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Update app.py
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app.py
CHANGED
@@ -1,70 +1,119 @@
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import gradio as gr
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import numpy as np
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import
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from diffusers import DiffusionPipeline
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from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler
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import torch
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import spaces
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pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash")
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pipe = pipe.to(device)
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pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing")
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MAX_SEED = np.iinfo(np.int32).max
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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examples = [
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"
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"
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"
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"
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"
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]
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css = '''
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.gradio-container{max-width: 560px !important}
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h1{text-align:center}
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footer {
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visibility: hidden
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}
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'''
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with gr.
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gr.Markdown("""# SDXL Flash
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### Super fast text to Image Generator.
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### <span style='color: red;'>You may change the steps from 5 to 8 or 10, if you didn't get satisfied results.
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### First Image processing takes time then images generate faster.""")
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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placeholder="Enter your prompt",
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container=False,
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)
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run_button = gr.Button("Run", scale=0)
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=
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lines=4,
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placeholder="Enter a negative prompt",
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value
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)
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label="
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minimum=0,
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maximum=
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step=1,
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value=
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=8,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=8,
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value=1024,
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=1.0,
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maximum=6.0,
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step=0.1,
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value=3.0,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=15,
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step=1,
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value=5,
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)
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gr.Examples(
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examples = examples,
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inputs = prompt,
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outputs = result,
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fn=infer,
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cache_examples=True
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)
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inputs
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outputs
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)
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import os
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import random
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import uuid
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import json
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import gradio as gr
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import numpy as np
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from PIL import Image
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import spaces
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import torch
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from diffusers import DiffusionPipeline
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DESCRIPTION = """# SDXL Flash"""
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if not torch.cuda.is_available():
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
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MAX_SEED = np.iinfo(np.int32).max
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
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USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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NUM_IMAGES_PER_PROMPT = 1
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if torch.cuda.is_available():
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pipe = DiffusionPipeline.from_pretrained(
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"sd-community/sdxl-flash",
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torch_dtype=torch.float16,
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use_safetensors=True,
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add_watermarker=False,
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variant="fp16"
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)
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if ENABLE_CPU_OFFLOAD:
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pipe.enable_model_cpu_offload()
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else:
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pipe.to(device)
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print("Loaded on Device!")
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if USE_TORCH_COMPILE:
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
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pipe2.unet = torch.compile(pipe2.unet, mode="reduce-overhead", fullgraph=True)
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print("Model Compiled!")
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def save_image(img):
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unique_name = str(uuid.uuid4()) + ".png"
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img.save(unique_name)
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return unique_name
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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return seed
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@spaces.GPU(enable_queue=False)
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def generate(
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prompt: str,
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negative_prompt: str = "",
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use_negative_prompt: bool = False,
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seed: int = 0,
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width: int = 1024,
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height: int = 1024,
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guidance_scale: float = 3,
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num_inference_steps: int = 9,
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randomize_seed: bool = False,
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use_resolution_binning: bool = True,
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progress=gr.Progress(track_tqdm=True),
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):
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pipe.to(device)
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seed = int(randomize_seed_fn(seed, randomize_seed))
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generator = torch.Generator().manual_seed(seed)
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if not use_negative_prompt:
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negative_prompt = "" # type: ignore
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negative_prompt += default_negative
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options = {
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"prompt":prompt,
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"negative_prompt":negative_prompt,
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"width":width,
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"height":height,
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"guidance_scale":guidance_scale,
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"num_inference_steps":num_inference_steps,
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"generator":generator,
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"num_images_per_prompt":NUM_IMAGES_PER_PROMPT,
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"use_resolution_binning":use_resolution_binning,
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"output_type":"pil",
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}
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images = pipe(**options).images
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image_paths = [save_image(img) for img in images]
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return image_paths, seed
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examples = [
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"neon holography crystal cat",
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"a cat eating a piece of cheese",
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"an astronaut riding a horse in space",
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"a cartoon of a boy playing with a tiger",
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"a cute robot artist painting on an easel, concept art",
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#"a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
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]
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css = '''
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.gradio-container{max-width: 560px !important}
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h1{text-align:center}
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'''
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with gr.Blocks(css=css) as demo:
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gr.Markdown(DESCRIPTION)
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with gr.Group():
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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placeholder="Enter your prompt",
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container=False,
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)
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run_button = gr.Button("Run", scale=0)
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result = gr.Gallery(label="Result", columns=NUM_IMAGES_PER_PROMPT, show_label=False)
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with gr.Accordion("Advanced options", open=False):
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with gr.Row():
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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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",
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visible=True,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row(visible=True):
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024,
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance Scale",
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minimum=0.1,
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maximum=6,
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step=0.1,
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value=3.0,
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=15,
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step=1,
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value=5,
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)
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gr.Examples(
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examples=examples,
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inputs=prompt,
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outputs=[result, seed],
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fn=generate,
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cache_examples=CACHE_EXAMPLES,
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)
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use_negative_prompt.change(
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fn=lambda x: gr.update(visible=x),
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inputs=use_negative_prompt,
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outputs=negative_prompt,
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api_name=False,
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)
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gr.on(
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triggers=[
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prompt.submit,
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negative_prompt.submit,
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run_button.click,
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],
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fn=generate,
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inputs=[
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prompt,
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negative_prompt,
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use_negative_prompt,
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seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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randomize_seed,
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],
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outputs=[result, seed],
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api_name="run",
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)
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if __name__ == "__main__":
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demo.queue(max_size=20).launch()
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