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app.py

@@ -1,26 +1,4 @@
-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()
 
+

app2.py

@@ -0,0 +1,264 @@
+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
+@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>
+ 
+        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)