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| import os | |
| import random | |
| import uuid | |
| import json | |
| import gradio as gr | |
| import numpy as np | |
| from PIL import Image | |
| import spaces | |
| import torch | |
| from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler | |
| 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 | |
| CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1" | |
| MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096")) | |
| USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" | |
| ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| pipe = StableDiffusionXLPipeline.from_pretrained( | |
| "sd-community/sdxl-flash", | |
| torch_dtype=torch.float16, | |
| use_safetensors=True, | |
| add_watermarker=False | |
| ) | |
| pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
| 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 generate( | |
| prompt: str, | |
| style: str = "BEST", | |
| negative_prompt: str = "", | |
| use_negative_prompt: bool = False, | |
| seed: int = 0, | |
| width: int = 1024, | |
| height: int = 1024, | |
| guidance_scale: float = 3, | |
| num_inference_steps: int = 25, | |
| randomize_seed: bool = False, | |
| use_resolution_binning: bool = True, | |
| progress=gr.Progress(track_tqdm=True), | |
| ): | |
| if style=="BEST" : | |
| pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle2") | |
| pipe.load_lora_weights("ehristoforu/dalle-3-xl", weight_name="dalle-3-xl-lora-v1.safetensors", adapter_name="dalle1") | |
| pipe.set_adapters(["dalle2","dalle1"], adapter_weights=[0.7, 0.3]) | |
| elif style=="Origami": | |
| pipe.load_lora_weights("RalFinger/origami-style-sdxl-lora", weight_name="ral-orgmi-sdxl.safetensors", adapter_name="origami") | |
| pipe.set_adapters(["origami"], adapter_weights=[2]) | |
| elif style=="3D": | |
| pipe.load_lora_weights("artificialguybr/3DRedmond-V1", weight_name="3DRedmond-3DRenderStyle-3DRenderAF.safetensors", adapter_name="dalle2") | |
| pipe.load_lora_weights("goofyai/3d_render_style_xl", weight_name="3d_render_style_xl.safetensors", adapter_name="dalle1") | |
| pipe.set_adapters(["dalle2","dalle1"], adapter_weights=[1.1, 0.8]) | |
| elif style=="PixelART": | |
| pipe.load_lora_weights("artificialguybr/PixelArtRedmond", weight_name="PixelArtRedmond-Lite64.safetensors", adapter_name="lora") | |
| pipe.load_lora_weights("nerijs/pixel-art-xl", weight_name="pixel-art-xl.safetensors", adapter_name="pixel") | |
| pipe.set_adapters(["lora", "pixel"], adapter_weights=[1.0, 1.2]) | |
| elif style=="Logo": | |
| pipe.load_lora_weights("artificialguybr/StickersRedmond", weight_name="StickersRedmond.safetensors", adapter_name="lora") | |
| pipe.load_lora_weights("artificialguybr/LogoRedmond-LogoLoraForSDXL", weight_name="LogoRedmond_LogoRedAF.safetensors", adapter_name="pixel") | |
| pipe.set_adapters(["lora", "pixel"], adapter_weights=[0.5, 1.2]) | |
| else: | |
| pipe.load_lora_weights() | |
| pipe.to("cuda") | |
| seed = int(randomize_seed_fn(seed, randomize_seed)) | |
| generator = torch.Generator().manual_seed(seed) | |
| options = { | |
| "prompt":prompt, | |
| "style":style, | |
| "negative_prompt":negative_prompt, | |
| "width":width, | |
| "height":height, | |
| "guidance_scale":guidance_scale, | |
| "num_inference_steps":num_inference_steps, | |
| "generator":generator, | |
| "use_resolution_binning":use_resolution_binning, | |
| "output_type":"pil", | |
| } | |
| images = pipe(**options).images | |
| image_paths = [save_image(img) for img in images] | |
| return image_paths, seed | |
| examples = [ | |
| "a cat eating a piece of cheese", | |
| "a ROBOT riding a BLUE horse on Mars, photorealistic", | |
| "a cartoon of a IRONMAN fighting with HULK, wall painting", | |
| "a cute robot artist painting on an easel, concept art", | |
| "Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k", | |
| "An alien grasping a sign board contain word 'Flash', futuristic, neonpunk, detailed", | |
| "Kids going to school, Anime style" | |
| ] | |
| css = ''' | |
| .gradio-container{max-width: 560px !important} | |
| h1{text-align:center} | |
| footer { | |
| visibility: hidden | |
| } | |
| ''' | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown("""# SDXL Flash | |
| ### First Image processing takes time then images generate faster.""") | |
| 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) | |
| with gr.Row(): | |
| style = gr.Radio(choices=["Default","BEST","3D", "PixelART","Logo","Origami"],label="Style", value="Default", interactive=True) | |
| with gr.Accordion("Advanced options", open=False): | |
| with gr.Row(): | |
| use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
| negative_prompt = gr.Text( | |
| label="Negative prompt", | |
| max_lines=5, | |
| lines=4, | |
| placeholder="Enter a negative prompt", | |
| 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, NSFW", | |
| visible=True, | |
| ) | |
| 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(visible=True): | |
| width = gr.Slider( | |
| label="Width", | |
| minimum=512, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=64, | |
| value=1024, | |
| ) | |
| height = gr.Slider( | |
| label="Height", | |
| minimum=512, | |
| maximum=MAX_IMAGE_SIZE, | |
| step=64, | |
| value=1024, | |
| ) | |
| with gr.Row(): | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| minimum=0.1, | |
| maximum=6, | |
| step=0.1, | |
| value=3.0, | |
| ) | |
| num_inference_steps = gr.Slider( | |
| label="Number of inference steps", | |
| minimum=1, | |
| maximum=15, | |
| step=1, | |
| value=8, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=prompt, | |
| outputs=[result, seed], | |
| fn=generate, | |
| cache_examples=CACHE_EXAMPLES, | |
| ) | |
| use_negative_prompt.change( | |
| fn=lambda x: gr.update(visible=x), | |
| inputs=use_negative_prompt, | |
| outputs=negative_prompt, | |
| api_name=False, | |
| ) | |
| gr.on( | |
| triggers=[ | |
| prompt.submit, | |
| negative_prompt.submit, | |
| run_button.click, | |
| ], | |
| fn=generate, | |
| inputs=[ | |
| prompt, | |
| style, | |
| negative_prompt, | |
| use_negative_prompt, | |
| seed, | |
| width, | |
| height, | |
| guidance_scale, | |
| num_inference_steps, | |
| randomize_seed, | |
| ], | |
| outputs=[result, seed], | |
| api_name="run", | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue(max_size=20).launch() |