Update app.py

app.py

@@ -1,142 +1,102 @@
 import gradio as gr
 import numpy as np
 import random
-#import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from diffusers import DDPMPipeline, DDIMScheduler
+import open_clip
+import torchvision
+from PIL import Image
+from tqdm import tqdm
+import torch.nn.functional as F
 
+# Initialize device
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+# Load CLIP model
+clip_model, _, preprocess = open_clip.create_model_and_transforms("ViT-B-32", pretrained="openai")
+clip_model.to(device)
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# Transform to preprocess images
+tfms = torchvision.transforms.Compose(
+    [
+        torchvision.transforms.Resize((224, 224)),
+        torchvision.transforms.ToTensor(),
+        torchvision.transforms.Normalize(
+            mean=(0.48145466, 0.4578275, 0.40821073),
+            std=(0.26862954, 0.26130258, 0.27577711),
+        ),
+    ]
+)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+# CLIP Loss function
+def clip_loss(image, text_features):
+    image_features = clip_model.encode_image(tfms(image).unsqueeze(0).to(device))
+    image_features = F.normalize(image_features, dim=-1)
+    text_features = F.normalize(text_features, dim=-1)
+    loss = (1 - torch.cosine_similarity(image_features, text_features)).mean()
+    return loss
 
-#@spaces.GPU #[uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+# Load Diffusion model
+model_repo_id = "muneebable/ddpm-celebahq-finetuned-anime-art"  # Replace with desired model repo
+image_pipe = DDPMPipeline.from_pretrained(model_repo_id)
+image_pipe.to(device)
+
+# Load scheduler
+scheduler = DDIMScheduler.from_pretrained(model_repo_id)
+scheduler.set_timesteps(num_inference_steps=40)
 
+# Gradio Inference Function
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-        
-    generator = torch.Generator().manual_seed(seed)
+        seed = random.randint(0, np.iinfo(np.int32).max)
+    generator = torch.manual_seed(seed)
     
-    image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+    # Embed prompt with CLIP
+    text = open_clip.tokenize([prompt]).to(device)
+    with torch.no_grad():
+        text_features = clip_model.encode_text(text)
+
+    x = torch.randn(4, 3, 256, 256).to(device)
     
-    return image, seed
+    for i, t in tqdm(enumerate(scheduler.timesteps)):
+        model_input = scheduler.scale_model_input(x, t)
+        with torch.no_grad():
+            noise_pred = image_pipe.unet(model_input, t)["sample"]
+        cond_grad = 0
+        for cut in range(4):
+            x = x.detach().requires_grad_()
+            x0 = scheduler.step(noise_pred, t, x).pred_original_sample
+            loss = clip_loss(x0, text_features) * guidance_scale
+            cond_grad -= torch.autograd.grad(loss, x)[0] / 4
+        alpha_bar = scheduler.alphas_cumprod[i]
+        x = x.detach() + cond_grad * alpha_bar.sqrt()
+        x = scheduler.step(noise_pred, t, x).prev_sample
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    # Convert output to an image
+    grid = torchvision.utils.make_grid(x.detach(), nrow=4)
+    im = grid.permute(1, 2, 0).cpu().clip(-1, 1) * 0.5 + 0.5
+    result_image = Image.fromarray((im.numpy() * 255).astype(np.uint8))
+    
+    return result_image, seed
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+# Gradio App
+with gr.Blocks() as demo:
+    prompt = gr.Textbox(placeholder="Enter your prompt", label="Prompt")
+    run_button = gr.Button("Generate")
 
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
+    result = gr.Image(label="Generated Image")
+
+    with gr.Accordion("Advanced Settings"):
+        negative_prompt = gr.Textbox(label="Negative Prompt")
+        seed = gr.Slider(0, np.iinfo(np.int32).max, value=0, label="Seed")
+        randomize_seed = gr.Checkbox(True, label="Randomize Seed")
+        width = gr.Slider(256, 1024, value=512, label="Width")
+        height = gr.Slider(256, 1024, value=512, label="Height")
+        guidance_scale = gr.Slider(0.0, 10.0, value=7.5, label="Guidance Scale")
+        num_inference_steps = gr.Slider(1, 50, value=50, label="Steps")
 
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024, #Replace with defaults that work for your model
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0, #Replace with defaults that work for your model
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2, #Replace with defaults that work for your model
-                )
-        
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
-    )
+    run_button.click(infer, 
+                     inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], 
+                     outputs=[result, seed])
 
-demo.queue().launch()
+demo.queue().launch()