Update app.py #1069

README.md

@@ -4,7 +4,7 @@ emoji: 👁
 colorFrom: red
 colorTo: pink
 sdk: gradio
-sdk_version: 6.14.0
+sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 license: openrail

app.py

@@ -1,19 +1,25 @@
 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,
+    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
@@ -23,18 +29,14 @@ BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
 
 # 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
-)
+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")
+    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(
@@ -46,53 +48,76 @@ main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
     torch_dtype=torch.float16,
 ).to("cuda")
 
+# 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++"
-    ),
+    "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
+    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
 
+    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_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):
-    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
-
+        #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:
@@ -100,6 +125,14 @@ def check_inputs(prompt: str, control_image: Image.Image):
     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
@@ -109,25 +142,30 @@ def inference(
     negative_prompt: str,
     guidance_scale: float = 8.0,
     controlnet_conditioning_scale: float = 1,
-    control_guidance_start: 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),
+    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,
@@ -138,13 +176,13 @@ 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"
     )
     upscaled_latents = upscale(out, "nearest-exact", 2)
     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,
@@ -152,17 +190,34 @@ def inference(
         strength=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")
 
-    return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
-
+    # 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,
+        },
+    )
 
-with gr.Blocks(css=css) as app:
+    return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
+        
+with gr.Blocks() as app:
     gr.Markdown(
         '''
         <div style="text-align: center;">
@@ -175,58 +230,24 @@ with gr.Blocks(css=css) as app:
         '''
     )
 
+
     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",
-            )
+            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)
+                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")
@@ -238,48 +259,30 @@ with gr.Blocks(css=css) as app:
     prompt.submit(
         check_inputs,
         inputs=[prompt, control_image],
-        queue=False,
+        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],
-    )
-
+        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,
+        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],
-    )
-
+        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)
 
-app.queue(max_size=20, api_open=False)
+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.launch(max_threads=400)
+    app_with_history.launch(max_threads=400)