update

app.py

@@ -1,7 +1,7 @@
 from email.policy import default
 from json import encoder
 import gradio as gr
-import spaces
+#import spaces
 import numpy as np
 import torch
 import requests
@@ -311,7 +311,8 @@ def set_attention_processor(unet,id_length,is_ipadapter = False):
             hidden_size = unet.config.block_out_channels[block_id]
         if cross_attention_dim is None:
             if name.startswith("up_blocks") :
-                attn_procs[name] = SpatialAttnProcessor2_0(id_length = id_length)
+                device = devictorch.get(torch)
+                attn_procs[name] = SpatialAttnProcessor2_0(id_length = id_length, device=device)
                 total_count +=1
             else:    
                 attn_procs[name] = AttnProcessor()
@@ -418,7 +419,8 @@ cur_step = 0
 id_length = 4
 total_length = 5
 cur_model_type = ""
-device="cuda"
+#device="cuda"
+device = devicetorch.get(torch)
 global attn_procs,unet
 attn_procs = {}
 ###
@@ -441,27 +443,27 @@ use_safetensors= False
 ### 
 pipe2 = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
     models_dict["Unstable"], torch_dtype=torch.float16, use_safetensors=use_safetensors)
-pipe2 = pipe2.to("cpu")
+pipe2 = pipe2.to(device)
 pipe2.load_photomaker_adapter(
     os.path.dirname(photomaker_path),
     subfolder="",
     weight_name=os.path.basename(photomaker_path),
     trigger_word="img"  # define the trigger word
 )
-pipe2 = pipe2.to("cpu")
+pipe2 = pipe2.to(device)
 pipe2.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
 pipe2.fuse_lora()
 
 pipe4 = PhotoMakerStableDiffusionXLPipeline.from_pretrained(
     models_dict["RealVision"], torch_dtype=torch.float16, use_safetensors=True)
-pipe4 = pipe4.to("cpu")
+pipe4 = pipe4.to(device)
 pipe4.load_photomaker_adapter(
     os.path.dirname(photomaker_path),
     subfolder="",
     weight_name=os.path.basename(photomaker_path),
     trigger_word="img"  # define the trigger word
 )
-pipe4 = pipe4.to("cpu")
+pipe4 = pipe4.to(device)
 pipe4.enable_freeu(s1=0.6, s2=0.4, b1=1.1, b2=1.2)
 pipe4.fuse_lora()
 
@@ -502,7 +504,7 @@ def change_visiale_by_model_type(_model_type):
 
 
 ######### Image Generation ##############
-@spaces.GPU(duration=120)
+#@spaces.GPU(duration=120)
 def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_name, _Ip_Adapter_Strength ,_style_strength_ratio, guidance_scale, seed_,  sa32_, sa64_, id_length_,  general_prompt, negative_prompt,prompt_array,G_height,G_width,_comic_type):
     _model_type = "Photomaker" if _model_type == "Using Ref Images" else "original"
     if _model_type == "Photomaker" and "img" not in general_prompt:
@@ -557,7 +559,8 @@ def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_nam
     if start_merge_step > 30:
         start_merge_step = 30
     print(f"start_merge_step:{start_merge_step}")
-    generator = torch.Generator(device="cuda").manual_seed(seed_)
+    device = devictorch.get(torch)
+    generator = torch.Generator(device=device).manual_seed(seed_)
     sa32, sa64 =  sa32_, sa64_
     id_length = id_length_
     clipped_prompts = prompts[:]
@@ -566,7 +569,8 @@ def process_generation(_sd_type,_model_type,_upload_images, _num_steps,style_nam
     print(prompts)
     id_prompts = prompts[:id_length]
     real_prompts = prompts[id_length:]
-    torch.cuda.empty_cache()
+    devicetorch.empty_cache(torch)
+    #torch.cuda.empty_cache()
     write = True
     cur_step = 0
 
@@ -804,4 +808,5 @@ with gr.Blocks(css=css) as demo:
 
     # demo.load(None, None, None, _js=load_js)
 
-demo.launch(server_name="0.0.0.0", share = True if use_va else False)
+#demo.launch(server_name="0.0.0.0", share = True if use_va else False)
+demo.launch()

requirements.txt

@@ -1,14 +1,14 @@
-xformers==0.0.20
-torch==2.0.1
-torchvision==0.15.2
+#xformers==0.0.20
+#torch==2.0.1
+#torchvision==0.15.2
 diffusers==0.25.0
 transformers==4.36.2
 huggingface-hub==0.20.2
-spaces==0.19.4
+#spaces==0.19.4
 numpy
 accelerate
 safetensors
 omegaconf
 peft
-spaces==0.19.4
-Pillow==9.5.0
+#spaces==0.19.4
+Pillow==9.5.0