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Update app.py
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app.py
CHANGED
@@ -90,7 +90,7 @@ class ModelWrapper:
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else:
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raise NotImplementedError()
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-
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print(f'noise: {noise.dtype}')
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#prompt_embed = prompt_embed.to(torch.float32)
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DTYPE = prompt_embed.dtype
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@@ -100,7 +100,7 @@ class ModelWrapper:
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current_timesteps = torch.ones(len(prompt_embed), device="cuda", dtype=torch.long) * constant
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#current_timesteps = current_timesteps.to(torch.float32)
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print(f'current_timestpes: {current_timesteps.dtype}')
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eval_images = self.model(noise, current_timesteps, prompt_embed, added_cond_kwargs=unet_added_conditions)
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print(eval_images.dtype)
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eval_images = get_x0_from_noise(noise, eval_images, alphas_cumprod, current_timesteps).to(self.DTYPE)
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print(eval_images.dtype)
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@@ -140,7 +140,7 @@ class ModelWrapper:
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)
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unet_added_conditions = {
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"time_ids": add_time_ids
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"text_embeds": batch_pooled_prompt_embeds.squeeze(1)
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}
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else:
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raise NotImplementedError()
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noise = noise.to(torch.float16)
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print(f'noise: {noise.dtype}')
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#prompt_embed = prompt_embed.to(torch.float32)
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DTYPE = prompt_embed.dtype
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current_timesteps = torch.ones(len(prompt_embed), device="cuda", dtype=torch.long) * constant
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#current_timesteps = current_timesteps.to(torch.float32)
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print(f'current_timestpes: {current_timesteps.dtype}')
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eval_images = self.model(noise, current_timesteps, prompt_embed, added_cond_kwargs=unet_added_conditions)
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print(eval_images.dtype)
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eval_images = get_x0_from_noise(noise, eval_images, alphas_cumprod, current_timesteps).to(self.DTYPE)
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print(eval_images.dtype)
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)
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unet_added_conditions = {
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"time_ids": add_time_ids,
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"text_embeds": batch_pooled_prompt_embeds.squeeze(1)
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}
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