Manjushri commited on
Commit
ad1d156
·
verified ·
1 Parent(s): 30917e2

Update app.py

Browse files

Fixed mysterious indent issue

Files changed (1) hide show
  1. app.py +8 -8
app.py CHANGED
@@ -102,7 +102,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
102
  image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
103
  torch.cuda.empty_cache()
104
 
105
- if upscale == "Yes":
106
  refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
107
  refiner.enable_xformers_memory_efficient_attention()
108
  refiner = refiner.to(device)
@@ -141,7 +141,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
141
  image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
142
  torch.cuda.empty_cache()
143
 
144
- if upscale == "Yes":
145
  refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
146
  refiner.enable_xformers_memory_efficient_attention()
147
  refiner = refiner.to(device)
@@ -182,7 +182,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
182
  image = refiner(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
183
  torch.cuda.empty_cache()
184
 
185
- if upscale == "Yes":
186
  refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
187
  refiner.enable_xformers_memory_efficient_attention()
188
  refiner = refiner.to(device)
@@ -205,9 +205,9 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
205
  return upscaled
206
  else:
207
 
208
- image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
209
- torch.cuda.empty_cache()
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- return image
211
 
212
  if Model == "Animagine XL 3.0":
213
  animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0")
@@ -226,7 +226,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
226
  image = animagine(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
227
  torch.cuda.empty_cache()
228
 
229
- if upscale == "Yes":
230
  animagine = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
231
  animagine.enable_xformers_memory_efficient_attention()
232
  animagine = animagine.to(device)
@@ -273,7 +273,7 @@ def genie (Model, Prompt, negative_prompt, height, width, scale, steps, seed, re
273
  refined = sdxl(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
274
  torch.cuda.empty_cache()
275
 
276
- if upscale == "Yes":
277
  sdxl = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
278
  sdxl.enable_xformers_memory_efficient_attention()
279
  sdxl = sdxl.to(device)
 
102
  image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
103
  torch.cuda.empty_cache()
104
 
105
+ if upscale == "Yes":
106
  refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
107
  refiner.enable_xformers_memory_efficient_attention()
108
  refiner = refiner.to(device)
 
141
  image = refiner(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
142
  torch.cuda.empty_cache()
143
 
144
+ if upscale == "Yes":
145
  refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
146
  refiner.enable_xformers_memory_efficient_attention()
147
  refiner = refiner.to(device)
 
182
  image = refiner(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
183
  torch.cuda.empty_cache()
184
 
185
+ if upscale == "Yes":
186
  refiner = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
187
  refiner.enable_xformers_memory_efficient_attention()
188
  refiner = refiner.to(device)
 
205
  return upscaled
206
  else:
207
 
208
+ image = semi(Prompt, negative_prompt=negative_prompt, height=height, width=width, num_inference_steps=steps, guidance_scale=scale).images[0]
209
+ torch.cuda.empty_cache()
210
+ return image
211
 
212
  if Model == "Animagine XL 3.0":
213
  animagine = DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0", torch_dtype=torch.float16, safety_checker=None) if torch.cuda.is_available() else DiffusionPipeline.from_pretrained("cagliostrolab/animagine-xl-3.0")
 
226
  image = animagine(Prompt, negative_prompt=negative_prompt, image=int_image, denoising_start=high_noise_frac).images[0]
227
  torch.cuda.empty_cache()
228
 
229
+ if upscale == "Yes":
230
  animagine = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
231
  animagine.enable_xformers_memory_efficient_attention()
232
  animagine = animagine.to(device)
 
273
  refined = sdxl(Prompt, negative_prompt=negative_prompt, image=image, denoising_start=high_noise_frac).images[0]
274
  torch.cuda.empty_cache()
275
 
276
+ if upscale == "Yes":
277
  sdxl = DiffusionPipeline.from_pretrained("stabilityai/sd-x2-latent-upscaler", torch_dtype=torch.float16, use_safetensors=True)
278
  sdxl.enable_xformers_memory_efficient_attention()
279
  sdxl = sdxl.to(device)