nodes/HelloVision-ComfyUI_HelloMeme
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fixed a bug

Browse Source
This commit is contained in:
songkey-win
2024-11-14 18:57:21 +08:00
parent e3d0af05d2
commit 8cd8e5677f
1 changed files with 7 additions and 14 deletions
Download Patch File Download Diff File Expand all files Collapse all files
hellomeme/pipelines/pipline_hm_video.py
+7 -14
View File
@@ -328,26 +328,19 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
drive_idx_chunks[-1] = list(range(drive_idx_chunks[-1][0], paded_video_len))
scheduler = EulerDiscreteScheduler(
num_train_timesteps=1000,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
)
# 5. set timesteps
timesteps, num_inference_steps = retrieve_timesteps(
scheduler, num_inference_steps, device, None, sigmas
self.scheduler, num_inference_steps, device, None, sigmas
)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * scheduler.order
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
base_noise = randn_tensor([batch_size, c, paded_video_len, h, w], dtype=prompt_embeds.dtype, generator=generator)
latents = scheduler.add_noise(pre_latents, base_noise, latent_timestep)
latents = self.scheduler.add_noise(pre_latents, base_noise, latent_timestep)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
@@ -358,7 +351,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
latent_model_input_all = torch.cat([latents.clone(), latents.clone()], dim=0) if \
self.do_classifier_free_guidance else latents.clone()
latent_model_input_all = scheduler.scale_model_input(latent_model_input_all, t)
latent_model_input_all = self.scheduler.scale_model_input(latent_model_input_all, t)
for cidx, chunk in enumerate(drive_idx_chunks):
if self.interrupt:
continue
@@ -389,7 +382,7 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
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 = scheduler.step(noise_pred.cpu(), t, latents,
latents = self.scheduler.step(noise_pred.cpu(), t, latents,
**extra_step_kwargs, return_dict=False)[0]
if callback_on_step_end is not None:
@@ -403,11 +396,11 @@ class HMVideoPipeline(StableDiffusionImg2ImgPipeline):
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.set_description(f"GEN stage2")
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(scheduler, "order", 1)
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
latents = latents / self.vae.config.scaling_factor