Update app_quant_latent1.py

app_quant_latent1.py

@@ -254,53 +254,50 @@ import torch
 
 
 def safe_generate_with_latents(
-transformer,
-vae,
-text_encoder,
-tokenizer,
-scheduler,
-pipe,
-prompt,
-height,
-width,
-steps,
-guidance_scale,
-negative_prompt,
-num_images_per_prompt,
-generator,
-cfg_normalization,
-cfg_truncation,
-max_sequence_length,
-):
-"""
-Runs your original latent generator.
-ANY error → returns None and logs it.
-"""
-try:
+ transformer,
+ vae,
+ text_encoder,
+ tokenizer,
+ scheduler,
+ pipe,
+ prompt,
+ height,
+ width,
+ steps,
+ guidance_scale,
+ negative_prompt,
+ num_images_per_prompt,
+ generator,
+ cfg_normalization,
+ cfg_truncation,
+ max_sequence_length,
+ ):
+
+ try:
 # --- Your ORIGINAL generate() code pasted here EXACTLY ---
-latents_or_images = generate(
-transformer=transformer,
-vae=vae,
-text_encoder=text_encoder,
-tokenizer=tokenizer,
-scheduler=scheduler,
-prompt=prompt,
-height=height,
-width=width,
-num_inference_steps=steps,
-guidance_scale=guidance_scale,
-negative_prompt=negative_prompt,
-num_images_per_prompt=num_images_per_prompt,
-generator=generator,
-cfg_normalization=cfg_normalization,
-cfg_truncation=cfg_truncation,
-max_sequence_length=max_sequence_length,
-output_type="latent", # IMPORTANT
-)
+  latents_or_images = generate(
+  transformer=transformer,
+  vae=vae,
+  text_encoder=text_encoder,
+  tokenizer=tokenizer,
+  scheduler=scheduler,
+  prompt=prompt,
+  height=height,
+  width=width,
+  num_inference_steps=steps,
+  guidance_scale=guidance_scale,
+  negative_prompt=negative_prompt,
+  num_images_per_prompt=num_images_per_prompt,
+  generator=generator,
+  cfg_normalization=cfg_normalization,
+  cfg_truncation=cfg_truncation,
+  max_sequence_length=max_sequence_length,
+  output_type="latent", # IMPORTANT
+  )
 
     return latents_or_images, None
 
-except Exception as e:
+ except Exception as e:
     return None, e