Update app.py

app.py

@@ -5,7 +5,6 @@
 # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 # copies of the Software, and to permit persons to whom the Software is
 import spaces
-
 import os
 import random
 import uuid
@@ -13,12 +12,8 @@ import gradio as gr
 import numpy as np
 from PIL import Image
 import torch
-#import torch._dynamo
-#import diffusers
 from diffusers import AutoencoderKL, StableDiffusionXLPipeline
 from diffusers import EulerAncestralDiscreteScheduler
-#from diffusers import DPMSolverSDEScheduler
-
 from typing import Tuple
 import paramiko
 import gc
@@ -95,7 +90,6 @@ DEFAULT_STYLE_NAME = "Style Zero"
 STYLE_NAMES = list(styles.keys())
 HF_TOKEN = os.getenv("HF_TOKEN")
 
-#sampling_schedule = AysSchedules["StableDiffusionXLTimesteps"]
 upscaler = UpscaleWithModel.from_pretrained("Kim2091/ClearRealityV1").to(torch.device("cuda:0"))
 
 def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
@@ -108,53 +102,19 @@ def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str
     return p.replace("{prompt}", positive), n + negative
 
 def load_and_prepare_model():
-    #vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", safety_checker=None)
     vaeX = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", safety_checker=None,use_safetensors=False,token=HF_TOKEN)
-    #vaeRV = AutoencoderKL.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='vae', safety_checker=None,token=HF_TOKEN)
-    #vae = AutoencoderKL.from_single_file('https://huggingface.co/ford442/sdxl-vae-bf16/mySLR/myslrVAE_v10.safetensors')
-    #vaeX = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae')   #  ,use_safetensors=True  FAILS
-    #unetX = UNet2DConditionModel.from_pretrained('ford442/RealVisXL_V5.0_BF16',subfolder='unet').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
-    #sched = EulerAncestralDiscreteScheduler.from_pretrained("SG161222/RealVisXL_V5.0", subfolder='scheduler',beta_schedule="scaled_linear", steps_offset=1,timestep_spacing="trailing"))
     sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1,use_karras_sigmas=True)
-    #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear")
-    #sched = DPMSolverSDEScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler')
-    #pipeX = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V5.0").to(torch.bfloat16)
-    #pipeX = StableDiffusionXLPipeline.from_pretrained("ford442/Juggernaut-XI-v11-fp32",use_safetensors=True)
     pipe = StableDiffusionXLPipeline.from_pretrained(
-        #'ford442/RealVisXL_V5.0_BF16',
-       #'ford442/Juggernaut-XI-v11-fp32',
-      #   'SG161222/RealVisXL_V5.0',
         #'John6666/uber-realistic-porn-merge-ponyxl-urpm-ponyxlhybridv1-sdxl',
         #'John6666/uber-realistic-porn-merge-xl-urpmxl-v3-sdxl',
         'John6666/uber-realistic-porn-merge-xl-urpmxl-v6final-sdxl',
-        #'John6666/big-lust-v16-sdxl',
-        #'John6666/photo-realistic-pony-v5-sdxl',
         #'John6666/pornworks-real-porn-ponyv04-sdxl',
         #'John6666/pornmaster-amateur-sdxlv1vae-sdxl',
         #torch_dtype=torch.bfloat16,
         add_watermarker=False,
         token=HF_TOKEN,
-       # custom_pipeline="lpw_stable_diffusion_xl",
         #use_safetensors=True,
-     #  vae=AutoencoderKL.from_pretrained("BeastHF/MyBack_SDXL_Juggernaut_XL_VAE/MyBack_SDXL_Juggernaut_XL_VAE_V10(version_X).safetensors",repo_type='model',safety_checker=None),
-     #   vae=AutoencoderKL.from_pretrained("stabilityai/sdxl-vae",repo_type='model',safety_checker=None, torch_dtype=torch.float32),
-       # vae=AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16",repo_type='model',safety_checker=None),
-        #vae=vae,
-        #unet=pipeX.unet,
-        #scheduler = sched,
-      #  scheduler = EulerAncestralDiscreteScheduler.from_config(pipeX.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
-        #scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset =1)
     )
-    #sched = EulerAncestralDiscreteScheduler.from_pretrained('SG161222/RealVisXL_V5.0',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear") #, beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
-    #sched = EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16',token=HF_TOKEN, subfolder='scheduler',beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1) #,use_karras_sigmas=True)
-    #pipe.vae = AsymmetricAutoencoderKL.from_pretrained('cross-attention/asymmetric-autoencoder-kl-x-2').to(torch.bfloat16)   #  ,use_safetensors=True  FAILS
-            
-    #pipe.vae = AutoencoderKL.from_pretrained('ford442/Juggernaut-XI-v11-fp32',subfolder='vae')   #  ,use_safetensors=True  FAILS
-    
-    #pipe.vae = AutoencoderKL.from_pretrained('stabilityai/sdxl-vae-bf16',subfolder='vae')  
-    #pipe.vae = AutoencoderKL.from_pretrained('stabilityai/sdxl-vae',subfolder='vae',force_upcast=False,scaling_factor= 0.182158767676)  
-    #pipe.vae.to(torch.bfloat16)
-
    
     '''
     scaling_factor (`float`, *optional*, defaults to 0.18215):
@@ -175,41 +135,22 @@ def load_and_prepare_model():
     #pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained('SG161222/RealVisXL_V5.0', subfolder='scheduler', algorithm_type='sde-dpmsolver++')
 
     #****  NEVER TO BFLOAT BEFORE CUDA****#
-    pipe.vae = vaeX.to(torch.bfloat16)
+    pipe.vae = vaeX #.to(torch.bfloat16)
     #****  NEVER TO BFLOAT BEFORE CUDA****#
 
-    #pipe.unet = unetX
-    
-    #pipe.vae.do_resize=False
-    #pipe.vae.do_rescale=False
-    #pipe.vae.do_convert_rgb=True
-    #pipe.vae.vae_scale_factor=8
+    #pipe.vae.force_upcast
 
     pipe.scheduler = sched
-    #pipe.vae=vae.to(torch.bfloat16)
-    #pipe.unet=pipeX.unet
-    #pipe.scheduler=EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config, beta_schedule="scaled_linear", beta_start=0.00085, beta_end=0.012, steps_offset=1)
-    #pipe.scheduler=EulerAncestralDiscreteScheduler.from_pretrained('ford442/RealVisXL_V5.0_BF16', subfolder='scheduler',beta_schedule="scaled_linear")
     # pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/Fantasy_World_XL.safetensors", adapter_name="fantasy")
     pipe.vae.set_default_attn_processor()  # Set attention processor first
 
-    #pipe.load_lora_weights("Keltezaa/flux_pussy_NSFW", weight_name="flux-pussy.safetensors", adapter_name="vagina", low_cpu_mem_usage=False,token=HF_TOKEN)
-    #pipe.load_lora_weights("xey/sldr_flux_nsfw_v2-studio", weight_name="sldr_flux_nsfw_v2-studio.safetensors", adapter_name="nsfw", low_cpu_mem_usage=False,token=HF_TOKEN)
     pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/skin_texture_style_v4.safetensors", adapter_name="skin", low_cpu_mem_usage=False,token=HF_TOKEN)
-    #pipe.unet.load_lora_adapter("ford442/sdxl-vae-bf16", weight_name="LoRA/skin_texture_style_v4.safetensors", prefix="unet")
-    #pipe.text_encoder.load_lora_adapter("ford442/sdxl-vae-bf16", weight_name="LoRA/skin_texture_style_v4.safetensors", prefix="text_encoder")
     
     #pipe.unet = pipe.unet.to(memory_format=torch.contiguous_format)
-    
-        #Some typical diffusers pipeline optimizations
-    #pipe.unet.to(memory_format=torch.channels_last) #Unsupported by hidet, but does not seem to make a difference if disabled.
+    #pipe.unet.to(memory_format=torch.channels_last)
     #pipe.enable_vae_tiling()
     #pipe.enable_xformers_memory_efficient_attention()
-
     
-    #pipe.load_lora_weights("ford442/sdxl-vae-bf16", weight_name="LoRA/epicNewPhoto.safetensors", adapter_name="photo")
-    #pipe.set_adapters(["skin", "vagina", "nsfw"], adapter_weights=[0.75, 0.25, 0.5])
-    #pipe.set_adapters(["skin", "photo", "fantasy"], adapter_weights=[0.75, 0.25, 0.5])
     #pipe.set_adapters(["skin"], adapter_weights=[0.5])    
     #pipe.unet.set_default_attn_processor()
     
@@ -229,8 +170,6 @@ def load_and_prepare_model():
     #print(f'UNET: {pipe.unet}')
     pipe.watermark=None
     pipe.safety_checker=None  
-
-    #sched = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config, beta_schedule="linear", algorithm_type="dpmsolver++")
     return pipe
     
 pipe = load_and_prepare_model()
@@ -307,7 +246,6 @@ def generate_30(
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
         "generator": generator,
-     #   "timesteps": sampling_schedule,
         "output_type": "pil",
     }
     if use_resolution_binning:
@@ -353,7 +291,6 @@ def generate_60(
     #pipe.set_adapters(["skin"], adapter_weights=[lora_scale])
     seed = int(randomize_seed_fn())
     generator = torch.Generator(device='cuda').manual_seed(seed)
-    #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
     options = {
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
@@ -363,7 +300,6 @@ def generate_60(
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
         "generator": generator,
-     #   "timesteps": sampling_schedule,
         "output_type": "pil",
     }
     if use_resolution_binning:
@@ -378,8 +314,6 @@ def generate_60(
     rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)
     image_paths = save_image(rv_image)
-    #torch.cuda.empty_cache()
-    #gc.collect()
     torch.set_float32_matmul_precision("medium")
     with torch.no_grad():
         upscale = upscaler(rv_image, tiling=True, tile_width=256, tile_height=256)
@@ -409,7 +343,6 @@ def generate_90(
     #pipe.set_adapters(["skin"], adapter_weights=[lora_scale])
     seed = int(randomize_seed_fn())
     generator = torch.Generator(device='cuda').manual_seed(seed)
-    #prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
     options = {
         "prompt": [prompt],
         "negative_prompt": [negative_prompt],
@@ -419,7 +352,6 @@ def generate_90(
         "guidance_scale": guidance_scale,
         "num_inference_steps": num_inference_steps,
         "generator": generator,
-     #   "timesteps": sampling_schedule,
         "output_type": "pil",
     }
     if use_resolution_binning:
@@ -434,8 +366,6 @@ def generate_90(
     rv_image.save(sd_image_path,optimize=False,compress_level=0)
     upload_to_ftp(sd_image_path)
     image_paths = save_image(rv_image)
-    #torch.cuda.empty_cache()
-    #gc.collect()
     torch.set_float32_matmul_precision("medium")
     with torch.no_grad():
         upscale = upscaler(rv_image, tiling=True, tile_width=256, tile_height=256)
@@ -632,9 +562,6 @@ with gr.Blocks(theme=gr.themes.Origin(),css=css) as demo:
     gr.Markdown("### REALVISXL V5.0")
     predefined_gallery = gr.Gallery(label="REALVISXL V5.0", columns=3, show_label=False, value=load_predefined_images1())
 
-    #gr.Markdown("### LIGHTNING V5.0")
-    #predefined_gallery = gr.Gallery(label="LIGHTNING V5.0", columns=3, show_label=False, value=load_predefined_images())
-
     gr.Markdown(
     """
     <div style="text-align: justify;">