rafaaa2105 commited on
Commit
4c2de11
·
verified ·
1 Parent(s): d2e6bd4

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +11 -9
app.py CHANGED
@@ -4,7 +4,7 @@ import random
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  import os
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  # import spaces #[uncomment to use ZeroGPU]
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- from diffusers import AutoPipelineForText2Image
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  import torch
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  device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -12,11 +12,14 @@ hf_token = os.getenv('HF_TOKEN')
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  if torch.cuda.is_available():
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  torch_dtype = torch.float16
 
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  else:
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  torch_dtype = torch.float32
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  pipe = pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", token=hf_token, torch_dtype=torch.bfloat16)
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  pipe.load_lora_weights('aleksa-codes/flux-ghibsky-illustration', weight_name='lora.safetensors')
 
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  pipe = pipe.to(device)
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  MAX_SEED = np.iinfo(np.int32).max
@@ -26,13 +29,12 @@ MAX_IMAGE_SIZE = 1024
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  # @spaces.GPU #[uncomment to use ZeroGPU]
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  def infer(
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  prompt,
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- negative_prompt,
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- seed,
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- randomize_seed,
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- width,
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- height,
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- guidance_scale,
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- num_inference_steps,
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  progress=gr.Progress(track_tqdm=True),
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  ):
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  if randomize_seed:
@@ -42,12 +44,12 @@ def infer(
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  image = pipe(
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  prompt=prompt,
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- negative_prompt=negative_prompt,
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  guidance_scale=guidance_scale,
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  num_inference_steps=num_inference_steps,
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  width=width,
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  height=height,
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  generator=generator,
 
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  ).images[0]
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  return image, seed
 
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  import os
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  # import spaces #[uncomment to use ZeroGPU]
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+ from diffusers import AutoPipelineForText2Image, AutoencoderKL, AutoencoderTiny
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  import torch
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  device = "cuda" if torch.cuda.is_available() else "cpu"
 
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  if torch.cuda.is_available():
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  torch_dtype = torch.float16
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+ torch.cuda.empty_cache()
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  else:
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  torch_dtype = torch.float32
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+ taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
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  pipe = pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", token=hf_token, torch_dtype=torch.bfloat16)
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  pipe.load_lora_weights('aleksa-codes/flux-ghibsky-illustration', weight_name='lora.safetensors')
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+ good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
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  pipe = pipe.to(device)
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  MAX_SEED = np.iinfo(np.int32).max
 
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  # @spaces.GPU #[uncomment to use ZeroGPU]
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  def infer(
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  prompt,
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+ seed=42,
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+ randomize_seed=True,
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+ width=1024,
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+ height=1024,
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+ guidance_scale=3.5,
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+ num_inference_steps=28,
 
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  progress=gr.Progress(track_tqdm=True),
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  ):
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  if randomize_seed:
 
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  image = pipe(
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  prompt=prompt,
 
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  guidance_scale=guidance_scale,
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  num_inference_steps=num_inference_steps,
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  width=width,
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  height=height,
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  generator=generator,
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+ good_vae=good_vae,
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  ).images[0]
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  return image, seed