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https://github.com/vladmandic/automatic
synced 2026-09-02 02:50:47 +02:00
add sdxl support
This commit is contained in:
Vladimir Mandic
+47
-25
@@ -223,7 +223,8 @@ class StableDiffusionProcessing:
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source_image = devices.cond_cast_float(source_image)
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# HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
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# identify itself with a field common to all models. The conditioning_key is also hybrid.
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if backend == Backend.DIFFUSERS: # TODO: Diffusers img2img_image_conditioning
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if backend == Backend.DIFFUSERS:
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log.warning('Diffusers not implemented: img2img_image_conditioning')
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return None
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if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
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return self.depth2img_image_conditioning(source_image)
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@@ -682,11 +683,6 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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x_samples_ddim = torch.stack(x_samples_ddim).float()
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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del samples_ddim
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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devices.torch_gc()
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if p.scripts is not None:
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p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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elif backend == Backend.DIFFUSERS:
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generator_device = 'cpu' if shared.opts.diffusers_generator_device == "cpu" else shared.device
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@@ -695,7 +691,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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sampler = sd_samplers.all_samplers_map.get(p.sampler_name, None)
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if sampler is None:
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sampler = sd_samplers.all_samplers_map.get("UniPC")
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shared.sd_model.scheduler = sd_samplers.create_sampler(sampler.name, shared.sd_model) # TODO(Patrick): For wrapped pipelines this is currently a no-op
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# shared.sd_model.scheduler = sd_samplers.create_sampler(sampler.name, shared.sd_model) # TODO(Patrick): For wrapped pipelines this is currently a no-op
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cross_attention_kwargs={}
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if lora_state['active']:
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@@ -708,23 +704,48 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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elif sd_models.get_diffusers_task(shared.sd_model) == sd_models.DiffusersTaskType.INPAINTING:
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# TODO(PVP): change out to latents once possible with `diffusers`
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task_specific_kwargs = {"image": p.init_images[0], "mask_image": p.image_mask, "strength": p.denoising_strength}
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output = shared.sd_model(
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output = shared.sd_model( # pylint: disable=not-callable
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prompt=prompts,
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negative_prompt=negative_prompts,
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num_inference_steps=p.steps,
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guidance_scale=p.cfg_scale,
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generator=generator,
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output_type="np",
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output_type='np' if shared.sd_refiner is None else 'latent',
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cross_attention_kwargs=cross_attention_kwargs,
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**task_specific_kwargs
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)
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if shared.sd_refiner is not None:
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init_image = output.images[0]
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output = shared.sd_refiner( # pylint: disable=not-callable
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prompt=prompts,
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negative_prompt=negative_prompts,
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num_inference_steps=p.steps,
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guidance_scale=p.cfg_scale,
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generator=generator,
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output_type='np',
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cross_attention_kwargs=cross_attention_kwargs,
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image=init_image
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)
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x_samples_ddim = output.images
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if p.enable_hr:
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log.warning('Diffusers not implemented: hires fix')
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if lora_state['active']:
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unload_diffusers_lora()
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else:
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raise ValueError(f"Unknown backend {backend}")
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.send_everything_to_cpu()
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devices.torch_gc()
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if p.scripts is not None:
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p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
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for i, x_sample in enumerate(x_samples_ddim):
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p.batch_index = i
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if backend == Backend.ORIGINAL:
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@@ -898,7 +919,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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if self.hr_upscaler is not None:
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self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): # TODO this is majority of processing time
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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def save_intermediate(image, index):
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"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
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if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
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return
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if not isinstance(image, Image.Image):
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image = sd_samplers.sample_to_image(image, index, approximation=0)
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orig1 = self.extra_generation_params
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orig2 = self.restore_faces
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self.extra_generation_params = {}
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self.restore_faces = False
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info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
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self.extra_generation_params = orig1
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self.restore_faces = orig2
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images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
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if backend == Backend.DIFFUSERS:
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sd_models.set_diffuser_pipe(self.sd_model, sd_models.DiffusersTaskType.TEXT_2_IMAGE)
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@@ -916,21 +953,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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target_width = self.hr_upscale_to_x
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target_height = self.hr_upscale_to_y
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def save_intermediate(image, index):
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"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
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if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
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return
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if not isinstance(image, Image.Image):
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image = sd_samplers.sample_to_image(image, index, approximation=0)
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orig1 = self.extra_generation_params
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orig2 = self.restore_faces
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self.extra_generation_params = {}
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self.restore_faces = False
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info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
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self.extra_generation_params = orig1
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self.restore_faces = orig2
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images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
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if latent_scale_mode is not None:
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for i in range(samples.shape[0]):
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save_intermediate(samples, i)
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