diff --git a/extensions-builtin/forge_space_illusion_diffusion/forge_app.py b/extensions-builtin/forge_space_illusion_diffusion/forge_app.py new file mode 100644 index 00000000..f816a87b --- /dev/null +++ b/extensions-builtin/forge_space_illusion_diffusion/forge_app.py @@ -0,0 +1,305 @@ +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, + EulerDiscreteScheduler +) +import tempfile +import time +from share_btn import community_icon_html, loading_icon_html, share_js +import user_history +from illusion_style import css +import os +from transformers import CLIPImageProcessor +from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker + +BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE" + + +with spaces.GPUObject() as gpu_object: + # Initialize both pipelines + vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16) + controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16) + + # Initialize the safety checker conditionally + SAFETY_CHECKER_ENABLED = os.environ.get("SAFETY_CHECKER", "0") == "1" + safety_checker = None + feature_extractor = None + if SAFETY_CHECKER_ENABLED: + safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker").to("cuda") + feature_extractor = CLIPImageProcessor.from_pretrained("openai/clip-vit-base-patch32") + + main_pipe = StableDiffusionControlNetPipeline.from_pretrained( + BASE_MODEL, + controlnet=controlnet, + vae=vae, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + torch_dtype=torch.float16, + ) + +cn_and_unet = torch.nn.ModuleList([main_pipe.unet, main_pipe.controlnet]) + +spaces.automatically_move_to_gpu_when_forward(main_pipe.unet, target_model=cn_and_unet) +spaces.automatically_move_to_gpu_when_forward(main_pipe.controlnet, target_model=cn_and_unet) +spaces.automatically_move_to_gpu_when_forward(main_pipe.vae) +spaces.automatically_move_to_gpu_when_forward(main_pipe.text_encoder) +spaces.change_attention_from_diffusers_to_forge(main_pipe.unet) +spaces.change_attention_from_diffusers_to_forge(main_pipe.vae) + +# Function to check NSFW images +# def check_nsfw_images(images: list[Image.Image]) -> tuple[list[Image.Image], list[bool]]: +# if SAFETY_CHECKER_ENABLED: +# safety_checker_input = feature_extractor(images, return_tensors="pt").to("cuda") +# has_nsfw_concepts = safety_checker( +# images=[images], +# clip_input=safety_checker_input.pixel_values.to("cuda") +# ) +# return images, has_nsfw_concepts +# else: +# return images, [False] * len(images) + +# 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(gpu_objects=[gpu_object], manual_load=True) +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=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( + ''' +
+

Illusion Diffusion HQ 🌀

+

Generate stunning high quality illusion artwork with Stable Diffusion

+

Illusion Diffusion is back up with a safety checker! Because I have been asked, if you would like to support me, consider using deforum.studio

+

A space by AP Follow me on Twitter with big contributions from multimodalart

+

This project works by using Monster Labs QR Control Net. Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: 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") + examples = ["checkers.png", "checkers_mid.jpg", "pattern.png", "ultra_checkers.png", "spiral.jpeg", "funky.jpeg"] + examples = [spaces.convert_root_path() + y for y in examples] + gr.Examples(examples=examples, 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 demo: + with gr.Tab("Demo"): + app.render() + with gr.Tab("Past generations"): + user_history.render() + +if __name__ == "__main__": + demo.launch() diff --git a/extensions-builtin/forge_space_illusion_diffusion/space_meta.json b/extensions-builtin/forge_space_illusion_diffusion/space_meta.json new file mode 100644 index 00000000..ce85768a --- /dev/null +++ b/extensions-builtin/forge_space_illusion_diffusion/space_meta.json @@ -0,0 +1,6 @@ +{ + "tag": "General Image Processing and Applications", + "title": "IllusionDiffusion (Monster Labs QR Control Net)", + "repo_id": "AP123/IllusionDiffusion", + "revision": "7a874d15dccd7b8c6c30d3440f07ddedb54ec454" +}