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| #!/usr/bin/env python | |
| import os | |
| import random | |
| import uuid | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import spaces | |
| import torch | |
| from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline, StableDiffusionXLPipeline, StableDiffusionXLInpaintPipeline, EulerAncestralDiscreteScheduler, DPMSolverSinglestepScheduler | |
| from diffusers.utils import load_image | |
| DESCRIPTION = """ | |
| # [Fluently Playground](https://huggingface.co/fluently) | |
| 🤗 New FluentlyXL v4! | |
| """ | |
| if not torch.cuda.is_available(): | |
| DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>" | |
| MAX_SEED = np.iinfo(np.int32).max | |
| USE_TORCH_COMPILE = 0 | |
| ENABLE_CPU_OFFLOAD = 0 | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| if torch.cuda.is_available(): | |
| pipe_4 = StableDiffusionPipeline.from_pretrained( | |
| "fluently/Fluently-v4", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| pipe_4.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_4.scheduler.config) | |
| pipe_4.to(device) | |
| pipe_anime = StableDiffusionPipeline.from_pretrained( | |
| "fluently/Fluently-anime", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| pipe_anime.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_anime.scheduler.config) | |
| pipe_anime.to(device) | |
| pipe_epic = StableDiffusionPipeline.from_pretrained( | |
| "fluently/Fluently-epic", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| pipe_epic.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_epic.scheduler.config) | |
| pipe_epic.to(device) | |
| pipe_xl_inpaint = StableDiffusionXLInpaintPipeline.from_single_file( | |
| "https://huggingface.co/fluently/Fluently-XL-v3-inpainting/blob/main/FluentlyXL-v3-inpainting.safetensors", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| pipe_xl_inpaint.to(device) | |
| pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained( | |
| "fluently/Fluently-v4-inpainting", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| #pipe_inpaint.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_inpaint.scheduler.config) | |
| pipe_inpaint.to(device) | |
| pipe_xl = StableDiffusionXLPipeline.from_pretrained( | |
| "fluently/Fluently-XL-v4", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| pipe_xl.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_xl.scheduler.config) | |
| pipe_xl.to(device) | |
| pipe_xl_lightning = StableDiffusionXLPipeline.from_pretrained( | |
| "fluently/Fluently-XL-v3-lightning", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| ) | |
| pipe_xl_lightning.scheduler = DPMSolverSinglestepScheduler.from_config(pipe_xl_lightning.scheduler.config, use_karras_sigmas=False, timestep_spacing="trailing") | |
| pipe_xl_lightning.to(device) | |
| print("Loaded on Device!") | |
| def save_image(img): | |
| unique_name = str(uuid.uuid4()) + ".png" | |
| img.save(unique_name) | |
| return unique_name | |
| def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| return seed | |
| def get_model(model): | |
| if model == "Fluently v4 inpaint" or model == "Fluently XL v3 inpaint": | |
| return gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(interactive=True) | |
| if model == "Fluently XL v3 Lightning": | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(interactive=False) | |
| else: | |
| return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(interactive=True) | |
| def generate( | |
| model, | |
| inpaint_image, | |
| mask_image, | |
| blur_factor, | |
| strength, | |
| prompt: str, | |
| negative_prompt: str = "", | |
| use_negative_prompt: bool = False, | |
| seed: int = 0, | |
| width: int = 1024, | |
| height: int = 1024, | |
| guidance_scale: float = 3, | |
| randomize_seed: bool = False, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| seed = int(randomize_seed_fn(seed, randomize_seed)) | |
| if not use_negative_prompt: | |
| negative_prompt = "" # type: ignore | |
| if model == "Fluently v4": | |
| images = pipe_4( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=30, | |
| num_images_per_prompt=1, | |
| output_type="pil", | |
| ).images | |
| elif model == "Fluently Anime": | |
| images = pipe_anime( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=30, | |
| num_images_per_prompt=1, | |
| output_type="pil", | |
| ).images | |
| elif model == "Fluently Epic": | |
| images = pipe_epic( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=30, | |
| num_images_per_prompt=1, | |
| output_type="pil", | |
| ).images | |
| elif model == "Fluently XL v4": | |
| images = pipe_xl( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=25, | |
| num_images_per_prompt=1, | |
| output_type="pil", | |
| ).images | |
| elif model == "Fluently XL v3 Lightning": | |
| images = pipe_xl_lightning( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=2, | |
| num_inference_steps=5, | |
| num_images_per_prompt=1, | |
| output_type="pil", | |
| ).images | |
| elif model == "Fluently v4 inpaint": | |
| blurred_mask = pipe_inpaint.mask_processor.blur(mask_image, blur_factor=blur_factor) | |
| images = pipe_inpaint( | |
| prompt=prompt, | |
| image=inpaint_image, | |
| mask_image=blurred_mask, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=30, | |
| strength=strength, | |
| num_images_per_prompt=1, | |
| output_type="pil", | |
| ).images | |
| else: | |
| blurred_mask = pipe_inpaint.mask_processor.blur(mask_image, blur_factor=blur_factor) | |
| images = pipe_xl_inpaint( | |
| prompt=prompt, | |
| image=inpaint_image, | |
| mask_image=blurred_mask, | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| guidance_scale=guidance_scale, | |
| num_inference_steps=25, | |
| strength=strength, | |
| num_images_per_prompt=1, | |
| output_type="pil", | |
| ).images | |
| image_paths = [save_image(img) for img in images] | |
| print(image_paths) | |
| return image_paths, seed | |
| examples = [ | |
| "neon holography crystal cat", | |
| "a cat eating a piece of cheese", | |
| "an astronaut riding a horse in space", | |
| "a cartoon of a boy playing with a tiger", | |
| "a cute robot artist painting on an easel, concept art", | |
| "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone" | |
| ] | |
| css = ''' | |
| .gradio-container{max-width: 560px !important} | |
| h1{text-align:center} | |
| footer { | |
| visibility: hidden | |
| } | |
| ''' | |
| with gr.Blocks(title="Fluently Playground", css=css) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| gr.DuplicateButton( | |
| value="Duplicate Space for private use", | |
| elem_id="duplicate-button", | |
| visible=False, | |
| ) | |
| with gr.Row(): | |
| model = gr.Radio( | |
| label="Model", | |
| choices=["Fluently XL v3 Lightning","Fluently XL v4", "Fluently v4", "Fluently Anime", "Fluently Epic", "Fluently XL v3 inpaint", "Fluently v4 inpaint"], | |
| value="Fluently XL v3 Lightning", | |
| interactive=True, | |
| ) | |
| md_mask = gr.Markdown(""" | |
| ⚠️ To generate an inpaint mask, go [here](https://huggingface.co/spaces/stevhliu/inpaint-mask-maker). | |
| """, visible=False) | |
| inpaint_image = gr.Image(label="Inpaint Image", interactive=True, scale=5, visible=False, type="pil") | |
| mask_image = gr.Image(label="Mask Image", interactive=True, scale=5, visible=False, type="pil") | |
| blur_factor = gr.Slider(label="Mask Blur Factor", minimum=0, maximum=100, value=4, step=1, interactive=True, visible=False) | |
| strength = gr.Slider(label="Denoising Strength", minimum=0.00, maximum=1.00, value=0.70, step=0.01, interactive=True, visible=False) | |
| 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("Run", scale=0) | |
| result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False) | |
| with gr.Accordion("Advanced options", open=False): | |
| use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=6, | |
| lines=5, | |
| value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation""", | |
| placeholder="Enter a negative prompt", | |
| visible=True, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=0, | |
| visible=True | |
| ) | |
| randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| with gr.Row(visible=True): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=256, | |
| maximum=1536, | |
| step=8, | |
| value=768, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=256, | |
| maximum=1536, | |
| step=8, | |
| value=768, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=0.1, | |
| maximum=20.0, | |
| step=0.1, | |
| value=5.5, | |
| interactive=False, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=[result, seed], | |
| fn=generate, | |
| cache_examples=False, | |
| ) | |
| use_negative_prompt.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_negative_prompt, | |
| outputs=negative_prompt, | |
| api_name=False, | |
| ) | |
| model.change( | |
| fn=get_model, | |
| inputs=model, | |
| outputs=[md_mask, inpaint_image, mask_image, blur_factor, strength, guidance_scale], | |
| api_name=False, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| prompt.submit, | |
| negative_prompt.submit, | |
| run_button.click, | |
| ], | |
| fn=generate, | |
| inputs=[ | |
| model, | |
| inpaint_image, | |
| mask_image, | |
| blur_factor, | |
| strength, | |
| prompt, | |
| negative_prompt, | |
| use_negative_prompt, | |
| seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| randomize_seed, | |
| ], | |
| outputs=[result, seed], | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch(show_api=False, debug=False) |