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| 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 | |
| import gc | |
| # Available models | |
| AVAILABLE_MODELS = { | |
| "Stable Diffusion XL": "stabilityai/stable-diffusion-xl-base-1.0", | |
| "Animagine XL 3.0": "cagliostrolab/animagine-xl-3.0", | |
| } | |
| # Available LoRA styles | |
| AVAILABLE_LORAS = { | |
| "TCD": "h1t/TCD-SDXL-LoRA", | |
| "Papercut": "TheLastBen/Papercut_SDXL", | |
| } | |
| def get_device(): | |
| if torch.cuda.is_available(): | |
| return "cuda" | |
| return "cpu" | |
| def get_dtype(): | |
| if torch.cuda.is_available(): | |
| return torch.float16 | |
| return torch.float32 | |
| def cleanup_memory(): | |
| gc.collect() | |
| if torch.cuda.is_available(): | |
| torch.cuda.empty_cache() | |
| 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): | |
| try: | |
| device = get_device() | |
| dtype = get_dtype() | |
| # Initialize the pipeline | |
| base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" | |
| tcd_lora_id = "h1t/TCD-SDXL-LoRA" | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| base_model_id, | |
| torch_dtype=dtype, | |
| use_safetensors=True, | |
| variant="fp16" if device == "cuda" else None | |
| ).to(device) | |
| pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) | |
| # Load and fuse LoRA weights with prefix=None | |
| pipe.load_lora_weights(tcd_lora_id, prefix=None) | |
| pipe.fuse_lora() | |
| # Generate the image | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image = pipe( | |
| prompt=prompt, | |
| num_inference_steps=num_steps, | |
| guidance_scale=guidance_scale, | |
| eta=eta, | |
| generator=generator, | |
| ).images[0] | |
| # Cleanup | |
| del pipe | |
| cleanup_memory() | |
| return image, "Image generated successfully!" | |
| except Exception as e: | |
| cleanup_memory() | |
| return None, f"Error generating image: {str(e)}" | |
| def generate_community_image(prompt, model_name, seed, num_steps, guidance_scale, eta): | |
| try: | |
| device = get_device() | |
| dtype = get_dtype() | |
| # Initialize the pipeline | |
| base_model_id = AVAILABLE_MODELS[model_name] | |
| tcd_lora_id = "h1t/TCD-SDXL-LoRA" | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| base_model_id, | |
| torch_dtype=dtype, | |
| use_safetensors=True, | |
| ).to(device) | |
| pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) | |
| # Load and fuse LoRA weights with prefix=None | |
| pipe.load_lora_weights(tcd_lora_id, prefix=None) | |
| pipe.fuse_lora() | |
| # Generate the image | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image = pipe( | |
| prompt=prompt, | |
| num_inference_steps=num_steps, | |
| guidance_scale=guidance_scale, | |
| eta=eta, | |
| generator=generator, | |
| ).images[0] | |
| # Cleanup | |
| del pipe | |
| cleanup_memory() | |
| return image, "Image generated successfully!" | |
| except Exception as e: | |
| cleanup_memory() | |
| return None, f"Error generating image: {str(e)}" | |
| def generate_style_mix(prompt, seed, num_steps, guidance_scale, eta, style_weight): | |
| try: | |
| device = get_device() | |
| dtype = get_dtype() | |
| # 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" | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| base_model_id, | |
| torch_dtype=dtype, | |
| use_safetensors=True, | |
| variant="fp16" if device == "cuda" else None | |
| ).to(device) | |
| pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) | |
| # Load multiple LoRA weights with prefix=None | |
| pipe.load_lora_weights(tcd_lora_id, adapter_name="tcd", prefix=None) | |
| pipe.load_lora_weights(styled_lora_id, adapter_name="style", prefix=None) | |
| # Set adapter weights | |
| pipe.set_adapters(["tcd", "style"], adapter_weights=[1.0, style_weight]) | |
| # Generate the image | |
| generator = torch.Generator(device=device).manual_seed(seed) | |
| image = pipe( | |
| prompt=prompt, | |
| num_inference_steps=num_steps, | |
| guidance_scale=guidance_scale, | |
| eta=eta, | |
| generator=generator, | |
| ).images[0] | |
| # Cleanup | |
| del pipe | |
| cleanup_memory() | |
| return image, "Image generated successfully!" | |
| except Exception as e: | |
| cleanup_memory() | |
| return None, f"Error generating image: {str(e)}" | |
| def generate_controlnet(prompt, init_image, seed, num_steps, guidance_scale, eta, controlnet_scale): | |
| try: | |
| device = get_device() | |
| dtype = get_dtype() | |
| # 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=dtype, | |
| use_safetensors=True, | |
| variant="fp16" if device == "cuda" else None | |
| ).to(device) | |
| # Initialize pipeline | |
| pipe = StableDiffusionXLControlNetPipeline.from_pretrained( | |
| base_model_id, | |
| controlnet=controlnet, | |
| torch_dtype=dtype, | |
| use_safetensors=True, | |
| variant="fp16" if device == "cuda" else None | |
| ).to(device) | |
| pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) | |
| # Load and fuse LoRA weights with prefix=None | |
| pipe.load_lora_weights(tcd_lora_id, prefix=None) | |
| pipe.fuse_lora() | |
| # Generate depth map | |
| depth_image = get_depth_map(init_image) | |
| # Generate the image | |
| generator = torch.Generator(device=device).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) | |
| # Cleanup | |
| del pipe, controlnet | |
| cleanup_memory() | |
| return grid, "Image generated successfully!" | |
| except Exception as e: | |
| cleanup_memory() | |
| return None, f"Error generating image: {str(e)}" | |
| def inpaint_image(prompt, init_image, mask_image, seed, num_steps, guidance_scale, eta, strength): | |
| try: | |
| device = get_device() | |
| dtype = get_dtype() | |
| # Initialize the pipeline | |
| base_model_id = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1" | |
| tcd_lora_id = "h1t/TCD-SDXL-LoRA" | |
| pipe = AutoPipelineForInpainting.from_pretrained( | |
| base_model_id, | |
| torch_dtype=dtype, | |
| use_safetensors=True, | |
| variant="fp16" if device == "cuda" else None | |
| ).to(device) | |
| pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) | |
| # Load and fuse LoRA weights with prefix=None | |
| pipe.load_lora_weights(tcd_lora_id, prefix=None) | |
| pipe.fuse_lora() | |
| # Generate the image | |
| generator = torch.Generator(device=device).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] | |
| # Cleanup | |
| del pipe | |
| cleanup_memory() | |
| # Return individual images instead of a grid | |
| return init_image, mask_image, image, "Image generated successfully!" | |
| except Exception as e: | |
| cleanup_memory() | |
| return None, None, None, f"Error generating image: {str(e)}" | |
| def generate_animation(prompt, seed, num_steps, guidance_scale, eta, num_frames, motion_scale): | |
| try: | |
| device = get_device() | |
| dtype = get_dtype() | |
| # 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).to(device) | |
| # Initialize pipeline with optimization | |
| pipe = AnimateDiffPipeline.from_pretrained( | |
| base_model_id, | |
| motion_adapter=adapter, | |
| torch_dtype=dtype, | |
| low_cpu_mem_usage=True, | |
| use_safetensors=True, | |
| variant="fp16" if device == "cuda" else None | |
| ).to(device) | |
| # Set TCD scheduler | |
| pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) | |
| # Load LoRA weights with prefix=None | |
| pipe.load_lora_weights(tcd_lora_id, adapter_name="tcd", prefix=None) | |
| pipe.load_lora_weights( | |
| motion_lora_id, | |
| adapter_name="motion-lora", | |
| prefix=None | |
| ) | |
| # Set adapter weights | |
| pipe.set_adapters(["tcd", "motion-lora"], adapter_weights=[1.0, motion_scale]) | |
| # Generate animation | |
| generator = torch.Generator(device=device).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) | |
| # Cleanup | |
| del pipe, adapter | |
| cleanup_memory() | |
| return gif_path, "Animation generated successfully!" | |
| except Exception as e: | |
| cleanup_memory() | |
| return None, f"Error generating animation: {str(e)}" | |
| # 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. ") | |
| 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") | |
| text_status = gr.Textbox(label="Status", interactive=False) | |
| 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, text_status], | |
| api_name="generate_image" | |
| ) | |
| 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") | |
| inpaint_status = gr.Textbox(label="Status", interactive=False) | |
| with gr.Column(): | |
| # Display individual images in a row | |
| with gr.Row(): | |
| inpaint_output_original = gr.Image(label="Original") | |
| inpaint_output_mask = gr.Image(label="Mask") | |
| inpaint_output_generated = gr.Image(label="Generated") | |
| inpaint_button.click( | |
| fn=inpaint_image, | |
| inputs=[ | |
| inpaint_prompt, init_image, mask_image, inpaint_seed, | |
| inpaint_steps, inpaint_guidance, inpaint_eta, inpaint_strength | |
| ], | |
| # Map function outputs to individual image components and status | |
| outputs=[inpaint_output_original, inpaint_output_mask, inpaint_output_generated, inpaint_status] | |
| ) | |
| 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_status = gr.Textbox(label="Status", interactive=False) | |
| community_button.click( | |
| fn=generate_community_image, | |
| inputs=[ | |
| community_prompt, model_dropdown, community_seed, | |
| community_steps, community_guidance, community_eta | |
| ], | |
| outputs=[community_output, community_status] | |
| ) | |
| 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_status = gr.Textbox(label="Status", interactive=False) | |
| style_button.click( | |
| fn=generate_style_mix, | |
| inputs=[ | |
| style_prompt, style_seed, style_steps, | |
| style_guidance, style_eta, style_weight | |
| ], | |
| outputs=[style_output, style_status] | |
| ) | |
| 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_status = gr.Textbox(label="Status", interactive=False) | |
| control_button.click( | |
| fn=generate_controlnet, | |
| inputs=[ | |
| control_prompt, control_image, control_seed, | |
| control_steps, control_guidance, control_eta, control_scale | |
| ], | |
| outputs=[control_output, control_status] | |
| ) | |
| 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") | |
| anim_status = gr.Textbox(label="Status", interactive=False) | |
| 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, anim_status] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |