base64_image):
pil_image = Image.open(base64_image)
return pil_image
def convert_to_base64(pil_image):
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as temp_file:
image.save(temp_file.name)
return temp_file.name
# Inference function
@spaces.GPU
def inference(
control_image: Image.Image,
prompt: str,
negative_prompt: str,
guidance_scale: float = 8.0,
controlnet_conditioning_scale: float = 1,
control_guidance_start: float = 1,
control_guidance_end: float = 1,
upscaler_strength: float = 0.5,
seed: int = -1,
sampler = "DPM++ Karras SDE",
progress = gr.Progress(track_tqdm=True),
profile: gr.OAuthProfile | None = None,
):
start_time = time.time()
start_time_struct = time.localtime(start_time)
start_time_formatted = time.strftime("%H:%M:%S", start_time_struct)
print(f"Inference started at {start_time_formatted}")
# Generate the initial image
#init_image = init_pipe(prompt).images[0]
# Rest of your existing code
control_image_small = center_crop_resize(control_image)
control_image_large = center_crop_resize(control_image, (1024, 1024))
main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
generator = torch.Generator(device="cuda").manual_seed(my_seed)
out = main_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=control_image_small,
guidance_scale=float(guidance_scale),
controlnet_conditioning_scale=float(controlnet_conditioning_scale),
generator=generator,
control_guidance_start=float(control_guidance_start),
control_guidance_end=float(control_guidance_end),
num_inference_steps=15,
output_type="latent"
)
upscaled_latents = upscale(out, "nearest-exact", 2)
out_image = image_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
control_image=control_image_large,
image=upscaled_latents,
guidance_scale=float(guidance_scale),
generator=generator,
num_inference_steps=20,
strength=upscaler_strength,
control_guidance_start=float(control_guidance_start),
control_guidance_end=float(control_guidance_end),
controlnet_conditioning_scale=float(controlnet_conditioning_scale)
)
end_time = time.time()
end_time_struct = time.localtime(end_time)
end_time_formatted = time.strftime("%H:%M:%S", end_time_struct)
print(f"Inference ended at {end_time_formatted}, taking {end_time-start_time}s")
# Save image + metadata
user_history.save_image(
label=prompt,
image=out_image["images"][0],
profile=profile,
metadata={
"prompt": prompt,
"negative_prompt": negative_prompt,
"guidance_scale": guidance_scale,
"controlnet_conditioning_scale": controlnet_conditioning_scale,
"control_guidance_start": control_guidance_start,
"control_guidance_end": control_guidance_end,
"upscaler_strength": upscaler_strength,
"seed": seed,
"sampler": sampler,
},
)
return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
with gr.Blocks() as app:
gr.Markdown(
'''
<center><h1>Illusion Diffusion HQ 🌀</h1></span>
<span font-size:16px;">Generate stunning high quality illusion artwork with Stable Diffusion</span>
</center>
A space by AP [Follow me on Twitter](https://twitter.com/angrypenguinPNG) with big contributions from [multimodalart](https://twitter.com/multimodalart)
This project works by using [Monster Labs QR Control Net](https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster).
Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: [MrUgleh](https://twitter.com/MrUgleh) for discovering the workflow :)
'''
)
state_img_input = gr.State()
state_img_output = gr.State()
with gr.Row():
with gr.Column():
control_image = gr.Image(label="Input Illusion", type="pil", elem_id="control_image")
controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.8, label="Illusion strength", elem_id="illusion_strength", info="ControlNet conditioning scale")
gr.Examples(examples=["checkers.png", "checkers_mid.jpg", "pattern.png", "ultra_checkers.png", "spiral.jpeg", "funky.jpeg" ], inputs=control_image)
prompt = gr.Textbox(label="Prompt", elem_id="prompt", info="Type what you want to generate", placeholder="Medieval village scene with busy streets and castle in the distance")
negative_prompt = gr.Textbox(label="Negative Prompt", info="Type what you don't want to see", value="low quality", elem_id="negative_prompt")
with gr.Accordion(label="Advanced Options", open=False):
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="Euler")
control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0, label="Start of ControlNet")
control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="End of ControlNet")
strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="Strength of the upscaler")
seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=-1, label="Seed", info="-1 means random seed")
used_seed = gr.Number(label="Last seed used",interactive=False)
run_btn = gr.Button("Run")
with gr.Column():
result_image = gr.Image(label="Illusion Diffusion Output", interactive=False, elem_id="output")
with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
community_icon = gr.HTML(community_icon_html)
loading_icon = gr.HTML(loading_icon_html)
share_button = gr.Button("Share to community", elem_id="share-btn")
prompt.submit(
check_inputs,
inputs=[prompt, control_image],
queue=False
).success(
convert_to_pil,
inputs=[control_image],
outputs=[state_img_input],
queue=False,
preprocess=False,
).success(
inference,
inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
outputs=[state_img_output, result_image, share_group, used_seed]
).success(
convert_to_base64,
inputs=[state_img_output],
outputs=[result_image],
queue=False,
postprocess=False
)
run_btn.click(
check_inputs,
inputs=[prompt, control_image],
queue=False
).success(
convert_to_pil,
inputs=[control_image],
outputs=[state_img_input],
queue=False,
preprocess=False,
).success(
inference,
inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
outputs=[state_img_output, result_image, share_group, used_seed]
).success(
convert_to_base64,
inputs=[state_img_output],
outputs=[result_image],
queue=False,
postprocess=False
)
share_button.click(None, [], [], js=share_js)
with gr.Blocks(css=css) as app_with_history:
with gr.Tab("Demo"):
app.render()
with gr.Tab("Past generations"):
user_history.render()
app_with_history.queue(max_size=20,api_open=False )
if __name__ == "__main__":
app_with_history.launch(max_threads=400)
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