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Yaron Koresh
commited on
Update app.py
Browse files
app.py
CHANGED
@@ -430,7 +430,8 @@ CHECKPOINTS = ESRGANUpscalerCheckpoints(
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device = DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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DTYPE = dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
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-
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# logging
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@@ -450,8 +451,8 @@ MAX_SEED = np.iinfo(np.int32).max
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# precision data
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seq=512
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image_steps=
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img_accu=
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# ui data
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@@ -523,6 +524,7 @@ torch.cuda.empty_cache()
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# functionality
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def upscaler(
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input_image: Image.Image,
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prompt: str = "Masterpiece, Best Quality, Hyper-Realistic, Super-Realistic.",
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@@ -593,7 +595,7 @@ def _summarize(text):
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gen = model.generate(
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toks,
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length_penalty=2.0,
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num_beams=
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early_stopping=True,
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max_length=512
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)
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@@ -1286,7 +1288,7 @@ def translate(txt,to_lang="en",from_lang="auto"):
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log(f'RET translate with translation as {translation}')
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return translation.lower()
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-
@spaces.GPU(duration=
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def handle_generation(h,w,d):
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log(f'CALL handle_generate')
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@@ -1334,7 +1336,7 @@ def handle_generation(h,w,d):
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output_type="pil",
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guidance_scale=img_accu,
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num_images_per_prompt=1,
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num_inference_steps=
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max_sequence_length=seq,
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generator=torch.Generator(device).manual_seed(random.randint(0, MAX_SEED))
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).images[0]
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device = DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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DTYPE = dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
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+
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enhancer = ESRGANUpscaler(checkpoints=CHECKPOINTS, device=device, dtype=DTYPE).to(device)
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# logging
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# precision data
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seq=512
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image_steps=30
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img_accu=9.0
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# ui data
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# functionality
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@spaces.GPU(duration=300)
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def upscaler(
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input_image: Image.Image,
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prompt: str = "Masterpiece, Best Quality, Hyper-Realistic, Super-Realistic.",
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gen = model.generate(
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toks,
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length_penalty=2.0,
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num_beams=8,
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early_stopping=True,
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max_length=512
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)
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log(f'RET translate with translation as {translation}')
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return translation.lower()
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@spaces.GPU(duration=100)
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def handle_generation(h,w,d):
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log(f'CALL handle_generate')
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output_type="pil",
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guidance_scale=img_accu,
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num_images_per_prompt=1,
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num_inference_steps=image_steps,
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max_sequence_length=seq,
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generator=torch.Generator(device).manual_seed(random.randint(0, MAX_SEED))
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).images[0]
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