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Update app.py
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app.py
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@@ -4,7 +4,7 @@ import random
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import spaces
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import torch
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import time
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from diffusers import DiffusionPipeline
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from custom_pipeline import FLUXPipelineWithIntermediateOutputs
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# Constants
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@@ -19,6 +19,7 @@ dtype = torch.float16
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pipe = FLUXPipelineWithIntermediateOutputs.from_pretrained(
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"black-forest-labs/FLUX.1-schnell", torch_dtype=dtype
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).to("cuda")
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torch.cuda.empty_cache()
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# Inference function
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@@ -26,7 +27,7 @@ torch.cuda.empty_cache()
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def generate_image(prompt, seed=42, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT, randomize_seed=False, num_inference_steps=2, progress=gr.Progress(track_tqdm=True)):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator(
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start_time = time.time()
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import spaces
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import torch
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import time
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from diffusers import DiffusionPipeline, AutoencoderTiny
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from custom_pipeline import FLUXPipelineWithIntermediateOutputs
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# Constants
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pipe = FLUXPipelineWithIntermediateOutputs.from_pretrained(
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"black-forest-labs/FLUX.1-schnell", torch_dtype=dtype
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).to("cuda")
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pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.float16)
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torch.cuda.empty_cache()
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# Inference function
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def generate_image(prompt, seed=42, width=DEFAULT_WIDTH, height=DEFAULT_HEIGHT, randomize_seed=False, num_inference_steps=2, progress=gr.Progress(track_tqdm=True)):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(int(float(seed)))
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start_time = time.time()
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