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fixed generator for prepare_extra_step_kwargs

This commit is contained in:
Johannes Stelzer 2024-01-08 20:31:28 +01:00
parent 196f8bc09e
commit df02d15562
1 changed files with 16 additions and 133 deletions
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147
diffusers_holder.py
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@ -114,8 +114,6 @@ class DiffusersHolder():
def get_noise(self, seed=420): def get_noise(self, seed=420):
generator = torch.Generator(device=self.device).manual_seed(int(seed))
latents = self.pipe.prepare_latents( latents = self.pipe.prepare_latents(
1, 1,
self.pipe.unet.config.in_channels, self.pipe.unet.config.in_channels,
@ -123,7 +121,7 @@ class DiffusersHolder():
self.width_img, self.width_img,
torch.float16, torch.float16,
self.pipe._execution_device, self.pipe._execution_device,
generator, torch.Generator(device=self.device).manual_seed(int(seed)),
None, None,
) )
@ -448,6 +446,7 @@ class DiffusersHolder():
list_latents_mixing=None, list_latents_mixing=None,
mixing_coeffs=0.0, mixing_coeffs=0.0,
return_image: Optional[bool] = False, return_image: Optional[bool] = False,
seed=420,
**kwargs, **kwargs,
): ):
@ -479,6 +478,7 @@ class DiffusersHolder():
callback_on_step_end = None callback_on_step_end = None
callback_on_step_end_tensor_inputs = ["latents"] callback_on_step_end_tensor_inputs = ["latents"]
# 0. Default height and width to unet # 0. Default height and width to unet
height = self.pipe.default_sample_size * self.pipe.vae_scale_factor height = self.pipe.default_sample_size * self.pipe.vae_scale_factor
width = self.pipe.default_sample_size * self.pipe.vae_scale_factor width = self.pipe.default_sample_size * self.pipe.vae_scale_factor
@ -488,8 +488,6 @@ class DiffusersHolder():
target_size = (height, width) target_size = (height, width)
# 1. (skipped) Check inputs. Raise error if not correct # 1. (skipped) Check inputs. Raise error if not correct
self.pipe._guidance_scale = guidance_scale self.pipe._guidance_scale = guidance_scale
self.pipe._guidance_rescale = guidance_rescale self.pipe._guidance_rescale = guidance_rescale
self.pipe._clip_skip = clip_skip self.pipe._clip_skip = clip_skip
@ -513,7 +511,8 @@ class DiffusersHolder():
list_latents_out = [] list_latents_out = []
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta)
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(torch.Generator(device=self.device).manual_seed(int(0)), eta)
# 7. Prepare added time ids & embeddings # 7. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds add_text_embeds = pooled_prompt_embeds
@ -630,119 +629,6 @@ class DiffusersHolder():
@torch.no_grad()
def run_diffusion_sd_xl_old(
self,
text_embeddings: list,
latents_start: torch.FloatTensor,
idx_start: int = 0,
list_latents_mixing=None,
mixing_coeffs=0.0,
return_image: Optional[bool] = False,
**kwargs,
):
# 0. Default height and width to unet
original_size = (self.width_img, self.height_img)
crops_coords_top_left = (0, 0)
target_size = original_size
batch_size = 1
eta = 0.0
num_images_per_prompt = 1
cross_attention_kwargs = None
generator = torch.Generator(device=self.device) # dummy generator
do_classifier_free_guidance = self.guidance_scale > 1.0
# 1. Check inputs. Raise error if not correct & 2. Define call parameters
list_mixing_coeffs = self.prepare_mixing(mixing_coeffs, list_latents_mixing)
# 3. Encode input prompt (already encoded outside bc of mixing, just split here)
prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds = text_embeddings
# 4. Prepare timesteps
self.pipe.scheduler.set_timesteps(self.num_inference_steps, device=self.device)
timesteps = self.pipe.scheduler.timesteps
# 5. Prepare latent variables
latents = latents_start.clone()
list_latents_out = []
# 6. Prepare extra step kwargs. usedummy generator
extra_step_kwargs = self.pipe.prepare_extra_step_kwargs(generator, eta) # dummy
# 7. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
if self.pipe.text_encoder_2 is None:
text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
else:
text_encoder_projection_dim = self.pipe.text_encoder_2.config.projection_dim
add_time_ids = self.pipe._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
dtype=prompt_embeds.dtype,
text_encoder_projection_dim=text_encoder_projection_dim,
)
negative_add_time_ids = add_time_ids
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(self.device)
add_text_embeds = add_text_embeds.to(self.device)
add_time_ids = add_time_ids.to(self.device).repeat(batch_size * num_images_per_prompt, 1)
# 8. Denoising loop
for i, t in enumerate(timesteps):
# Write latents out and skip
if i < idx_start:
list_latents_out.append(None)
continue
# Set the right starting latents
elif i == idx_start:
latents = latents_start.clone()
# Mix latents for crossfeeding
if i > 0 and list_mixing_coeffs[i] > 0:
latents_mixtarget = list_latents_mixing[i - 1].clone()
latents = interpolate_spherical(latents, latents_mixtarget, list_mixing_coeffs[i])
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2)# if do_classifier_free_guidance else latents
# Always scale latents
latent_model_input = self.pipe.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
noise_pred = self.pipe.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.pipe.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
# Append latents
list_latents_out.append(latents.clone())
if return_image:
return self.latent2image(latents)
else:
return list_latents_out
#%% #%%
if __name__ == "__main__": if __name__ == "__main__":
from PIL import Image from PIL import Image
@ -757,17 +643,7 @@ if __name__ == "__main__":
pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16) pipe.vae = AutoencoderTiny.from_pretrained('madebyollin/taesdxl', torch_device='cuda', torch_dtype=torch.float16)
pipe.vae = pipe.vae.cuda() pipe.vae = pipe.vae.cuda()
#%% #%%
self = DiffusersHolder(pipe)
self.set_num_inference_steps(4)
prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
text_embeddings1 = self.get_text_embedding(prompt1)
latents_start = self.get_noise(seed=420)
latents = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)[-1]
image = self.latent2image(latents)
xxxx
# # xxx # # xxx
# self.set_dimensions((512, 512)) # self.set_dimensions((512, 512))
# self.set_num_inference_steps(4) # self.set_num_inference_steps(4)
@ -785,6 +661,7 @@ if __name__ == "__main__":
self.set_num_inference_steps(num_inference_steps) self.set_num_inference_steps(num_inference_steps)
latents_start = self.get_noise() latents_start = self.get_noise()
guidance_scale = 0 guidance_scale = 0
self.guidance_scale = 0
#% get embeddings1 #% get embeddings1
prompt1 = "Photo of a colorful landscape with a blue sky with clouds" prompt1 = "Photo of a colorful landscape with a blue sky with clouds"
@ -797,11 +674,17 @@ if __name__ == "__main__":
prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2 prompt_embeds2, negative_prompt_embeds2, pooled_prompt_embeds2, negative_pooled_prompt_embeds2 = text_embeddings2
latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False) latents1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=False)
latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
img1 = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
img1B = self.run_diffusion_sd_xl(text_embeddings1, latents_start, idx_start=0, return_image=True)
# check if brings same image if restarted
img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True) # latents2 = self.run_diffusion_sd_xl(text_embeddings2, latents_start, idx_start=0, return_image=False)
# # check if brings same image if restarted
# img1_return = self.run_diffusion_sd_xl(text_embeddings1, latents1[idx_mix-1], idx_start=idx_start, return_image=True)
# mix latents # mix latents
#%% #%%