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fix: wrap validation inference with torch.no_grad() in dreambooth examples #13273

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fix: wrap validation inference with torch.no_grad() in dreambooth examples #13273

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21 changes: 11 additions & 10 deletions examples/dreambooth/train_dreambooth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,16 +175,17 @@ def log_validation(
# run inference
generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
images = []
if args.validation_images is None:
for _ in range(args.num_validation_images):
with torch.autocast("cuda"):
image = pipeline(**pipeline_args, num_inference_steps=25, generator=generator).images[0]
images.append(image)
else:
for image in args.validation_images:
image = Image.open(image)
image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
images.append(image)
with torch.no_grad():
if args.validation_images is None:
for _ in range(args.num_validation_images):
with torch.autocast("cuda"):
image = pipeline(**pipeline_args, num_inference_steps=25, generator=generator).images[0]
images.append(image)
else:
for image in args.validation_images:
image = Image.open(image)
image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
images.append(image)

for tracker in accelerator.trackers:
if tracker.name == "tensorboard":
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2 changes: 1 addition & 1 deletion examples/dreambooth/train_dreambooth_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,7 +191,7 @@ def log_validation(
# autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()
autocast_ctx = nullcontext()

with autocast_ctx:
with torch.no_grad(), autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]

for tracker in accelerator.trackers:
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20 changes: 11 additions & 9 deletions examples/dreambooth/train_dreambooth_lora.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,17 +155,19 @@ def log_validation(

if args.validation_images is None:
images = []
for _ in range(args.num_validation_images):
with torch.amp.autocast(accelerator.device.type):
image = pipeline(**pipeline_args, generator=generator).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with torch.amp.autocast(accelerator.device.type):
image = pipeline(**pipeline_args, generator=generator).images[0]
images.append(image)
else:
images = []
for image in args.validation_images:
image = Image.open(image)
with torch.amp.autocast(accelerator.device.type):
image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
images.append(image)
with torch.no_grad():
for image in args.validation_images:
image = Image.open(image)
with torch.amp.autocast(accelerator.device.type):
image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
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13 changes: 7 additions & 6 deletions examples/dreambooth/train_dreambooth_lora_flux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -210,12 +210,13 @@ def log_validation(
pipeline_args["prompt"], prompt_2=pipeline_args["prompt"]
)
images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, generator=generator
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, generator=generator
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
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15 changes: 8 additions & 7 deletions examples/dreambooth/train_dreambooth_lora_flux2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,13 +209,14 @@ def log_validation(
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
17 changes: 9 additions & 8 deletions examples/dreambooth/train_dreambooth_lora_flux2_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,14 +209,15 @@ def log_validation(
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
image=pipeline_args["image"],
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
image=pipeline_args["image"],
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
15 changes: 8 additions & 7 deletions examples/dreambooth/train_dreambooth_lora_flux2_klein.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,13 +209,14 @@ def log_validation(
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
19 changes: 10 additions & 9 deletions examples/dreambooth/train_dreambooth_lora_flux2_klein_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -209,15 +209,16 @@ def log_validation(
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
image=pipeline_args["image"],
prompt_embeds=pipeline_args["prompt_embeds"],
negative_prompt_embeds=pipeline_args["negative_prompt_embeds"],
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
image=pipeline_args["image"],
prompt_embeds=pipeline_args["prompt_embeds"],
negative_prompt_embeds=pipeline_args["negative_prompt_embeds"],
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
19 changes: 10 additions & 9 deletions examples/dreambooth/train_dreambooth_lora_flux_kontext.py
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Original file line number Diff line number Diff line change
Expand Up @@ -213,15 +213,16 @@ def log_validation(
prompt = pipeline_args_cp.pop("prompt")
prompt_embeds, pooled_prompt_embeds, text_ids = pipeline.encode_prompt(prompt, prompt_2=None)
images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
**pipeline_args_cp,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
**pipeline_args_cp,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
25 changes: 13 additions & 12 deletions examples/dreambooth/train_dreambooth_lora_hidream.py
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Original file line number Diff line number Diff line change
Expand Up @@ -212,18 +212,19 @@ def log_validation(
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds_t5=pipeline_args["prompt_embeds_t5"],
prompt_embeds_llama3=pipeline_args["prompt_embeds_llama3"],
negative_prompt_embeds_t5=pipeline_args["negative_prompt_embeds_t5"],
negative_prompt_embeds_llama3=pipeline_args["negative_prompt_embeds_llama3"],
pooled_prompt_embeds=pipeline_args["pooled_prompt_embeds"],
negative_pooled_prompt_embeds=pipeline_args["negative_pooled_prompt_embeds"],
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds_t5=pipeline_args["prompt_embeds_t5"],
prompt_embeds_llama3=pipeline_args["prompt_embeds_llama3"],
negative_prompt_embeds_t5=pipeline_args["negative_prompt_embeds_t5"],
negative_prompt_embeds_llama3=pipeline_args["negative_prompt_embeds_llama3"],
pooled_prompt_embeds=pipeline_args["pooled_prompt_embeds"],
negative_pooled_prompt_embeds=pipeline_args["negative_pooled_prompt_embeds"],
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
2 changes: 1 addition & 1 deletion examples/dreambooth/train_dreambooth_lora_lumina2.py
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Expand Up @@ -171,7 +171,7 @@ def log_validation(
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

with autocast_ctx:
with torch.no_grad(), autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]

for tracker in accelerator.trackers:
Expand Down
17 changes: 9 additions & 8 deletions examples/dreambooth/train_dreambooth_lora_qwen_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,14 +200,15 @@ def log_validation(
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=pipeline_args["prompt_embeds"],
prompt_embeds_mask=pipeline_args["prompt_embeds_mask"],
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt_embeds=pipeline_args["prompt_embeds"],
prompt_embeds_mask=pipeline_args["prompt_embeds_mask"],
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
3 changes: 2 additions & 1 deletion examples/dreambooth/train_dreambooth_lora_sana.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,7 +192,8 @@ def log_validation(
# run inference
generator = torch.Generator(device=accelerator.device).manual_seed(args.seed) if args.seed is not None else None

images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]
with torch.no_grad():
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
2 changes: 1 addition & 1 deletion examples/dreambooth/train_dreambooth_lora_sd3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -204,7 +204,7 @@ def log_validation(
# autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()
autocast_ctx = nullcontext()

with autocast_ctx:
with torch.no_grad(), autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]

for tracker in accelerator.trackers:
Expand Down
2 changes: 1 addition & 1 deletion examples/dreambooth/train_dreambooth_lora_sdxl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,7 +216,7 @@ def log_validation(
else:
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

with autocast_ctx:
with torch.no_grad(), autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]

for tracker in accelerator.trackers:
Expand Down
17 changes: 9 additions & 8 deletions examples/dreambooth/train_dreambooth_lora_z_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,14 +208,15 @@ def log_validation(
autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()

images = []
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt=args.validation_prompt,
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)
with torch.no_grad():
for _ in range(args.num_validation_images):
with autocast_ctx:
image = pipeline(
prompt=args.validation_prompt,
prompt_embeds=pipeline_args["prompt_embeds"],
generator=generator,
).images[0]
images.append(image)

for tracker in accelerator.trackers:
phase_name = "test" if is_final_validation else "validation"
Expand Down
2 changes: 1 addition & 1 deletion examples/dreambooth/train_dreambooth_sd3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -180,7 +180,7 @@ def log_validation(
# autocast_ctx = torch.autocast(accelerator.device.type) if not is_final_validation else nullcontext()
autocast_ctx = nullcontext()

with autocast_ctx:
with torch.no_grad(), autocast_ctx:
images = [pipeline(**pipeline_args, generator=generator).images[0] for _ in range(args.num_validation_images)]

for tracker in accelerator.trackers:
Expand Down