Update app.py

app.py

@@ -1,171 +1,144 @@
-import spaces
+import os
+import time
+import random
+import tempfile
 import torch
 import gradio as gr
-from gradio import processing_utils, utils
 from PIL import Image
-import random
+
+import spaces
+from gradio import processing_utils, utils
 
 from diffusers import (
-    DiffusionPipeline,
     AutoencoderKL,
-    StableDiffusionControlNetPipeline,
     ControlNetModel,
-    StableDiffusionLatentUpscalePipeline,
-    StableDiffusionImg2ImgPipeline,
+    StableDiffusionControlNetPipeline,
     StableDiffusionControlNetImg2ImgPipeline,
+    StableDiffusionLatentUpscalePipeline,
     DPMSolverMultistepScheduler,
-    EulerDiscreteScheduler
+    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"
+# -----------------------------
+# Device & dtype (GPU/CPU auto)
+# -----------------------------
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if device == "cuda" else torch.float32
 
-# 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)
+# -----------------------------
+# Base / ControlNet models
+# -----------------------------
+BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
+VAE_ID = "stabilityai/sd-vae-ft-mse"
+CONTROLNET_ID = "monster-labs/control_v1p_sd15_qrcode_monster"
 
-# 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")
+# -----------------------------
+# Load components
+# -----------------------------
+vae = AutoencoderKL.from_pretrained(VAE_ID, torch_dtype=dtype)
+controlnet = ControlNetModel.from_pretrained(CONTROLNET_ID, torch_dtype=dtype)
 
+# ⚠️ safety checker & clip feature extractor removed
 main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
     BASE_MODEL,
     controlnet=controlnet,
     vae=vae,
-    safety_checker=safety_checker,
-    feature_extractor=feature_extractor,
-    torch_dtype=torch.float16,
-).to("cuda")
+    safety_checker=None,      # <= important
+    feature_extractor=None,   # <= important
+    torch_dtype=dtype,
+)
+main_pipe = main_pipe.to(device)
 
-# 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"
+# Img2Img pipe reusing components
 image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
+image_pipe = image_pipe.to(device)
 
-
-#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++"),
+    "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)):
+# -----------------------------
+# Helpers
+# -----------------------------
+def center_crop_resize(img: Image.Image, 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
+    new_dim = min(width, height)
+    left = (width - new_dim) / 2
+    top = (height - new_dim) / 2
+    right = (width + new_dim) / 2
+    bottom = (height + new_dim) / 2
     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)
+    if crop == "center":
+        old_w = samples.shape[3]
+        old_h = samples.shape[2]
+        old_aspect = old_w / old_h
+        new_aspect = width / height
+        x = 0
+        y = 0
+        if old_aspect > new_aspect:
+            x = round((old_w - old_w * (new_aspect / old_aspect)) / 2)
+        elif old_aspect < new_aspect:
+            y = round((old_h - old_h * (old_aspect / new_aspect)) / 2)
+        s = samples[:, :, y : old_h - y, x : old_w - 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)
+    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 == "":
+    if not 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
+# -----------------------------
+# Inference
+# -----------------------------
 @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,
+    controlnet_conditioning_scale: float = 1.0,
+    control_guidance_start: float = 1.0,
+    control_guidance_end: float = 1.0,
     upscaler_strength: float = 0.5,
     seed: int = -1,
-    sampler = "DPM++ Karras SDE",
+    sampler: str = "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_small = center_crop_resize(control_image, (512, 512))
     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)
-    
+
+    my_seed = random.randint(0, 2**32 - 1) if seed == -1 else int(seed)
+    generator = torch.Generator(device=device).manual_seed(my_seed)
+
+    # First pass -> latents
     out = main_pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -176,28 +149,28 @@ def inference(
         control_guidance_start=float(control_guidance_start),
         control_guidance_end=float(control_guidance_end),
         num_inference_steps=15,
-        output_type="latent"
+        output_type="latent",
     )
+
+    # Upscale latents
     upscaled_latents = upscale(out, "nearest-exact", 2)
+
+    # Second pass -> image
     out_image = image_pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
-        control_image=control_image_large,        
+        control_image=control_image_large,
         image=upscaled_latents,
         guidance_scale=float(guidance_scale),
         generator=generator,
         num_inference_steps=20,
-        strength=upscaler_strength,
+        strength=float(upscaler_strength),
         control_guidance_start=float(control_guidance_start),
         control_guidance_end=float(control_guidance_end),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale)
+        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
+    # Save history
     user_history.save_image(
         label=prompt,
         image=out_image["images"][0],
@@ -210,44 +183,49 @@ def inference(
             "control_guidance_start": control_guidance_start,
             "control_guidance_end": control_guidance_end,
             "upscaler_strength": upscaler_strength,
-            "seed": seed,
+            "seed": my_seed,
             "sampler": sampler,
         },
     )
 
     return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
-        
+
+# -----------------------------
+# UI
+# -----------------------------
 with gr.Blocks() as app:
     gr.Markdown(
         '''
         <div style="text-align: center;">
             <h1>Illusion Diffusion HQ 🌀</h1>
-            <p style="font-size:16px;">Generate stunning high quality illusion artwork with Stable Diffusion</p>
-            <p>Illusion Diffusion is back up with a safety checker! Because I have been asked, if you would like to support me, consider using <a href="https://deforum.studio">deforum.studio</a></p>
-            <p>A space by AP <a href="https://twitter.com/angrypenguinPNG">Follow me on Twitter</a> with big contributions from <a href="https://twitter.com/multimodalart">multimodalart</a></p>
-            <p>This project works by using <a href="https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster">Monster Labs QR Control Net</a>. Given a prompt and your pattern, we use a QR code conditioned controlnet to create a stunning illusion! Credit to: <a href="https://twitter.com/MrUgleh">MrUgleh</a> for discovering the workflow :)</p>
+            <p style="font-size:16px;">Generate high-quality illusion artwork with Stable Diffusion + ControlNet</p>
+            <p>A space by AP with contributions from the community.</p>
+            <p>This uses <a href="https://huggingface.co/monster-labs/control_v1p_sd15_qrcode_monster">Monster Labs QR ControlNet</a>.</p>
         </div>
         '''
     )
 
-
     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")
+            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 a castle in the distance")
+            negative_prompt = gr.Textbox(label="Negative Prompt", info="What you do NOT want", value="low quality, blurry", 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)
+                sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="Euler", label="Sampler")
+                control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.0, label="Start of ControlNet")
+                control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="End of ControlNet")
+                strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1.0, label="Strength of the upscaler")
+                seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=-1, label="Seed", info="-1 = random")
+                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")
@@ -256,6 +234,7 @@ with gr.Blocks() as app:
                 loading_icon = gr.HTML(loading_icon_html)
                 share_button = gr.Button("Share to community", elem_id="share-btn")
 
+    # Wire up
     prompt.submit(
         check_inputs,
         inputs=[prompt, control_image],
@@ -263,8 +242,9 @@ with gr.Blocks() as app:
     ).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])
-    
+        outputs=[result_image, result_image, share_group, used_seed]
+    )
+
     run_btn.click(
         check_inputs,
         inputs=[prompt, control_image],
@@ -272,8 +252,9 @@ with gr.Blocks() as app:
     ).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])
-    
+        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:
@@ -282,7 +263,7 @@ with gr.Blocks(css=css) as app_with_history:
     with gr.Tab("Past generations"):
         user_history.render()
 
-app_with_history.queue(max_size=20,api_open=False )
+app_with_history.queue(max_size=20, api_open=False)
 
 if __name__ == "__main__":
     app_with_history.launch(max_threads=400)