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Update app.py
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app.py
CHANGED
@@ -8,7 +8,7 @@ import gradio as gr
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import numpy as np
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import uuid
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import spaces
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from diffusers import ConsistencyDecoderVAE, DPMSolverMultistepScheduler, Transformer2DModel, AutoencoderKL
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import torch
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from typing import Tuple
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from datetime import datetime
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@@ -93,7 +93,7 @@ style_list = [
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
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STYLE_NAMES = list(styles.keys())
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DEFAULT_STYLE_NAME = "Realistic"
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SCHEDULE_NAME = ["DPM-Solver"
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DEFAULT_SCHEDULE_NAME = "DPM-Solver"
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NUM_IMAGES_PER_PROMPT = 1
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@@ -169,9 +169,7 @@ def generate(
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height: int = 400,
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schedule: str = 'DPM-Solver',
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dpms_guidance_scale: float = 3.5,
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sas_guidance_scale: float = 3,
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dpms_inference_steps: int = 9,
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sas_inference_steps: int = 25,
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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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@@ -184,11 +182,6 @@ def generate(
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pipe.scheduler = DPMSolverMultistepScheduler()
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num_inference_steps = dpms_inference_steps
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guidance_scale = dpms_guidance_scale
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elif schedule == "SA-Solver":
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if not isinstance(pipe.scheduler, SASolverScheduler):
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pipe.scheduler = SASolverScheduler.from_config(pipe.scheduler.config, algorithm_type='data_prediction', tau_func=lambda t: 1 if 200 <= t <= 800 else 0, predictor_order=2, corrector_order=2)
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num_inference_steps = sas_inference_steps
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guidance_scale = sas_guidance_scale
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else:
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raise ValueError(f"Unknown schedule: {schedule}")
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@@ -217,16 +210,6 @@ def generate(
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examples = [
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"A Monkey with a happy face in the Sahara desert.",
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"Eiffel Tower was Made up of ICE to look like a cloud, with the bell tower at the top of the building.",
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"3D small, round, fluffy creature with big, expressive eyes explores a vibrant, enchanted forest. The creature, a whimsical blend of a rabbit and a squirrel, has soft blue fur and a bushy, striped tail. It hops along a sparkling stream, its eyes wide with wonder. The forest is alive with magical elements: flowers that glow and change colors, trees with leaves in shades of purple and silver, and small floating lights that resemble fireflies. The creature stops to interact playfully with a group of tiny, fairy-like beings dancing around a mushroom ring. The creature looks up in awe at a large, glowing tree that seems to be the heart of the forest.",
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"Color photo of a corgi made of transparent glass, standing on the riverside in Yosemite National Park.",
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"A close-up photo of a woman. She wore a blue coat with a gray dress underneath. She has blue eyes and blond hair, and wears a pair of earrings. Behind are blurred city buildings and streets.",
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"A litter of golden retriever puppies playing in the snow. Their heads pop out of the snow, covered in.",
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"a handsome young boy in the middle with sky color background wearing eye glasses, it's super detailed with anime style, it's a portrait with delicated eyes and nice looking face",
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"an astronaut sitting in a diner, eating fries, cinematic, analog film",
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"Pirate ship trapped in a cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, intricate detail.",
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"professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.",
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"Outside View from Hotel Made up of Chocolate in space.",
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]
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with gr.Blocks(css="style.css") as demo:
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@@ -355,9 +338,7 @@ with gr.Blocks(css="style.css") as demo:
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height,
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schedule,
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dpms_guidance_scale,
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sas_guidance_scale,
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dpms_inference_steps,
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sas_inference_steps,
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randomize_seed,
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],
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outputs=[result, seed],
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import numpy as np
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import uuid
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import spaces
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from diffusers import ConsistencyDecoderVAE, DPMSolverMultistepScheduler, Transformer2DModel, AutoencoderKL
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import torch
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from typing import Tuple
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from datetime import datetime
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
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STYLE_NAMES = list(styles.keys())
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DEFAULT_STYLE_NAME = "Realistic"
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SCHEDULE_NAME = ["DPM-Solver"]
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DEFAULT_SCHEDULE_NAME = "DPM-Solver"
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NUM_IMAGES_PER_PROMPT = 1
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height: int = 400,
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schedule: str = 'DPM-Solver',
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dpms_guidance_scale: float = 3.5,
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dpms_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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pipe.scheduler = DPMSolverMultistepScheduler()
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num_inference_steps = dpms_inference_steps
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guidance_scale = dpms_guidance_scale
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else:
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raise ValueError(f"Unknown schedule: {schedule}")
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examples = [
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"A Monkey with a happy face in the Sahara desert.",
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]
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with gr.Blocks(css="style.css") as demo:
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height,
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schedule,
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dpms_guidance_scale,
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dpms_inference_steps,
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randomize_seed,
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],
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outputs=[result, seed],
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