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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os, random, glob, re, json, base64 | |
| from datetime import datetime | |
| import gradio as gr | |
| import numpy as np | |
| import PIL.Image | |
| import spaces | |
| import torch | |
| import pandas as pd | |
| from diffusers import AutoencoderKL, DiffusionPipeline | |
| DESCRIPTION = """ | |
| # 🎨 ArtForge: OpenDALLE AI Masterpiece Arena 🖼️🏆 | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. Please use <a href=\"https://huggingface.co/spaces/mrfakename/OpenDalleV1.1-GPU-Demo\">the online demo</a> instead.</p>" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1" | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1" | |
| ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0") == "1" | |
| # Global variables for metadata and likes cache | |
| image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created']) | |
| LIKES_CACHE_FILE = "likes_cache.json" | |
| def load_likes_cache(): | |
| if os.path.exists(LIKES_CACHE_FILE): | |
| with open(LIKES_CACHE_FILE, 'r') as f: | |
| return json.load(f) | |
| return {} | |
| def save_likes_cache(cache): | |
| with open(LIKES_CACHE_FILE, 'w') as f: | |
| json.dump(cache, f) | |
| likes_cache = load_likes_cache() | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| if torch.cuda.is_available(): | |
| vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) | |
| pipe = DiffusionPipeline.from_pretrained("dataautogpt3/OpenDalleV1.1", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16") | |
| if ENABLE_REFINER: | |
| refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16") | |
| if ENABLE_CPU_OFFLOAD: | |
| pipe.enable_model_cpu_offload() | |
| if ENABLE_REFINER: refiner.enable_model_cpu_offload() | |
| else: | |
| pipe.to(device) | |
| if ENABLE_REFINER: refiner.to(device) | |
| if USE_TORCH_COMPILE: | |
| pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) | |
| if ENABLE_REFINER: refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True) | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| return random.randint(0, MAX_SEED) if randomize_seed else seed | |
| def create_download_link(filename): | |
| with open(filename, "rb") as file: | |
| encoded_string = base64.b64encode(file.read()).decode('utf-8') | |
| download_link = f'<a href="data:image/png;base64,{encoded_string}" download="{filename}">Download Image</a>' | |
| return download_link | |
| def save_image(image: PIL.Image.Image, prompt: str) -> str: | |
| global image_metadata, likes_cache | |
| timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
| safe_prompt = re.sub(r'[^\w\s-]', '', prompt.lower())[:50] | |
| safe_prompt = re.sub(r'[-\s]+', '-', safe_prompt).strip('-') | |
| filename = f"{timestamp}_{safe_prompt}.png" | |
| image.save(filename) | |
| new_row = pd.DataFrame({ | |
| 'Filename': [filename], | |
| 'Prompt': [prompt], | |
| 'Likes': [0], | |
| 'Dislikes': [0], | |
| 'Hearts': [0], | |
| 'Created': [datetime.now()] | |
| }) | |
| image_metadata = pd.concat([image_metadata, new_row], ignore_index=True) | |
| likes_cache[filename] = {'likes': 0, 'dislikes': 0, 'hearts': 0} | |
| save_likes_cache(likes_cache) | |
| return filename | |
| def get_image_gallery(): | |
| global image_metadata | |
| image_files = image_metadata['Filename'].tolist() | |
| return [(file, get_image_caption(file)) for file in image_files if os.path.exists(file)] | |
| def get_image_caption(filename): | |
| global likes_cache, image_metadata | |
| if filename in likes_cache: | |
| likes = likes_cache[filename]['likes'] | |
| dislikes = likes_cache[filename]['dislikes'] | |
| hearts = likes_cache[filename]['hearts'] | |
| prompt = image_metadata[image_metadata['Filename'] == filename]['Prompt'].values[0] | |
| return f"{filename}\nPrompt: {prompt}\n👍 {likes} 👎 {dislikes} ❤️ {hearts}" | |
| return filename | |
| def delete_all_images(): | |
| global image_metadata, likes_cache | |
| for file in image_metadata['Filename']: | |
| if os.path.exists(file): | |
| os.remove(file) | |
| image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created']) | |
| likes_cache = {} | |
| save_likes_cache(likes_cache) | |
| return get_image_gallery(), image_metadata.values.tolist() | |
| def delete_image(filename): | |
| global image_metadata, likes_cache | |
| if filename and os.path.exists(filename): | |
| os.remove(filename) | |
| image_metadata = image_metadata[image_metadata['Filename'] != filename] | |
| if filename in likes_cache: | |
| del likes_cache[filename] | |
| save_likes_cache(likes_cache) | |
| return get_image_gallery(), image_metadata.values.tolist() | |
| def vote(filename, vote_type): | |
| global likes_cache | |
| if filename in likes_cache: | |
| likes_cache[filename][vote_type.lower()] += 1 | |
| save_likes_cache(likes_cache) | |
| return get_image_gallery(), image_metadata.values.tolist() | |
| def generate(prompt: str, negative_prompt: str = "", prompt_2: str = "", negative_prompt_2: str = "", use_negative_prompt: bool = False, use_prompt_2: bool = False, use_negative_prompt_2: bool = False, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale_base: float = 5.0, guidance_scale_refiner: float = 5.0, num_inference_steps_base: int = 25, num_inference_steps_refiner: int = 25, apply_refiner: bool = False, progress=gr.Progress(track_tqdm=True)) -> PIL.Image.Image: | |
| print(f"** Generating image for: \"{prompt}\" **") | |
| generator = torch.Generator().manual_seed(seed) | |
| if not use_negative_prompt: negative_prompt = None | |
| if not use_prompt_2: prompt_2 = None | |
| if not use_negative_prompt_2: negative_prompt_2 = None | |
| if not apply_refiner: | |
| image = pipe(prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, width=width, height=height, guidance_scale=guidance_scale_base, num_inference_steps=num_inference_steps_base, generator=generator, output_type="pil").images[0] | |
| else: | |
| latents = pipe(prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, width=width, height=height, guidance_scale=guidance_scale_base, num_inference_steps=num_inference_steps_base, generator=generator, output_type="latent").images | |
| image = refiner(prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, guidance_scale=guidance_scale_refiner, num_inference_steps=num_inference_steps_refiner, image=latents, generator=generator).images[0] | |
| filename = save_image(image, prompt) | |
| download_link = create_download_link(filename) | |
| return image, get_image_gallery(), download_link, image_metadata.values.tolist() | |
| examples = [ | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} painting of a majestic lighthouse on a rocky coast. Use bold brushstrokes and a vibrant color palette to capture the interplay of light and shadow as the lighthouse beam cuts through a stormy night sky.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} still life featuring a pair of vintage eyeglasses. Focus on the intricate details of the frames and lenses, using a warm color scheme to evoke a sense of nostalgia and wisdom.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} depiction of a rustic wooden stool in a sunlit artist's studio. Emphasize the texture of the wood and the interplay of light and shadow, using a mix of earthy tones and highlights.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} scene viewed through an ornate window frame. Contrast the intricate details of the window with a dreamy, soft-focus landscape beyond, using a palette that transitions from cool interior tones to warm exterior hues.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} close-up study of interlaced fingers. Use a monochromatic color scheme to emphasize the form and texture of the hands, with dramatic lighting to create depth and emotion.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} composition featuring a set of dice in motion. Capture the energy and randomness of the throw, using a dynamic color palette and blurred lines to convey movement.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} interpretation of heaven. Create an ethereal atmosphere with soft, billowing clouds and radiant light, using a palette of celestial blues, golds, and whites.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} portrayal of an ancient, mystical gate. Combine architectural details with elements of fantasy, using a rich, jewel-toned palette to create an air of mystery and magic.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} portrait of a curious cat. Focus on capturing the feline's expressive eyes and sleek form, using a mix of bold and subtle colors to bring out the cat's personality.", | |
| f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} abstract representation of toes in sand. Use textured brushstrokes to convey the feeling of warm sand, with a palette inspired by a sun-drenched beach." | |
| ] | |
| css = ''' | |
| .gradio-container {max-width: 1024px !important} | |
| h1 {text-align: center} | |
| footer {visibility: hidden} | |
| ''' | |
| theme = gr.themes.Soft() | |
| with gr.Blocks(css=css, theme=theme) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1") | |
| with gr.Tab("Generate Images"): | |
| with gr.Group(): | |
| with gr.Row(): | |
| prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False) | |
| run_button = gr.Button("Generate", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| download_link = gr.HTML(label="Download", show_label=False) | |
| with gr.Accordion("Advanced options", open=False): | |
| use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) | |
| use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False) | |
| use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False) | |
| negative_prompt = gr.Text(label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=False) | |
| prompt_2 = gr.Text(label="Prompt 2", max_lines=1, placeholder="Enter your second prompt", visible=False) | |
| negative_prompt_2 = gr.Text(label="Negative prompt 2", max_lines=1, placeholder="Enter a second 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) | |
| height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024) | |
| apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER) | |
| with gr.Row(): | |
| guidance_scale_base = gr.Slider(label="Guidance scale for base", minimum=1, maximum=20, step=0.1, value=5.0) | |
| num_inference_steps_base = gr.Slider(label="Number of inference steps for base", minimum=10, maximum=100, step=1, value=25) | |
| with gr.Row(visible=False) as refiner_params: | |
| guidance_scale_refiner = gr.Slider(label="Guidance scale for refiner", minimum=1, maximum=20, step=0.1, value=5.0) | |
| num_inference_steps_refiner = gr.Slider(label="Number of inference steps for refiner", minimum=10, maximum=100, step=1, value=25) | |
| with gr.Tab("Gallery and Voting"): | |
| image_gallery = gr.Gallery(label="Generated Images", show_label=True, columns=4, height="auto") | |
| with gr.Row(): | |
| like_button = gr.Button("👍 Like") | |
| dislike_button = gr.Button("👎 Dislike") | |
| heart_button = gr.Button("❤️ Heart") | |
| delete_image_button = gr.Button("🗑️ Delete Selected Image") | |
| selected_image = gr.State(None) | |
| with gr.Tab("Metadata and Management"): | |
| metadata_df = gr.Dataframe( | |
| label="Image Metadata", | |
| headers=["Filename", "Prompt", "Likes", "Dislikes", "Hearts", "Created"], | |
| interactive=False | |
| ) | |
| delete_all_button = gr.Button("🗑️ Delete All Images") | |
| gr.Examples(examples=examples, inputs=prompt, outputs=[result, image_gallery, download_link, metadata_df], fn=generate, cache_examples=CACHE_EXAMPLES) | |
| use_negative_prompt.change(fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt, queue=False, api_name=False) | |
| use_prompt_2.change(fn=lambda x: gr.update(visible=x), inputs=use_prompt_2, outputs=prompt_2, queue=False, api_name=False) | |
| use_negative_prompt_2.change(fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt_2, outputs=negative_prompt_2, queue=False, api_name=False) | |
| apply_refiner.change(fn=lambda x: gr.update(visible=x), inputs=apply_refiner, outputs=refiner_params, queue=False, api_name=False) | |
| prompt.submit(fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False).then( | |
| fn=generate, | |
| inputs=[prompt, negative_prompt, prompt_2, negative_prompt_2, use_negative_prompt, use_prompt_2, use_negative_prompt_2, seed, width, height, guidance_scale_base, guidance_scale_refiner, num_inference_steps_base, num_inference_steps_refiner, apply_refiner], | |
| outputs=[result, image_gallery, download_link, metadata_df] | |
| ) | |
| run_button.click(fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False).then( | |
| fn=generate, | |
| inputs=[prompt, negative_prompt, prompt_2, negative_prompt_2, use_negative_prompt, use_prompt_2, use_negative_prompt_2, seed, width, height, guidance_scale_base, guidance_scale_refiner, num_inference_steps_base, num_inference_steps_refiner, apply_refiner], | |
| outputs=[result, image_gallery, download_link, metadata_df] | |
| ) | |
| image_gallery.select(fn=lambda evt: evt, inputs=[], outputs=[selected_image]) | |
| like_button.click(fn=lambda x: vote(x, 'likes'), inputs=[selected_image], outputs=[image_gallery, metadata_df]) | |
| dislike_button.click(fn=lambda x: vote(x, 'dislikes'), inputs=[selected_image], outputs=[image_gallery, metadata_df]) | |
| heart_button.click(fn=lambda x: vote(x, 'hearts'), inputs=[selected_image], outputs=[image_gallery, metadata_df]) | |
| delete_image_button.click(fn=delete_image, inputs=[selected_image], outputs=[image_gallery, metadata_df]) | |
| delete_all_button.click(fn=delete_all_images, inputs=[], outputs=[image_gallery, metadata_df]) | |
| demo.load(fn=lambda: (get_image_gallery(), image_metadata.values.tolist()), outputs=[image_gallery, metadata_df]) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch(share=True, debug=False) |