JoPmt commited on
Commit
428793e
1 Parent(s): 298a8f7

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +79 -8
app.py CHANGED
@@ -1,20 +1,70 @@
1
  from diffusers import AutoPipelineForText2Image, PNDMScheduler
2
  import torch
 
3
  import gradio as gr
4
  from PIL import Image
5
- import os, random
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- import PIL.Image
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- from transformers import pipeline
8
  from diffusers.utils import load_image
 
 
 
9
  from accelerate import Accelerator
10
-
 
 
 
 
 
11
  accelerator = Accelerator(cpu=True)
 
 
12
  apol=[]
 
13
  pipe = accelerator.prepare(AutoPipelineForText2Image.from_pretrained("openskyml/overall-v1", torch_dtype=torch.float32, variant=None, use_safetensors=False, safety_checker=None))
14
  pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
15
  pipe.unet.to(memory_format=torch.channels_last)
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  pipe.to("cpu")
17
- def plex(prompt,neg_prompt,stips,nut):
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
18
  if nut == 0:
19
  nm = random.randint(1, 2147483616)
20
  while nm % 32 != 0:
@@ -22,11 +72,32 @@ def plex(prompt,neg_prompt,stips,nut):
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  else:
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  nm=nut
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  generator = torch.Generator(device="cpu").manual_seed(nm)
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- image = pipe(prompt=[prompt]*2, negative_prompt=[neg_prompt]*2, generator=generator, num_inference_steps=stips)
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  for a, imze in enumerate(image["images"]):
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  apol.append(imze)
 
 
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  return apol
29
 
30
- iface = gr.Interface(fn=plex,inputs=[gr.Textbox(label="Prompt"), gr.Textbox(label="negative_prompt", value="low quality, bad quality"),gr.Slider(label="num steps",minimum=1,step=1,maximum=30,value=20),gr.Slider(label="manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0)],outputs=gr.Gallery(label="Generated Output Image", columns=1),description="Running on cpu, very slow! by JoPmt.")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
31
  iface.queue(max_size=1,api_open=False)
32
- iface.launch(max_threads=1)
 
1
  from diffusers import AutoPipelineForText2Image, PNDMScheduler
2
  import torch
3
+ from transformers import pipeline
4
  import gradio as gr
5
  from PIL import Image
 
 
 
6
  from diffusers.utils import load_image
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+ import os, random, gc, re, json, time, shutil, glob
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+ import PIL.Image
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+ import tqdm
10
  from accelerate import Accelerator
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+ from huggingface_hub import HfApi, InferenceClient, ModelCard, RepoCard, upload_folder, hf_hub_download, HfFileSystem
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+ HfApi=HfApi()
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+ HF_TOKEN=os.getenv("HF_TOKEN")
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+ HF_HUB_DISABLE_TELEMETRY=1
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+ DO_NOT_TRACK=1
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+ HF_HUB_ENABLE_HF_TRANSFER=0
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  accelerator = Accelerator(cpu=True)
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+ InferenceClient=InferenceClient()
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+
20
  apol=[]
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+
22
  pipe = accelerator.prepare(AutoPipelineForText2Image.from_pretrained("openskyml/overall-v1", torch_dtype=torch.float32, variant=None, use_safetensors=False, safety_checker=None))
23
  pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
24
  pipe.unet.to(memory_format=torch.channels_last)
25
  pipe.to("cpu")
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+
27
+ def chdr(apol,prompt,modil,stips,fnamo,gaul):
28
+ try:
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+ type="KNDSK22_INTERP"
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+ los=""
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+ tre='./tmpo/'+fnamo+'.json'
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+ tra='./tmpo/'+fnamo+'_0.png'
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+ flng=["yssup", "sllab", "stsaerb", "sinep", "selppin", "ssa", "tnuc", "mub", "kcoc", "kcid", "anigav", "dekan", "edun", "slatineg", "xes", "nrop", "stit", "ttub", "bojwolb", "noitartenep", "kcuf", "kcus", "kcil", "elttil", "gnuoy", "thgit", "lrig", "etitep", "dlihc", "yxes"]
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+ flng=[itm[::-1] for itm in flng]
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+ ptn = r"\b" + r"\b|\b".join(flng) + r"\b"
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+ if re.search(ptn, prompt, re.IGNORECASE):
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+ print("onon buddy")
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+ else:
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+ dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type}
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+ with open(tre, 'w') as f:
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+ json.dump(dobj, f)
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+ HfApi.upload_folder(repo_id="JoPmt/hf_community_images",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
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+ dobj={'img_name':fnamo,'model':modil,'lora':los,'prompt':prompt,'steps':stips,'type':type,'haed':gaul,}
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+ with open(tre, 'w') as f:
45
+ json.dump(dobj, f)
46
+ HfApi.upload_folder(repo_id="JoPmt/Tst_datast_imgs",folder_path="./tmpo",repo_type="dataset",path_in_repo="./",token=HF_TOKEN)
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+ try:
48
+ for pgn in glob.glob('./tmpo/*.png'):
49
+ os.remove(pgn)
50
+ for jgn in glob.glob('./tmpo/*.json'):
51
+ os.remove(jgn)
52
+ del tre
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+ del tra
54
+ except:
55
+ print("cant")
56
+ except:
57
+ print("failed to make obj")
58
+
59
+ def plax(gaul,req: gr.Request):
60
+ gaul=str(req.headers)
61
+ return gaul
62
+
63
+ def plex(prompt,neg_prompt,stips,nut,wit,het,gaul,progress=gr.Progress(track_tqdm=True)):
64
+ gc.collect()
65
+ apol=[]
66
+ modil="openskyml/overall-v1"
67
+ fnamo=""+str(int(time.time()))+""
68
  if nut == 0:
69
  nm = random.randint(1, 2147483616)
70
  while nm % 32 != 0:
 
72
  else:
73
  nm=nut
74
  generator = torch.Generator(device="cpu").manual_seed(nm)
75
+ image = pipe(prompt=[prompt]*2, negative_prompt=[neg_prompt]*2, generator=generator, num_inference_steps=stips,height=het,width=wit)
76
  for a, imze in enumerate(image["images"]):
77
  apol.append(imze)
78
+ imze.save('./tmpo/'+fnamo+'_'+str(i)+'.png', 'PNG')
79
+ chdr(apol,prompt,modil,stips,fnamo,gaul)
80
  return apol
81
 
82
+ def aip(ill,api_name="/run"):
83
+ return
84
+ def pit(ill,api_name="/predict"):
85
+ return
86
+
87
+ with gr.Blocks(theme=random.choice([gr.themes.Monochrome(),gr.themes.Base.from_hub("gradio/seafoam"),gr.themes.Base.from_hub("freddyaboulton/dracula_revamped"),gr.themes.Glass(),gr.themes.Base(),]),analytics_enabled=False) as iface:
88
+ ##iface.description="Running on cpu, very slow! by JoPmt."
89
+ out=gr.Gallery(label="Generated Output Image", columns=1)
90
+ inut=gr.Textbox(label="Prompt")
91
+ gaul=gr.Textbox(visible=False)
92
+ btn=gr.Button("GENERATE")
93
+ with gr.Accordion("Advanced Settings", open=False):
94
+ inet=gr.Textbox(label="Negative_prompt", value="lowres,text,bad quality,low quality,jpeg artifacts,ugly,bad hands,bad face,blurry,bad eyes,watermark,signature")
95
+ inyt=gr.Slider(label="Num inference steps",minimum=1,step=1,maximum=30,value=20)
96
+ indt=gr.Slider(label="Manual seed (leave 0 for random)",minimum=0,step=32,maximum=2147483616,value=0)
97
+ inwt=gr.Slider(label="Width",minimum=256,step=32,maximum=1024,value=768)
98
+ inht=gr.Slider(label="Height",minimum=256,step=32,maximum=1024,value=768)
99
+
100
+ btn.click(fn=plax,inputs=gaul,outputs=gaul).then(fn=plex, outputs=[out], inputs=[inut,inet,inyt,indt,inwt,inht,gaul])
101
+
102
  iface.queue(max_size=1,api_open=False)
103
+ iface.launch(max_threads=20,inline=False,show_api=False)