mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-24 23:28:45 +00:00
remove useless torch.no_grad() contexts
This commit is contained in:
Pierre Chapuis
parent
eea340c6c4
commit
cf9efb57c8
|
|
@ -401,8 +401,6 @@ def test_diffusion_std_random_init(
|
|||
|
||||
prompt = "a cute cat, detailed high-quality professional image"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -410,7 +408,6 @@ def test_diffusion_std_random_init(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -432,10 +429,7 @@ def test_diffusion_std_random_init_float16(
|
|||
|
||||
prompt = "a cute cat, detailed high-quality professional image"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
assert clip_text_embedding.dtype == torch.float16
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -443,7 +437,6 @@ def test_diffusion_std_random_init_float16(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -468,8 +461,6 @@ def test_diffusion_std_init_image(
|
|||
|
||||
prompt = "a cute cat, detailed high-quality professional image"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -477,7 +468,6 @@ def test_diffusion_std_init_image(
|
|||
manual_seed(2)
|
||||
x = sd15.init_latents((512, 512), cutecat_init, first_step=first_step)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps[first_step:]:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -503,8 +493,6 @@ def test_diffusion_inpainting(
|
|||
|
||||
prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -513,7 +501,6 @@ def test_diffusion_inpainting(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -540,10 +527,7 @@ def test_diffusion_inpainting_float16(
|
|||
|
||||
prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
assert clip_text_embedding.dtype == torch.float16
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -552,7 +536,6 @@ def test_diffusion_inpainting_float16(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -583,8 +566,6 @@ def test_diffusion_controlnet(
|
|||
|
||||
prompt = "a cute cat, detailed high-quality professional image"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -598,7 +579,6 @@ def test_diffusion_controlnet(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
controlnet.set_controlnet_condition(cn_condition)
|
||||
x = sd15(
|
||||
|
|
@ -630,8 +610,6 @@ def test_diffusion_controlnet_structural_copy(
|
|||
|
||||
prompt = "a cute cat, detailed high-quality professional image"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -645,7 +623,6 @@ def test_diffusion_controlnet_structural_copy(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
controlnet.set_controlnet_condition(cn_condition)
|
||||
x = sd15(
|
||||
|
|
@ -676,8 +653,6 @@ def test_diffusion_controlnet_float16(
|
|||
|
||||
prompt = "a cute cat, detailed high-quality professional image"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -691,7 +666,6 @@ def test_diffusion_controlnet_float16(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
controlnet.set_controlnet_condition(cn_condition)
|
||||
x = sd15(
|
||||
|
|
@ -729,8 +703,6 @@ def test_diffusion_controlnet_stack(
|
|||
|
||||
prompt = "a cute cat, detailed high-quality professional image"
|
||||
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -748,7 +720,6 @@ def test_diffusion_controlnet_stack(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
depth_controlnet.set_controlnet_condition(depth_cn_condition)
|
||||
canny_controlnet.set_controlnet_condition(canny_cn_condition)
|
||||
|
|
@ -779,8 +750,6 @@ def test_diffusion_lora(
|
|||
pytest.skip(allow_module_level=True)
|
||||
|
||||
prompt = "a cute cat"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -790,7 +759,6 @@ def test_diffusion_lora(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -819,8 +787,6 @@ def test_diffusion_lora_float16(
|
|||
pytest.skip(allow_module_level=True)
|
||||
|
||||
prompt = "a cute cat"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -830,7 +796,6 @@ def test_diffusion_lora_float16(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -859,8 +824,6 @@ def test_diffusion_lora_twice(
|
|||
pytest.skip(allow_module_level=True)
|
||||
|
||||
prompt = "a cute cat"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -872,7 +835,6 @@ def test_diffusion_lora_twice(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -893,9 +855,8 @@ def test_diffusion_refonly(
|
|||
test_device: torch.device,
|
||||
):
|
||||
sd15 = sd15_ddim
|
||||
prompt = "Chicken"
|
||||
|
||||
with torch.no_grad():
|
||||
prompt = "Chicken"
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
|
||||
|
||||
sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
|
||||
|
|
@ -906,7 +867,6 @@ def test_diffusion_refonly(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
noise = torch.randn(2, 4, 64, 64, device=test_device)
|
||||
noised_guide = sd15.scheduler.add_noise(guide, noise, step)
|
||||
|
|
@ -934,9 +894,8 @@ def test_diffusion_inpainting_refonly(
|
|||
):
|
||||
sd15 = sd15_inpainting
|
||||
n_steps = 30
|
||||
prompt = "" # unconditional
|
||||
|
||||
with torch.no_grad():
|
||||
prompt = "" # unconditional
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
|
||||
|
||||
sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
|
||||
|
|
@ -950,7 +909,6 @@ def test_diffusion_inpainting_refonly(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
noise = torch.randn_like(guide)
|
||||
noised_guide = sd15.scheduler.add_noise(guide, noise, step)
|
||||
|
|
@ -986,8 +944,6 @@ def test_diffusion_textual_inversion_random_init(
|
|||
n_steps = 30
|
||||
|
||||
prompt = "a cute cat on a <gta5-artwork>"
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
|
@ -995,7 +951,6 @@ def test_diffusion_textual_inversion_random_init(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
@ -1033,7 +988,6 @@ def test_diffusion_ip_adapter(
|
|||
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
|
||||
ip_adapter.inject()
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
|
||||
|
||||
|
|
@ -1052,7 +1006,6 @@ def test_diffusion_ip_adapter(
|
|||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
|
|
|
|||
Loading…
Reference in a new issue