Run dalle on 6gb+ memory
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@ -26,6 +26,6 @@ c. Create a write token/Copy an existing Token key and enter in the cmd window.
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## Requirements
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## Requirements
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* 11GB of free space.
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* 11GB of free space.
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* Nvidia card with 8GB+ video memory.
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* Nvidia card with 8GB+ video memory.
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* Currently min dalle can run in 6gb. Testing purpose
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## Additional info
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## Additional info
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Tested on RTX3070. One picture was making 14 seconds.
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Tested on RTX3070. One picture was making 12 - 14 seconds.
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@ -3,13 +3,14 @@ from distutils.log import error
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from msilib.schema import Directory
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from msilib.schema import Directory
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import os
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import os
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import time
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import time
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from IPython.display import display, update_display
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import torch
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import torch
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from tqdm.auto import tqdm
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from tqdm.auto import tqdm
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from min_dalle import MinDalle
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from min_dalle import MinDalle
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t = torch.cuda.get_device_properties(0).total_memory
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t = torch.cuda.get_device_properties(0).total_memory
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if t <=8500000000:
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is_mega = True
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if t <=6400000000:
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print("Not enough GPU memory to generate pictures")
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print("Not enough GPU memory to generate pictures")
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else:
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else:
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amount = int(input("Amount: "))
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amount = int(input("Amount: "))
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@ -26,11 +27,18 @@ else:
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dtype = "float32"
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dtype = "float32"
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if t <= 10500000000:
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if t <= 10500000000:
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dtype = "float16"
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dtype = "float16"
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print("Running float16 type")
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else:
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print("Running float32 type")
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if t<= 8500000000:
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print("Not enough memory to run mega dalle. Running smaller variant")
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is_mega=False
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else:
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print("Running in mega mode")
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model = MinDalle(
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model = MinDalle(
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dtype=getattr(torch, dtype),
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dtype=getattr(torch, dtype),
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device='cuda',
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device='cuda',
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is_mega=True,
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is_mega=False,
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is_reusable=True
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is_reusable=True
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)
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)
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@ -46,6 +46,10 @@ else:
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if t <= 10500000000:
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if t <= 10500000000:
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pipe.enable_attention_slicing()
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pipe.enable_attention_slicing()
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def dummy_checker(images, **kwargs):
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return images, False
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pipe.safety_checker = dummy_checker
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def preprocess(image):
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def preprocess(image):
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w, h = image.size
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w, h = image.size
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32
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@ -76,7 +80,7 @@ else:
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if filename.endswith(".png"):
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if filename.endswith(".png"):
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path_img.append(os.path.join(root, filename))
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path_img.append(os.path.join(root, filename))
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print("Found %i pictures", len(path_img))
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print("Found " +str(len(path_img)) +" pictures")
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start_time = time.time()
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start_time = time.time()
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counterr=0
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counterr=0
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@ -99,10 +103,10 @@ else:
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for seed in range(endSeed-startSeed+1):
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for seed in range(endSeed-startSeed+1):
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generator = torch.Generator(device=device).manual_seed(startSeed+seed)
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generator = torch.Generator(device=device).manual_seed(startSeed+seed)
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strenght = startStrength
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guidance_scale = startScale
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while strenght <= endStrength:
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while guidance_scale <= endScale:
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guidance_scale = startScale
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strenght = startStrength
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while guidance_scale <= endScale:
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while strenght <= endStrength:
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with autocast("cuda"):
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with autocast("cuda"):
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image = pipe(prompt=prompt, init_image=init_image, strength=strenght, guidance_scale=guidance_scale, generator=generator)["sample"][0]
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image = pipe(prompt=prompt, init_image=init_image, strength=strenght, guidance_scale=guidance_scale, generator=generator)["sample"][0]
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image.save(directory+str(counterr)+"/" + str(allwork) +".jpg")
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image.save(directory+str(counterr)+"/" + str(allwork) +".jpg")
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@ -119,8 +123,9 @@ else:
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allwork+=1
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allwork+=1
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guidance_scale+=deltaScale
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strenght+=deltaStrength
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strenght+=deltaStrength
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guidance_scale+=deltaScale
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counterr+=1
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counterr+=1
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