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fix(examples): explicitly pass resolution to text-to-image inference #12929

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fix(examples): explicitly pass resolution to text-to-image inference #12929

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16 changes: 14 additions & 2 deletions examples/text_to_image/train_text_to_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -171,7 +171,13 @@ def log_validation(vae, text_encoder, tokenizer, unet, args, accelerator, weight
autocast_ctx = torch.autocast(accelerator.device.type)

with autocast_ctx:
image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
image = pipeline(
args.validation_prompts[i],
height=args.resolution,
width=args.resolution,
num_inference_steps=20,
generator=generator,
).images[0]

images.append(image)

Expand Down Expand Up @@ -1150,7 +1156,13 @@ def unwrap_model(model):

for i in range(len(args.validation_prompts)):
with torch.autocast("cuda"):
image = pipeline(args.validation_prompts[i], num_inference_steps=20, generator=generator).images[0]
image = pipeline(
args.validation_prompts[i],
height=args.resolution,
width=args.resolution,
num_inference_steps=20,
generator=generator,
).images[0]
images.append(image)

if args.push_to_hub:
Expand Down
10 changes: 9 additions & 1 deletion examples/text_to_image/train_text_to_image_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,15 @@ def log_validation(

with autocast_ctx:
for _ in range(args.num_validation_images):
images.append(pipeline(args.validation_prompt, num_inference_steps=30, generator=generator).images[0])
images.append(
pipeline(
args.validation_prompt,
height=args.resolution,
width=args.resolution,
num_inference_steps=30,
generator=generator,
).images[0]
)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
6 changes: 5 additions & 1 deletion examples/text_to_image/train_text_to_image_lora_sdxl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,11 @@ def log_validation(

# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
pipeline_args = {"prompt": args.validation_prompt}
pipeline_args = {
"prompt": args.validation_prompt,
"height": args.resolution,
"width": args.resolution,
}
if torch.backends.mps.is_available():
autocast_ctx = nullcontext()
else:
Expand Down
14 changes: 12 additions & 2 deletions examples/text_to_image/train_text_to_image_sdxl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1258,7 +1258,11 @@ def compute_time_ids(original_size, crops_coords_top_left):
if args.seed is not None
else None
)
pipeline_args = {"prompt": args.validation_prompt}
pipeline_args = {
"prompt": args.validation_prompt,
"height": args.resolution,
"width": args.resolution,
}

with autocast_ctx:
images = [
Expand Down Expand Up @@ -1327,7 +1331,13 @@ def compute_time_ids(original_size, crops_coords_top_left):

with autocast_ctx:
images = [
pipeline(args.validation_prompt, num_inference_steps=25, generator=generator).images[0]
pipeline(
args.validation_prompt,
height=args.resolution,
width=args.resolution,
num_inference_steps=25,
generator=generator,
).images[0]
for _ in range(args.num_validation_images)
]

Expand Down