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Update app.py
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app.py
CHANGED
@@ -18,7 +18,7 @@ from diffusers import AutoencoderKL, StableDiffusionXLPipeline, EulerAncestralDi
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from typing import Tuple
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#from transformers import AutoTokenizer, AutoModelForCausalLM
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import paramiko
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-
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#os.system("chmod +x ./cusparselt.sh")
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#os.system("./cusparselt.sh")
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#os.system("chmod +x ./cudnn.sh")
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@@ -204,8 +204,8 @@ def generate(
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options["use_resolution_binning"] = True
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images = []
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with torch.no_grad():
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-
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batch_options = options.copy()
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batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
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if "negative_prompt" in batch_options:
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@@ -215,6 +215,8 @@ def generate(
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images[0].save(sd_image_path,optimize=False,compress_level=0)
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upload_to_ftp(sd_image_path)
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image_paths = [save_image(img) for img in images]
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return image_paths, seed
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def generate_cpu(
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@@ -238,7 +240,7 @@ def generate_cpu(
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pipe.to("cpu")
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seed = int(randomize_seed_fn(seed, randomize_seed))
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generator = torch.Generator(device='
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prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
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from typing import Tuple
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#from transformers import AutoTokenizer, AutoModelForCausalLM
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import paramiko
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import gc
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#os.system("chmod +x ./cusparselt.sh")
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#os.system("./cusparselt.sh")
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#os.system("chmod +x ./cudnn.sh")
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options["use_resolution_binning"] = True
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images = []
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#with torch.no_grad():
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for i in range(0, num_images, BATCH_SIZE):
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batch_options = options.copy()
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batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE]
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if "negative_prompt" in batch_options:
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images[0].save(sd_image_path,optimize=False,compress_level=0)
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upload_to_ftp(sd_image_path)
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image_paths = [save_image(img) for img in images]
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torch.cuda.empty_cache()
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gc.collect()
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return image_paths, seed
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def generate_cpu(
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pipe.to("cpu")
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seed = int(randomize_seed_fn(seed, randomize_seed))
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generator = torch.Generator(device='cuda').manual_seed(seed)
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prompt, negative_prompt = apply_style(style_selection, prompt, negative_prompt)
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