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Remove unnecessary offset in img2img #1653

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Merged
merged 1 commit into from
Dec 10, 2022
Merged

Remove unnecessary offset in img2img #1653

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6 changes: 2 additions & 4 deletions src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -376,11 +376,9 @@ def check_inputs(self, prompt, strength, callback_steps):

def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

t_start = max(num_inference_steps - init_timestep + offset, 0)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]

return timesteps, num_inference_steps - t_start
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6 changes: 2 additions & 4 deletions src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py
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Expand Up @@ -414,11 +414,9 @@ def decode_latents(self, latents):
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

t_start = max(num_inference_steps - init_timestep + offset, 0)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]

return timesteps, num_inference_steps - t_start
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6 changes: 2 additions & 4 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py
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Expand Up @@ -323,11 +323,9 @@ def check_inputs(self, prompt, strength, callback_steps):
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

t_start = max(num_inference_steps - init_timestep + offset, 0)
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@patrickvonplaten patrickvonplaten Dec 10, 2022

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offset is never really used as it cancels itself out

t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]

return timesteps, num_inference_steps - t_start
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6 changes: 2 additions & 4 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -381,11 +381,9 @@ def check_inputs(self, prompt, strength, callback_steps):

def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

t_start = max(num_inference_steps - init_timestep + offset, 0)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]

return timesteps, num_inference_steps - t_start
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6 changes: 2 additions & 4 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -396,11 +396,9 @@ def check_inputs(self, prompt, strength, callback_steps):
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
offset = self.scheduler.config.get("steps_offset", 0)
init_timestep = int(num_inference_steps * strength) + offset
init_timestep = min(init_timestep, num_inference_steps)
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)

t_start = max(num_inference_steps - init_timestep + offset, 0)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]

return timesteps, num_inference_steps - t_start
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