mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-09 23:12:02 +00:00
remove useless torch.no_grad() contexts
This commit is contained in:
Pierre Chapuis
parent
eea340c6c4
commit
cf9efb57c8
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@ -401,8 +401,6 @@ def test_diffusion_std_random_init(
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prompt = "a cute cat, detailed high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -410,7 +408,6 @@ def test_diffusion_std_random_init(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -432,10 +429,7 @@ def test_diffusion_std_random_init_float16(
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prompt = "a cute cat, detailed high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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assert clip_text_embedding.dtype == torch.float16
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sd15.set_num_inference_steps(n_steps)
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@ -443,7 +437,6 @@ def test_diffusion_std_random_init_float16(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -468,8 +461,6 @@ def test_diffusion_std_init_image(
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prompt = "a cute cat, detailed high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -477,7 +468,6 @@ def test_diffusion_std_init_image(
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manual_seed(2)
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x = sd15.init_latents((512, 512), cutecat_init, first_step=first_step)
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with torch.no_grad():
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for step in sd15.steps[first_step:]:
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x = sd15(
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x,
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@ -503,8 +493,6 @@ def test_diffusion_inpainting(
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prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -513,7 +501,6 @@ def test_diffusion_inpainting(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -540,10 +527,7 @@ def test_diffusion_inpainting_float16(
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prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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assert clip_text_embedding.dtype == torch.float16
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sd15.set_num_inference_steps(n_steps)
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@ -552,7 +536,6 @@ def test_diffusion_inpainting_float16(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -583,8 +566,6 @@ def test_diffusion_controlnet(
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prompt = "a cute cat, detailed high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -598,7 +579,6 @@ def test_diffusion_controlnet(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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controlnet.set_controlnet_condition(cn_condition)
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x = sd15(
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@ -630,8 +610,6 @@ def test_diffusion_controlnet_structural_copy(
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prompt = "a cute cat, detailed high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -645,7 +623,6 @@ def test_diffusion_controlnet_structural_copy(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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controlnet.set_controlnet_condition(cn_condition)
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x = sd15(
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@ -676,8 +653,6 @@ def test_diffusion_controlnet_float16(
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prompt = "a cute cat, detailed high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -691,7 +666,6 @@ def test_diffusion_controlnet_float16(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
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with torch.no_grad():
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for step in sd15.steps:
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controlnet.set_controlnet_condition(cn_condition)
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x = sd15(
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@ -729,8 +703,6 @@ def test_diffusion_controlnet_stack(
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prompt = "a cute cat, detailed high-quality professional image"
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negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -748,7 +720,6 @@ def test_diffusion_controlnet_stack(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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depth_controlnet.set_controlnet_condition(depth_cn_condition)
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canny_controlnet.set_controlnet_condition(canny_cn_condition)
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@ -779,8 +750,6 @@ def test_diffusion_lora(
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pytest.skip(allow_module_level=True)
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prompt = "a cute cat"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -790,7 +759,6 @@ def test_diffusion_lora(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -819,8 +787,6 @@ def test_diffusion_lora_float16(
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pytest.skip(allow_module_level=True)
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prompt = "a cute cat"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -830,7 +796,6 @@ def test_diffusion_lora_float16(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -859,8 +824,6 @@ def test_diffusion_lora_twice(
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pytest.skip(allow_module_level=True)
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prompt = "a cute cat"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -872,7 +835,6 @@ def test_diffusion_lora_twice(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -893,9 +855,8 @@ def test_diffusion_refonly(
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test_device: torch.device,
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):
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sd15 = sd15_ddim
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prompt = "Chicken"
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with torch.no_grad():
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prompt = "Chicken"
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
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@ -906,7 +867,6 @@ def test_diffusion_refonly(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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noise = torch.randn(2, 4, 64, 64, device=test_device)
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noised_guide = sd15.scheduler.add_noise(guide, noise, step)
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@ -934,9 +894,8 @@ def test_diffusion_inpainting_refonly(
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):
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sd15 = sd15_inpainting
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n_steps = 30
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prompt = "" # unconditional
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with torch.no_grad():
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prompt = "" # unconditional
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
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@ -950,7 +909,6 @@ def test_diffusion_inpainting_refonly(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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noise = torch.randn_like(guide)
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noised_guide = sd15.scheduler.add_noise(guide, noise, step)
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@ -986,8 +944,6 @@ def test_diffusion_textual_inversion_random_init(
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n_steps = 30
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prompt = "a cute cat on a <gta5-artwork>"
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
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sd15.set_num_inference_steps(n_steps)
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@ -995,7 +951,6 @@ def test_diffusion_textual_inversion_random_init(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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@ -1033,7 +988,6 @@ def test_diffusion_ip_adapter(
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ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
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ip_adapter.inject()
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with torch.no_grad():
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clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
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@ -1052,7 +1006,6 @@ def test_diffusion_ip_adapter(
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manual_seed(2)
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x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
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with torch.no_grad():
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for step in sd15.steps:
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x = sd15(
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x,
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