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qwen-image with hires

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-08-24 09:19:51 -04:00
parent ad1cfcf840
commit 49fe21e253
3 changed files with 16 additions and 3 deletions
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CHANGELOG.md
+2 -1
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@@ -1,6 +1,6 @@
# Change Log for SD.Next
## Update for 2025-08-23
## Update for 2025-08-24
- **Models**
- **Chroma** final versions: [Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD), [Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base) and [Chroma1-Flash](https://huggingface.co/lodestones/Chroma1-Flash)
@@ -17,6 +17,7 @@
- improve handling of pre-quantized flux models
- fix networks reference models display on windows
- wan use correct pipeline for i2v models
- qwen-image with hires
## Update for 2025-08-20
modules/processing_args.py
+9 -2
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@@ -382,8 +382,15 @@ def set_pipeline_args(p, model, prompts:list, negative_prompts:list, prompts_2:t
if 'width' in possible and 'height' in possible:
vae_scale_factor = sd_vae.get_vae_scale_factor(model)
if isinstance(args['image'], torch.Tensor) or isinstance(args['image'], np.ndarray):
args['width'] = vae_scale_factor * args['image'].shape[-1]
args['height'] = vae_scale_factor * args['image'].shape[-2]
if args['image'].shape[-1] == 3: # nhwc
args['width'] = args['image'].shape[-2]
args['height'] = args['image'].shape[-3]
elif args['image'].shape[-3] == 3: # nchw
args['width'] = args['image'].shape[-1]
args['height'] = args['image'].shape[-2]
else: # assume latent
args['width'] = vae_scale_factor * args['image'].shape[-1]
args['height'] = vae_scale_factor * args['image'].shape[-2]
elif isinstance(args['image'], Image.Image):
args['width'] = args['image'].width
args['height'] = args['image'].height
modules/processing_diffusers.py
+5
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@@ -223,6 +223,11 @@ def process_hires(p: processing.StableDiffusionProcessing, output):
shared.state.update('Upscale', 0, 1)
output.images = resize_hires(p, latents=output.images)
sd_hijack_hypertile.hypertile_set(p, hr=True)
elif torch.is_tensor(output.images) and output.images.shape[-1] == 3: # nhwc
if output.images.dim() == 3:
output.images = TF.to_pil_image(output.images.permute(2,0,1))
elif output.images.dim() == 4:
output.images = [TF.to_pil_image(output.images[i].permute(2,0,1)) for i in range(output.images.shape[0])]
strength = p.hr_denoising_strength if p.hr_denoising_strength > 0 else p.denoising_strength
if (p.hr_upscaler.lower().startswith('latent') or p.hr_force) and strength > 0: