Deploy custom Gradio projec3t

app.py

@@ -1,7 +1,443 @@
+import torch
+import numpy as np
 import gradio as gr
+from diffusers import (
+    StableDiffusionXLPipeline, 
+    AutoPipelineForInpainting, 
+    TCDScheduler,
+    ControlNetModel,
+    StableDiffusionXLControlNetPipeline,
+    MotionAdapter,
+    AnimateDiffPipeline
+)
+from diffusers.utils import make_image_grid, export_to_gif
+from PIL import Image
+import io
+import requests
+from transformers import DPTImageProcessor, DPTForDepthEstimation
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Available models
+AVAILABLE_MODELS = {
+    "Stable Diffusion XL": "stabilityai/stable-diffusion-xl-base-1.0",
+    "Animagine XL 3.0": "cagliostrolab/animagine-xl-3.0",
+}
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Available LoRA styles
+AVAILABLE_LORAS = {
+    "TCD": "h1t/TCD-SDXL-LoRA",
+    "Papercut": "TheLastBen/Papercut_SDXL",
+}
+
+def get_depth_map(image):
+    # Initialize depth estimator
+    depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas")
+    feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
+    
+    # Process image
+    image = feature_extractor(images=image, return_tensors="pt").pixel_values
+    with torch.no_grad():
+        depth_map = depth_estimator(image).predicted_depth
+
+    # Resize and normalize depth map
+    depth_map = torch.nn.functional.interpolate(
+        depth_map.unsqueeze(1),
+        size=(1024, 1024),
+        mode="bicubic",
+        align_corners=False,
+    )
+    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
+    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
+    image = torch.cat([depth_map] * 3, dim=1)
+
+    # Convert to PIL Image
+    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
+    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
+    return image
+
+def load_image_from_url(url):
+    response = requests.get(url)
+    return Image.open(io.BytesIO(response.content)).convert("RGB")
+
+def generate_image(prompt, seed, num_steps, guidance_scale, eta):
+    # Initialize the pipeline
+    base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    tcd_lora_id = "h1t/TCD-SDXL-LoRA"
+    
+    # Use CPU for inference
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        base_model_id,
+        torch_dtype=torch.float32  # Use float32 for CPU
+    )
+    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+    
+    # Load and fuse LoRA weights
+    pipe.load_lora_weights(tcd_lora_id)
+    pipe.fuse_lora()
+    
+    # Generate the image
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        eta=eta,
+        generator=generator,
+    ).images[0]
+    
+    return image
+
+def generate_community_image(prompt, model_name, seed, num_steps, guidance_scale, eta):
+    # Initialize the pipeline
+    base_model_id = AVAILABLE_MODELS[model_name]
+    tcd_lora_id = "h1t/TCD-SDXL-LoRA"
+    
+    # Use CPU for inference
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        base_model_id,
+        torch_dtype=torch.float32  # Use float32 for CPU
+    )
+    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+    
+    # Load and fuse LoRA weights
+    pipe.load_lora_weights(tcd_lora_id)
+    pipe.fuse_lora()
+    
+    # Generate the image
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        eta=eta,
+        generator=generator,
+    ).images[0]
+    
+    return image
+
+def generate_style_mix(prompt, seed, num_steps, guidance_scale, eta, style_weight):
+    # Initialize the pipeline
+    base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    tcd_lora_id = "h1t/TCD-SDXL-LoRA"
+    styled_lora_id = "TheLastBen/Papercut_SDXL"
+    
+    # Use CPU for inference
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        base_model_id,
+        torch_dtype=torch.float32  # Use float32 for CPU
+    )
+    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+    
+    # Load multiple LoRA weights
+    pipe.load_lora_weights(tcd_lora_id, adapter_name="tcd")
+    pipe.load_lora_weights(styled_lora_id, adapter_name="style")
+    
+    # Set adapter weights
+    pipe.set_adapters(["tcd", "style"], adapter_weights=[1.0, style_weight])
+    
+    # Generate the image
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        eta=eta,
+        generator=generator,
+    ).images[0]
+    
+    return image
+
+def generate_controlnet(prompt, init_image, seed, num_steps, guidance_scale, eta, controlnet_scale):
+    # Initialize the pipeline
+    base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    controlnet_id = "diffusers/controlnet-depth-sdxl-1.0"
+    tcd_lora_id = "h1t/TCD-SDXL-LoRA"
+    
+    # Initialize ControlNet
+    controlnet = ControlNetModel.from_pretrained(
+        controlnet_id,
+        torch_dtype=torch.float32  # Use float32 for CPU
+    )
+    
+    # Initialize pipeline
+    pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+        base_model_id,
+        controlnet=controlnet,
+        torch_dtype=torch.float32  # Use float32 for CPU
+    )
+    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+    
+    # Load and fuse LoRA weights
+    pipe.load_lora_weights(tcd_lora_id)
+    pipe.fuse_lora()
+    
+    # Generate depth map
+    depth_image = get_depth_map(init_image)
+    
+    # Generate the image
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        image=depth_image,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        eta=eta,
+        controlnet_conditioning_scale=controlnet_scale,
+        generator=generator,
+    ).images[0]
+    
+    # Create a grid of the depth map and result
+    grid = make_image_grid([depth_image, image], rows=1, cols=2)
+    return grid
+
+def inpaint_image(prompt, init_image, mask_image, seed, num_steps, guidance_scale, eta, strength):
+    # Initialize the pipeline
+    base_model_id = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
+    tcd_lora_id = "h1t/TCD-SDXL-LoRA"
+    
+    # Use CPU for inference
+    pipe = AutoPipelineForInpainting.from_pretrained(
+        base_model_id,
+        torch_dtype=torch.float32  # Use float32 for CPU
+    )
+    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+    
+    # Load and fuse LoRA weights
+    pipe.load_lora_weights(tcd_lora_id)
+    pipe.fuse_lora()
+    
+    # Generate the image
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        image=init_image,
+        mask_image=mask_image,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        eta=eta,
+        strength=strength,
+        generator=generator,
+    ).images[0]
+    
+    # Create a grid of the original image, mask, and result
+    grid = make_image_grid([init_image, mask_image, image], rows=1, cols=3)
+    return grid
+
+def generate_animation(prompt, seed, num_steps, guidance_scale, eta, num_frames, motion_scale):
+    # Initialize the pipeline
+    base_model_id = "frankjoshua/toonyou_beta6"
+    motion_adapter_id = "guoyww/animatediff-motion-adapter-v1-5"
+    tcd_lora_id = "h1t/TCD-SD15-LoRA"
+    motion_lora_id = "guoyww/animatediff-motion-lora-zoom-in"
+    
+    # Load motion adapter
+    adapter = MotionAdapter.from_pretrained(motion_adapter_id)
+    
+    # Initialize pipeline with CPU optimization
+    pipe = AnimateDiffPipeline.from_pretrained(
+        base_model_id,
+        motion_adapter=adapter,
+        torch_dtype=torch.float32,  # Use float32 for CPU
+        low_cpu_mem_usage=True,     # Enable low CPU memory usage
+        use_safetensors=False       # Use standard PyTorch weights
+    )
+    
+    # Set TCD scheduler
+    pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config)
+    
+    # Load LoRA weights
+    pipe.load_lora_weights(tcd_lora_id, adapter_name="tcd")
+    pipe.load_lora_weights(
+        motion_lora_id,
+        adapter_name="motion-lora"
+    )
+    
+    # Set adapter weights
+    pipe.set_adapters(["tcd", "motion-lora"], adapter_weights=[1.0, motion_scale])
+    
+    # Generate animation
+    generator = torch.Generator().manual_seed(seed)
+    frames = pipe(
+        prompt=prompt,
+        num_inference_steps=num_steps,
+        guidance_scale=guidance_scale,
+        cross_attention_kwargs={"scale": 1},
+        num_frames=num_frames,
+        eta=eta,
+        generator=generator
+    ).frames[0]
+    
+    # Export to GIF
+    gif_path = "animation.gif"
+    export_to_gif(frames, gif_path)
+    return gif_path
+
+# Create the Gradio interface
+with gr.Blocks(title="TCD-SDXL Image Generator") as demo:
+    gr.Markdown("# TCD-SDXL Image Generator")
+    gr.Markdown("Generate images using Trajectory Consistency Distillation with Stable Diffusion XL. Note: This runs on CPU, so generation may take some time.")
+    
+    with gr.Tabs():
+        with gr.TabItem("Text to Image"):
+            with gr.Row():
+                with gr.Column():
+                    text_prompt = gr.Textbox(
+                        label="Prompt",
+                        value="Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.",
+                        lines=3
+                    )
+                    text_seed = gr.Slider(minimum=0, maximum=2147483647, value=0, label="Seed", step=1)
+                    text_steps = gr.Slider(minimum=1, maximum=10, value=4, label="Number of Steps", step=1)
+                    text_guidance = gr.Slider(minimum=0, maximum=1, value=0, label="Guidance Scale")
+                    text_eta = gr.Slider(minimum=0, maximum=1, value=0.3, label="Eta")
+                    text_button = gr.Button("Generate")
+                with gr.Column():
+                    text_output = gr.Image(label="Generated Image")
+            
+            text_button.click(
+                fn=generate_image,
+                inputs=[text_prompt, text_seed, text_steps, text_guidance, text_eta],
+                outputs=text_output
+            )
+        
+        with gr.TabItem("Inpainting"):
+            with gr.Row():
+                with gr.Column():
+                    inpaint_prompt = gr.Textbox(
+                        label="Prompt",
+                        value="a tiger sitting on a park bench",
+                        lines=3
+                    )
+                    init_image = gr.Image(label="Initial Image", type="pil")
+                    mask_image = gr.Image(label="Mask Image", type="pil")
+                    inpaint_seed = gr.Slider(minimum=0, maximum=2147483647, value=0, label="Seed", step=1)
+                    inpaint_steps = gr.Slider(minimum=1, maximum=10, value=8, label="Number of Steps", step=1)
+                    inpaint_guidance = gr.Slider(minimum=0, maximum=1, value=0, label="Guidance Scale")
+                    inpaint_eta = gr.Slider(minimum=0, maximum=1, value=0.3, label="Eta")
+                    inpaint_strength = gr.Slider(minimum=0, maximum=1, value=0.99, label="Strength")
+                    inpaint_button = gr.Button("Inpaint")
+                with gr.Column():
+                    inpaint_output = gr.Image(label="Result (Original | Mask | Generated)")
+            
+            inpaint_button.click(
+                fn=inpaint_image,
+                inputs=[
+                    inpaint_prompt, init_image, mask_image, inpaint_seed,
+                    inpaint_steps, inpaint_guidance, inpaint_eta, inpaint_strength
+                ],
+                outputs=inpaint_output
+            )
+            
+        with gr.TabItem("Community Models"):
+            with gr.Row():
+                with gr.Column():
+                    community_prompt = gr.Textbox(
+                        label="Prompt",
+                        value="A man, clad in a meticulously tailored military uniform, stands with unwavering resolve. The uniform boasts intricate details, and his eyes gleam with determination. Strands of vibrant, windswept hair peek out from beneath the brim of his cap.",
+                        lines=3
+                    )
+                    model_dropdown = gr.Dropdown(
+                        choices=list(AVAILABLE_MODELS.keys()),
+                        value="Animagine XL 3.0",
+                        label="Select Model"
+                    )
+                    community_seed = gr.Slider(minimum=0, maximum=2147483647, value=0, label="Seed", step=1)
+                    community_steps = gr.Slider(minimum=1, maximum=10, value=8, label="Number of Steps", step=1)
+                    community_guidance = gr.Slider(minimum=0, maximum=1, value=0, label="Guidance Scale")
+                    community_eta = gr.Slider(minimum=0, maximum=1, value=0.3, label="Eta")
+                    community_button = gr.Button("Generate")
+                with gr.Column():
+                    community_output = gr.Image(label="Generated Image")
+            
+            community_button.click(
+                fn=generate_community_image,
+                inputs=[
+                    community_prompt, model_dropdown, community_seed,
+                    community_steps, community_guidance, community_eta
+                ],
+                outputs=community_output
+            )
+            
+        with gr.TabItem("Style Mixing"):
+            with gr.Row():
+                with gr.Column():
+                    style_prompt = gr.Textbox(
+                        label="Prompt",
+                        value="papercut of a winter mountain, snow",
+                        lines=3
+                    )
+                    style_seed = gr.Slider(minimum=0, maximum=2147483647, value=0, label="Seed", step=1)
+                    style_steps = gr.Slider(minimum=1, maximum=10, value=4, label="Number of Steps", step=1)
+                    style_guidance = gr.Slider(minimum=0, maximum=1, value=0, label="Guidance Scale")
+                    style_eta = gr.Slider(minimum=0, maximum=1, value=0.3, label="Eta")
+                    style_weight = gr.Slider(minimum=0, maximum=2, value=1.0, label="Style Weight", step=0.1)
+                    style_button = gr.Button("Generate")
+                with gr.Column():
+                    style_output = gr.Image(label="Generated Image")
+            
+            style_button.click(
+                fn=generate_style_mix,
+                inputs=[
+                    style_prompt, style_seed, style_steps,
+                    style_guidance, style_eta, style_weight
+                ],
+                outputs=style_output
+            )
+            
+        with gr.TabItem("ControlNet"):
+            with gr.Row():
+                with gr.Column():
+                    control_prompt = gr.Textbox(
+                        label="Prompt",
+                        value="stormtrooper lecture, photorealistic",
+                        lines=3
+                    )
+                    control_image = gr.Image(label="Input Image", type="pil")
+                    control_seed = gr.Slider(minimum=0, maximum=2147483647, value=0, label="Seed", step=1)
+                    control_steps = gr.Slider(minimum=1, maximum=10, value=4, label="Number of Steps", step=1)
+                    control_guidance = gr.Slider(minimum=0, maximum=1, value=0, label="Guidance Scale")
+                    control_eta = gr.Slider(minimum=0, maximum=1, value=0.3, label="Eta")
+                    control_scale = gr.Slider(minimum=0, maximum=1, value=0.5, label="ControlNet Scale", step=0.1)
+                    control_button = gr.Button("Generate")
+                with gr.Column():
+                    control_output = gr.Image(label="Result (Depth Map | Generated)")
+            
+            control_button.click(
+                fn=generate_controlnet,
+                inputs=[
+                    control_prompt, control_image, control_seed,
+                    control_steps, control_guidance, control_eta, control_scale
+                ],
+                outputs=control_output
+            )
+
+        with gr.TabItem("Animation"):
+            with gr.Row():
+                with gr.Column():
+                    anim_prompt = gr.Textbox(
+                        label="Prompt",
+                        value="best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress",
+                        lines=3
+                    )
+                    anim_seed = gr.Slider(minimum=0, maximum=2147483647, value=0, label="Seed", step=1)
+                    anim_steps = gr.Slider(minimum=1, maximum=10, value=5, label="Number of Steps", step=1)
+                    anim_guidance = gr.Slider(minimum=0, maximum=1, value=0, label="Guidance Scale")
+                    anim_eta = gr.Slider(minimum=0, maximum=1, value=0.3, label="Eta")
+                    anim_frames = gr.Slider(minimum=8, maximum=32, value=24, label="Number of Frames", step=1)
+                    anim_motion_scale = gr.Slider(minimum=0, maximum=2, value=1.2, label="Motion Scale", step=0.1)
+                    anim_button = gr.Button("Generate Animation")
+                with gr.Column():
+                    anim_output = gr.Image(label="Generated Animation", format="gif")
+            
+            anim_button.click(
+                fn=generate_animation,
+                inputs=[
+                    anim_prompt, anim_seed, anim_steps,
+                    anim_guidance, anim_eta, anim_frames,
+                    anim_motion_scale
+                ],
+                outputs=anim_output
+            )
+
+if __name__ == "__main__":
+    demo.launch()