Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 17:24:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

fix wan i2i

Browse Source 
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-09-11 09:50:20 -04:00
parent 16670636d7
commit 2bb96e6926
7 changed files with 29 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/processing_args.py
+13 -9
View File
@@ -19,9 +19,10 @@ disable_pbar = os.environ.get('SD_DISABLE_PBAR', None) is not None
def task_specific_kwargs(p, model):
model_cls = model.__class__.__name__
vae_scale_factor = sd_vae.get_vae_scale_factor(model)
task_args = {}
is_img2img_model = bool('Zero123' in shared.sd_model.__class__.__name__)
is_img2img_model = bool('Zero123' in model_cls)
if len(getattr(p, 'init_images', [])) > 0:
if isinstance(p.init_images[0], str):
p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images]
@@ -36,7 +37,7 @@ def task_specific_kwargs(p, model):
}
elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.IMAGE_2_IMAGE or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
if model.__class__.__name__ in sd_models.i2i_pipes:
if model_cls in sd_models.i2i_pipes:
pass
else:
model.register_to_config(requires_aesthetics_score = False)
@@ -49,19 +50,19 @@ def task_specific_kwargs(p, model):
'image': p.init_images,
'strength': p.denoising_strength,
}
if model.__class__.__name__ == 'FluxImg2ImgPipeline' or model.__class__.__name__ == 'FluxKontextPipeline': # needs explicit width/height
if model_cls == 'FluxImg2ImgPipeline' or model_cls == 'FluxKontextPipeline': # needs explicit width/height
if torch.is_tensor(p.init_images[0]):
p.width, p.height = p.init_images[0].shape[-1] * vae_scale_factor, p.init_images[0].shape[-2] * vae_scale_factor
else:
p.width, p.height = 8 * math.ceil(p.init_images[0].width / vae_scale_factor), 8 * math.ceil(p.init_images[0].height / vae_scale_factor)
if model.__class__.__name__ == 'FluxKontextPipeline':
if model_cls == 'FluxKontextPipeline':
aspect_ratio = p.width / p.height
max_area = max(p.width, p.height)**2
p.width, p.height = round((max_area * aspect_ratio) ** 0.5), round((max_area / aspect_ratio) ** 0.5)
p.width, p.height = p.width // vae_scale_factor * vae_scale_factor, p.height // vae_scale_factor * vae_scale_factor
task_args['max_area'] = max_area
task_args['width'], task_args['height'] = p.width, p.height
elif model.__class__.__name__ == 'OmniGenPipeline' or model.__class__.__name__ == 'OmniGen2Pipeline':
elif model_cls == 'OmniGenPipeline' or model_cls == 'OmniGen2Pipeline':
p.width, p.height = vae_scale_factor * math.ceil(p.init_images[0].width / vae_scale_factor), vae_scale_factor * math.ceil(p.init_images[0].height / vae_scale_factor)
task_args = {
'width': p.width,
@@ -78,7 +79,7 @@ def task_specific_kwargs(p, model):
}
elif (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.INPAINTING or is_img2img_model) and len(getattr(p, 'init_images', [])) > 0:
if shared.sd_model_type == 'sdxl' and hasattr(model, 'register_to_config'):
if model.__class__.__name__ in [sd_models.i2i_pipes]:
if model_cls in [sd_models.i2i_pipes]:
pass
else:
model.register_to_config(requires_aesthetics_score = False)
@@ -101,9 +102,9 @@ def task_specific_kwargs(p, model):
}
# model specific args
if model.__class__.__name__ == 'QwenImageEditPipeline' and len(getattr(p, 'init_images', [])) == 0:
if model_cls == 'QwenImageEditPipeline' and len(getattr(p, 'init_images', [])) == 0:
task_args['image'] = [Image.new('RGB', (p.width, p.height), (0, 0, 0))] # monkey-patch so qwen-image-edit pipeline does not error-out on t2i
if model.__class__.__name__ == 'LatentConsistencyModelPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
if model_cls == 'LatentConsistencyModelPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
p.ops.append('lcm')
init_latents = [processing_vae.vae_encode(image, model=shared.sd_model, vae_type=p.vae_type).squeeze(dim=0) for image in p.init_images]
init_latent = torch.stack(init_latents, dim=0).to(shared.device)
@@ -114,7 +115,7 @@ def task_specific_kwargs(p, model):
'width': p.width if hasattr(p, 'width') else None,
'height': p.height if hasattr(p, 'height') else None,
}
if model.__class__.__name__ == 'BlipDiffusionPipeline':
if model_cls == 'BlipDiffusionPipeline':
if len(getattr(p, 'init_images', [])) == 0:
shared.log.error('BLiP diffusion requires init image')
return task_args
@@ -124,6 +125,9 @@ def task_specific_kwargs(p, model):
'target_subject_category': getattr(p, 'prompt', '').split()[-1],
'output_type': 'pil',
}
if model.__class__.__name__ == 'WanImageToVideoPipeline' and hasattr(p, 'init_images') and len(p.init_images) > 0:
task_args['image'] = p.init_images[0]
if debug_enabled:
debug_log(f'Process task specific args: {task_args}')
return task_args