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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 | |
from transformers.utils.hub import move_cache | |
move_cache() | |
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 | |
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> | |
This project works by using | |
[Monster Labs QR Control Net](https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster) and [multimodalart](https://twitter.com/multimodalart) | |
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( | |
inference, | |
inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler], | |
outputs=[result_image, result_image, share_group, used_seed]) | |
run_btn.click( | |
check_inputs, | |
inputs=[prompt, control_image], | |
queue=False | |
).success( | |
inference, | |
inputs=[control_image, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler], | |
outputs=[result_image, result_image, share_group, used_seed]) | |
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) | |