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remove useless torch.no_grad() contexts

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This commit is contained in:
Pierre Chapuis 2023-09-12 17:55:39 +02:00
parent eea340c6c4
commit cf9efb57c8
1 changed files with 169 additions and 216 deletions
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385
tests/e2e/test_diffusion.py
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@ -401,24 +401,21 @@ def test_diffusion_std_random_init(
prompt = "a cute cat, detailed high-quality professional image" prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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) sd15.set_num_inference_steps(n_steps)
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_std_random_init) ensure_similar_images(predicted_image, expected_image_std_random_init)
@ -432,10 +429,7 @@ def test_diffusion_std_random_init_float16(
prompt = "a cute cat, detailed high-quality professional image" prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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 assert clip_text_embedding.dtype == torch.float16
sd15.set_num_inference_steps(n_steps) sd15.set_num_inference_steps(n_steps)
@ -443,15 +437,14 @@ def test_diffusion_std_random_init_float16(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_std_random_init, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image_std_random_init, min_psnr=35, min_ssim=0.98)
@ -468,24 +461,21 @@ def test_diffusion_std_init_image(
prompt = "a cute cat, detailed high-quality professional image" prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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) sd15.set_num_inference_steps(n_steps)
manual_seed(2) manual_seed(2)
x = sd15.init_latents((512, 512), cutecat_init, first_step=first_step) x = sd15.init_latents((512, 512), cutecat_init, first_step=first_step)
with torch.no_grad(): for step in sd15.steps[first_step:]:
for step in sd15.steps[first_step:]: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_std_init_image) ensure_similar_images(predicted_image, expected_image_std_init_image)
@ -503,9 +493,7 @@ def test_diffusion_inpainting(
prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen" 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" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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) sd15.set_num_inference_steps(n_steps)
sd15.set_inpainting_conditions(kitchen_dog, kitchen_dog_mask) sd15.set_inpainting_conditions(kitchen_dog, kitchen_dog_mask)
@ -513,15 +501,14 @@ def test_diffusion_inpainting(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
# PSNR and SSIM values are large because with float32 we get large differences even v.s. ourselves. # PSNR and SSIM values are large because with float32 we get large differences even v.s. ourselves.
ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=25, min_ssim=0.95) ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=25, min_ssim=0.95)
@ -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" 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" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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 assert clip_text_embedding.dtype == torch.float16
sd15.set_num_inference_steps(n_steps) sd15.set_num_inference_steps(n_steps)
@ -552,15 +536,14 @@ def test_diffusion_inpainting_float16(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
# PSNR and SSIM values are large because float16 is even worse than float32. # PSNR and SSIM values are large because float16 is even worse than float32.
ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=20, min_ssim=0.92) ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=20, min_ssim=0.92)
@ -583,9 +566,7 @@ def test_diffusion_controlnet(
prompt = "a cute cat, detailed high-quality professional image" prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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) sd15.set_num_inference_steps(n_steps)
@ -598,16 +579,15 @@ def test_diffusion_controlnet(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: controlnet.set_controlnet_condition(cn_condition)
controlnet.set_controlnet_condition(cn_condition) x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@ -630,9 +610,7 @@ def test_diffusion_controlnet_structural_copy(
prompt = "a cute cat, detailed high-quality professional image" prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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) sd15.set_num_inference_steps(n_steps)
@ -645,16 +623,15 @@ def test_diffusion_controlnet_structural_copy(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: controlnet.set_controlnet_condition(cn_condition)
controlnet.set_controlnet_condition(cn_condition) x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@ -676,9 +653,7 @@ def test_diffusion_controlnet_float16(
prompt = "a cute cat, detailed high-quality professional image" prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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) sd15.set_num_inference_steps(n_steps)
@ -691,16 +666,15 @@ def test_diffusion_controlnet_float16(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: controlnet.set_controlnet_condition(cn_condition)
controlnet.set_controlnet_condition(cn_condition) x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@ -729,9 +703,7 @@ def test_diffusion_controlnet_stack(
prompt = "a cute cat, detailed high-quality professional image" prompt = "a cute cat, detailed high-quality professional image"
negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality"
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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) sd15.set_num_inference_steps(n_steps)
@ -748,17 +720,16 @@ def test_diffusion_controlnet_stack(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: depth_controlnet.set_controlnet_condition(depth_cn_condition)
depth_controlnet.set_controlnet_condition(depth_cn_condition) canny_controlnet.set_controlnet_condition(canny_cn_condition)
canny_controlnet.set_controlnet_condition(canny_cn_condition) x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_controlnet_stack, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image_controlnet_stack, min_psnr=35, min_ssim=0.98)
@ -779,9 +750,7 @@ def test_diffusion_lora(
pytest.skip(allow_module_level=True) pytest.skip(allow_module_level=True)
prompt = "a cute cat" prompt = "a cute cat"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
with torch.no_grad():
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sd15.set_num_inference_steps(n_steps) sd15.set_num_inference_steps(n_steps)
@ -790,15 +759,14 @@ def test_diffusion_lora(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@ -819,9 +787,7 @@ def test_diffusion_lora_float16(
pytest.skip(allow_module_level=True) pytest.skip(allow_module_level=True)
prompt = "a cute cat" prompt = "a cute cat"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
with torch.no_grad():
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sd15.set_num_inference_steps(n_steps) sd15.set_num_inference_steps(n_steps)
@ -830,15 +796,14 @@ def test_diffusion_lora_float16(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=33, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image, min_psnr=33, min_ssim=0.98)
@ -859,9 +824,7 @@ def test_diffusion_lora_twice(
pytest.skip(allow_module_level=True) pytest.skip(allow_module_level=True)
prompt = "a cute cat" prompt = "a cute cat"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
with torch.no_grad():
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sd15.set_num_inference_steps(n_steps) sd15.set_num_inference_steps(n_steps)
@ -872,15 +835,14 @@ def test_diffusion_lora_twice(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98)
@ -893,10 +855,9 @@ def test_diffusion_refonly(
test_device: torch.device, test_device: torch.device,
): ):
sd15 = sd15_ddim sd15 = sd15_ddim
prompt = "Chicken"
with torch.no_grad(): prompt = "Chicken"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt) clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sai = ReferenceOnlyControlAdapter(sd15.unet).inject() sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
@ -906,19 +867,18 @@ def test_diffusion_refonly(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: noise = torch.randn(2, 4, 64, 64, device=test_device)
noise = torch.randn(2, 4, 64, 64, device=test_device) noised_guide = sd15.scheduler.add_noise(guide, noise, step)
noised_guide = sd15.scheduler.add_noise(guide, noise, step) sai.set_controlnet_condition(noised_guide)
sai.set_controlnet_condition(noised_guide) x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) torch.randn(2, 4, 64, 64, device=test_device) # for SD Web UI reproductibility only
torch.randn(2, 4, 64, 64, device=test_device) # for SD Web UI reproductibility only predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_refonly, min_psnr=35, min_ssim=0.99) ensure_similar_images(predicted_image, expected_image_refonly, min_psnr=35, min_ssim=0.99)
@ -934,10 +894,9 @@ def test_diffusion_inpainting_refonly(
): ):
sd15 = sd15_inpainting sd15 = sd15_inpainting
n_steps = 30 n_steps = 30
prompt = "" # unconditional
with torch.no_grad(): prompt = "" # unconditional
clip_text_embedding = sd15.compute_clip_text_embedding(prompt) clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sai = ReferenceOnlyControlAdapter(sd15.unet).inject() sai = ReferenceOnlyControlAdapter(sd15.unet).inject()
@ -950,22 +909,21 @@ def test_diffusion_inpainting_refonly(
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: noise = torch.randn_like(guide)
noise = torch.randn_like(guide) noised_guide = sd15.scheduler.add_noise(guide, noise, step)
noised_guide = sd15.scheduler.add_noise(guide, noise, step) # See https://github.com/Mikubill/sd-webui-controlnet/pull/1275 ("1.1.170 reference-only begin to support
# See https://github.com/Mikubill/sd-webui-controlnet/pull/1275 ("1.1.170 reference-only begin to support # inpaint variation models")
# inpaint variation models") noised_guide = torch.cat([noised_guide, torch.zeros_like(noised_guide)[:, 0:1, :, :], guide], dim=1)
noised_guide = torch.cat([noised_guide, torch.zeros_like(noised_guide)[:, 0:1, :, :], guide], dim=1)
sai.set_controlnet_condition(noised_guide) sai.set_controlnet_condition(noised_guide)
x = sd15( x = sd15(
x, x,
step=step, step=step,
clip_text_embedding=clip_text_embedding, clip_text_embedding=clip_text_embedding,
condition_scale=7.5, condition_scale=7.5,
) )
predicted_image = sd15.lda.decode_latents(x) predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_inpainting_refonly, min_psnr=35, min_ssim=0.99) ensure_similar_images(predicted_image, expected_image_inpainting_refonly, min_psnr=35, min_ssim=0.99)
@ -986,24 +944,21 @@ def test_diffusion_textual_inversion_random_init(
n_steps = 30 n_steps = 30
prompt = "a cute cat on a <gta5-artwork>" prompt = "a cute cat on a <gta5-artwork>"
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
with torch.no_grad():
clip_text_embedding = sd15.compute_clip_text_embedding(prompt)
sd15.set_num_inference_steps(n_steps) sd15.set_num_inference_steps(n_steps)
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device) x = torch.randn(1, 4, 64, 64, device=test_device)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_textual_inversion_random_init, min_psnr=35, min_ssim=0.98) ensure_similar_images(predicted_image, expected_image_textual_inversion_random_init, min_psnr=35, min_ssim=0.98)
@ -1033,34 +988,32 @@ def test_diffusion_ip_adapter(
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights) ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
ip_adapter.inject() ip_adapter.inject()
with torch.no_grad(): clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
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))
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2) negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2) negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
clip_text_embedding = torch.cat( clip_text_embedding = torch.cat(
( (
torch.cat([negative_text_embedding, negative_image_embedding], dim=1), torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1), torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
)
) )
)
sd15.set_num_inference_steps(n_steps) sd15.set_num_inference_steps(n_steps)
manual_seed(2) manual_seed(2)
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
with torch.no_grad(): for step in sd15.steps:
for step in sd15.steps: x = sd15(
x = sd15( x,
x, step=step,
step=step, clip_text_embedding=clip_text_embedding,
clip_text_embedding=clip_text_embedding, condition_scale=7.5,
condition_scale=7.5, )
) predicted_image = sd15.lda.decode_latents(x)
predicted_image = sd15.lda.decode_latents(x)
ensure_similar_images(predicted_image, expected_image_ip_adapter_woman) ensure_similar_images(predicted_image, expected_image_ip_adapter_woman)